A formalism for the logical description of computational agents and multi-agent systems is given. It is explained how it such a formal description can be used to configure and reason about multi-agent systems realizing computational intelligence models. A usage within a real software system Bang 3 is demonstrated. The logical description of multiagent systems opens Bang 3 for interaction with ontology based distributed knowledge systems like the Semantic Web or the Knowledge Grid.

In this article, the author generalizes Dempster`s rule, Yager`s rule, and Dubois-Prade`s rule for belief functions combination in order to be applicable to hyper-power sets according to the Dezert-Smarandache (DSm) Theory. A comparison of the rules with the DSm rule of combination is further presented.

This contribution deals with a belief processing which enables managing of multiple and overlapping elements of a frame of discernment. An outline of the Dempster-Shafer theory for such cases is presented, including several types of constraints for simplification of its large computational complexity. DSmT - a new theory rapidly developing the last five years - is briefly introduced. Finally, it is shown that the DSmT is a special case of the general Dempster-Shafer approach.

Definitions of two different ways of coarsening of basic belief assignments to opinions the simple coarsening and the normal coarsening are recalled in this contribution. A relation of results of combination of the normal opinions using the consensus operator to belief functions on an original n-element frame of discernment is examined. A questionable meaning of the normal coarsening is discussed.

In this study we engage in belief processing which enables managing of multiple and overlapping elements of a frame of discernment. Our focus is directed on DSm approach, which was originally introduced as a generalization of the Dempster-Shafer theory. Paradoxically, later it was presented also as a special case of the Dempster-Shafer approach applied on such frame of discernment. In this paper we discuss what is new in DSm approach, what are the benefits of DSm approach, and what is its real contribution to belief function theory.

Principal ideas of the minC combination are recalled. A mathematical structure of generalized frames of discernment is formalized. A generalized schema for a computation of the minC combination is presented. Conflicting belief masses redistribution among non-conflicting focal elements is overviewed. Final general formulas for computation of the minC combination are presented. Some examples of computation of the minC combination follow. Related works and approaches are referred in the end.

V tomto příspěvku se zbýváme extrakcí informací z webových zdrojů převážně textového charakteru. K tomuto účelu jsme se pokusili využít několik lingvistických nástrojů pro zpracování přirozeného textu v češtině. Jmenovitě se jedná o nástroje pražského projektu PDT a český WordNet. Cílem příspěvku je přiblížit možnosti, které tyto nástroje pro extrakci informací z textu poskytují. Extrakcí informací se zde zabýváme především v kontextu sémantického webu a zkoumáme možnosti, jak tyto nástroje využít pro automatizaci sémantické anotace stránek současného webu.

Uncertainty querying of large data can be solved by providing top-k answers according to a user fuzzy ranking/scoring function. Usually different users have different fuzzy scoring function a user preference model. Main goal of this paper is to assign a user a preference model automatically. To achieve this we decompose user’s fuzzy ranking function to ordering of particular attributes and to a combination function. To solve the problem of automatic assignment of user model we design two algorithms, one for learning user preference on particular attribute and second for learning the combination function. Methods were integrated into a Fagin-like top-k querying system with some new heuristics and tested.

We discuss models of user and group preferences in social networks and the Semantic web. We construct a model for user and group preference querying over RDF data as well as for ordering of answers by aggregation of particular attribute ranking. We have implemented our methods and heuristics into the Tokaf middleware framework prototype. We describe also experiments with Tokaf.

We discuss models of user preferences in Web environment. We construct a model for user preference querying over a number of data sources and ordering of answers by a combination of particular attribute rankings. We generalize Fagin's algorithm in two directions - we develop some new heuristics for top-k search in the model without random access and propose a method of ordering lists of objects by user fuzzy function. To enable different user preferences our system does not require objects to be sorted - instead we use a B+- tree on each of the attribute domains. This leads to a more realistic model of Web services. We implement our methods and heuristics for search of top-k answers into Tokaf middleware framework prototype. We describe experiments with Tokaf and compare different performance measures with some other methods.

Web search heuristics based on Fagin’s threshold algorithm assume we have the user profile in the form of particular attribute ordering and a fuzzy aggregation function representing the user combining function. Having these, there are sufficient algorithms for searching top-k answers. Finding particular attribute ordering and aggregation for a user still remains a problem. In this short paper our main contribution is a proof of concept of a new iterative process of acquisition of user preferences and attribute ordering .

During the implementation of an environmental accounting system in a company, one of the most important pieces of information to obtain is a detailed understanding about material flows (raw materials, semi-finished products, final products and wastes) and flows of different types of energy inputs (buying, selling and wastage) when thinking about the consequences on the company. The authors, in the article: 1) study the question of the quantification of the flows, and the accuracy of their measurement, 2) provide an environmental accounting statement, with help of standards and indices, statistical trends analysis.

A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a Hopfield-like neural network could be used for a natural procedure for Boolean factor analysis. To ensure efficient Boolean factor analysis, we propose our original modification not only of Hopfield network architecture but also its dynamics as well. In this paper, we describe neural network implementation of the Boolean factor analysis method. We show the advantages of our Hopfield-like network modification step by step on artificially generated data. At the end, we show the efficiency of the method on artificial data containing a known list of factors. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data.

We propose a model of a middleware system enabling personalized web search for users with different preferences. We integrate both inductive and deductive tasks to find user preferences and consequently best objects. The model is based on modeling preferences by fuzzy sets and fuzzy logic. We present the model-theoretic semantic for fuzzy description logic f-EL which is the motivation of creating a model for fuzzy RDF. Our model was experimentally implemented and integration was tested.

A version of fuzzy description logic based on the basic (continuous t-norm based) fuzzy predicate logic BL is presented. Problems of satisfiability, validity and subsumption of concepts are discussed and reduced to problems of fuzzy propositional logic known to be decidable for any continuous t-norm. For Lukasiewicz t-norm some stronger results are obtained.

Continuous t-norm based fuzzy predicate logic is surveyed as a generalization of classical predicate logic; then a kind of fuzzy description logic based on our fuzzy predicate logic is briefly described as a powerful but still decidable formal system of description logic dealing with vague (imprecise) concepts.

Příspěvek se zabývá problematikou získávání logických tvrzení z dat, tedy těmi metodami dobývání znalostí z dat (data mining), jejichž výsledky lze vyjádřit v jazyce nějaké formální logiky. Je podán velmi stručný přehled širokého spektra rozmanitých metod tohoto typu, jak metod vycházejících ze statistických přístupů, tak i metod spočívajících na principech strojového učení, a je poukázáno na specifický charakter metod založených na umělých neuronových sítích. Pro ilustraci jsou podrobněji popsány dvě konkrétní metody získávání logických tvrzení z dat. Jednou z nich je metoda Guha, která vychází z observační logiky a je pravděpodobně nejstarší metodou získávání pravidel z dat vůbec. Druhou je metoda založená na po částech lineárních vícevrstvých perceptronech.

The chapter provides a survey of some clustering methods relevant to the clustering document collections and, in consequence, Web data. We start with classical methods of cluster analysis which seem to be relevant in approaching to cluster Web data. The graph clustering is also described since its methods contribute significantly to clustering Web data. A use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.

Most of information retrieval systems depend on Boolean queries. The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we deal with using of Genetic algorithms in Information retrieval specially in optimizing of a Boolean query.

In this paper, we compare performance of novel neural network based algorithm for Boolean factor analysis with several dimension reduction techniques as a tool for feature extraction. Compared are namely singular value decomposition, semi-discrete decomposition and non-negative matrix factorization algorithms, including some cluster analysis methods as well. Even if the mainly mentioned methods are linear, it is interesting to compare them with neural network based Boolean factor analysis, because they are well elaborated. Second reason for this is to show basic differences between Boolean and linear case. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data.

After it has been successfully done in probability and possibility theories, the paper is the first attempt to introduce the operator of composition also for belief functions. We prove that the proposed definition preserves all the necessary properties of the operator enabling us to define compositional models as an efficient tool for multidimensional models representation.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid. We demonstrated that our approach gives good results on the parallel implementation of a selected generalized genetic algorithm. A model was constructed and its predictions compared with the reality on various computer architectures, including one parallel cluster machine. We also designed and implemented an open multi-agent system suitable for the above mentioned experiments and many others. This system synthesizes the areas of high performance computing, multi-agent systems and computational intelligence into an efficient and flexible means of running experiments.

This paper introduces a novel method for semantic annotation of web pages. Semantic annotation is performed with regard to unwritten and empirically proven agreement between users and web designers using Web patterns. This method is based on extraction of patterns which are characteristic for concrete domain. Patterns provide formalization of the agreement and allows assignment of semantics to parts of web pages. Experimental results verify the e®ectives of the proposed method.

We discuss two approaches to supervised learning, namely regularization networks and RBF networks, and demonstrate their performance on experiments. We claim that the performance of these two models is comparable, so the RBF networks can be used as a cheaper alternative to regularization networks.

Kernel based learning methods are subject of great interest at present. We discuss two kernel based learning methods, namely the Regularization Networks (RN) and the Radial Basis Function Network (RBF networks). The RNs are derived from the regularization theory, had been studied thoroughly from a function approximation point of view, and therefore have very good theoretical background. The RBF networks represent a model of artificial neural networks with both neuro-physiological and mathematical motivation. In addition they may be treated as a generalised form of Regularization Networks, i.e. RN with increased number of kernel functions. We demonstrated the performance of both approaches on experiments, including both benchmark and real-life learning tasks. We claim that the performance of RN and RBF network is comparable in terms of generalisation error. The RN approach usually leads to solutions with higher model complexity (high number of base units). In this situations, the RBF networks can be used as a ’cheaper’ alternative.

In this paper we study learning with Regularization Networks (RN). RN are feedforward neural networks with one hidden layer. Since they have a very good theoretical background, we study their practical aspects and applicability. On experiments we demonstrate the role of the regularization parameter, compare RN with different kernels and parameter settings on benchmark data sets. Then we apply RN to a problem of a flow rate prediction, real data from Czech river Sázava are used. All experiments were made using the system Bang.

The problem of supervised learning is a subject of great interest at present. It covers wide range of tasks, such as various classification, prediction, or forecasting problems, i.e. problems that also often arise in semantic web applications. We study one approach to this problem - regularization networks. We introduce composite types of kernel functions, sum kernels and product kernels. On experiments we demonstrate the role of the regularization parameter and kernel function in the regularization network learning, and compare networks with different types of kernel functions.

Learning from data with generalization capability is studied in the framework of minimization of regularized empirical error functionals over nested families of hypothesis sets with increasing model complexity. ForTikhonov`s regularization with kernel stabilizers, minimization over restricted hypothesis sets containing for a fixed integer n only linear combinations of all n-tuples of kernel functions is investigated. Upper bounds are derived on the rate of convergence of suboptimal solutions from such sets to the optimal solution achievable without restrictions on model complexity.The bounds are of the form 1/sqrt(n) multiplied by a term that depends on the size of the sample of empirical data, the vector of output data, the Gram matrix of the kernel with respect to the input data, and the regularization parameter.

It is shown that learning from data modelled as minimaization of error functionals can be reformulated in terms of inverse problems. This reformulation allows to characterize optimal input-output functions of networks with kernel units.

Complexity of data with respect to a particular class of neural networks is studied. Data complexity is measured by the magnitude of a certain norm of either the regression function induced by a probability measure describing the data or a function interpolating a sample ofinput/output pairs of training data chosen with respect to this probability. The norm is tailored to a type of computational units in the network class. It is shown that for data for which this norm is small, convergence of infima of error functionals over networks with increasing number of hidden units to the global minima is relatively fast. Thus for such data, networks with a reasonable model complexity can achieve good performance during learning. For perceptron networks, the relationship between data complexity, data dimensionality and smoothness is investigated.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Abstract Logic is the foundational discipline of many sciences. Part mathematics, part philosophy and part computing science, logic remains a core intellectual study and is increasingly relevant to practical concerns. It spreads into planning, into program synthesis, into circuit design and into discourse analysis. It underpins the entire science of artiŻcial intelligence. In order to increase knowledge from the field of logic, I participated in the Logic Summer School. This report covers some information.

World Wide Web obsahuje data, která jsou pro počítačové programy nesrozumitelná. Následkem toho je na něm obtížné některé věci zautomatizovat. Nedostatky současného webu by měl odstranit sémantický web, ve kterém data budou mít přesně popsaný význam. Zlepšení může přinést také v oblasti integrace, která je v případě dat pocházejících z webu velmi obtížná. Tento článek se zabývá integrací webových dat. Zaměřuje se na relační data ve formátu XML a navrhuje postupy základních integračních operací.

Knowledge representation in geographic information systems (GIS) and associated data processing presents many challenges for researchers. To use ontologies as knowledge representation belongs to the most topical problems to solve. This involves ontology development as well as ontology re-usage. The goal of the research described in this paper is to develop a specific ontology for a given GIS area.

Ontologies play an important role in knowledge representation. Among various fields, where ontologies can be useful, is the GIS data area. We consider data in a specific GIS domain and develop a new ontology. The result is described in this paper.

A crucial point in automated data processing is the way in which the data are expressed. One possibility is to employ existing features of the Semantic Web - ontologies. Ontologies play an important role in a knowledge representation. The aim of the research presented in this paper is to provide more automated VirGIS system. VirGIS is an integration system that works with GIS (Geographical Information Systems) data. As a first step of our research, we describe its data using common Semantic Web techniques and build a VirGIS ontology.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Ontologies play an important role in a knowledge representation. It involves ontology development as well as ontology re-use. Among various fields, where ontologies can be useful, is the GIS (Geographical Information System) data area. The goal of the research described in this paper is to develop a specific ontology for a given GIS domain. At first, we describe a general methodology and main tools for ontology development. Then a new ontology that covers data used in a VirGIS integration system is presented. The paper describes the VirGIS specified ontology as well as a list of spatio-temporal data ontologies that are available and possible to use for a general data features description.

Integration has been an acknowledged problem for a long time. With the aim at combining data from different sources, data integration usually provides a unified global view over these data. A crucial part of the task is the establishment of the connection between the global view and the local sources. Two basic approaches have been proposed for this purpose: Global As View (GAV) and Local As View (LAV).With the Semantic Web and its data description means, there is also another possibility - to employ ontologies for the relationship description in an integration system.

Information Retrieval (IR) as a process of searching relevant information is a significant discipline of a data processing field. European Summer School in Information Retrieval ESSIR provides students, academic and industrial researchers and developers a grounding in the core objects of IR (models, architectures, algorithms), as well as covering some current topics, e.g. information retrieval from the Web. We have participated its 5th year that was held at Dublin City University in Dublin, Ireland.

Datová integrace je uznávaný problém v oblasti zpracování dat. Jejím cílem je obvykle poskytnout ucelený pohled na několik datových zdrojů. V případě nematerializovaného řešení je klíčové stanovení vazeb mezi poskytovaným virtuálním pohledem a daty uloženými ve zdrojích. článek se zabývá řešením stanovení těchto vazeb. Svůj přístup zakládá na ontologiích.

A key point in modern automated data processing is metadata semantics representation. Employing Semantic Web existing features - ontologies - is a promising option. Ontologies open a novel approach to knowledge representation. The paper presents a GIS (Geographic Information System) domain application illustrating ontological approach to data integration and data processing automation in the specific system. This VirGIS system is an integration system that works with spatio-temporal data. We start our study with developing the data representation based on common Semantic Web techniques and build a VirGIS ontology.

Data integration usually provides a unified global view over several data sources. A crucial part of the task is the establishment of the connection between the global view and the local sources. For this purpose, two basic mapping approaches have been proposed: GAV (Global As View) and LAV (Local As View). On the Semantic Web, there can be considered also an ontological approach. In this paper, data integration is solved using ontologies of the sources. To express relationships between the global view and local source schemas, an ontology for the integration system is built. Thus, a schema integration task is transformed to an ontology merging task.

V tomto příspěvku je popsán přístup k virtuální integraci dat využívající současných principů, metod a nástrojů sémantického webu. Přístup pracuje s daty ve formátu RDF a předpokládá dostupnost ontologií, které je popisují. Ontologie jsou základem pro všechny kroky prezentovaného integračního procesu. Jsou využity jak k určení vztahů mezi daty a poskytovaným integrovaným pohledem, tak i k zápisu nalezených korespondencí. Ty jsou dále použity při zpracování dotazů kladených na integrovaná data.

The paper deals with one step of non-materialized data integration - schema matching task. It works with data sources on the Semantic Web; the crucial assumption for the considered task is available ontologies describing data to integrate. Source ontologies are used to find correspondences between source schemas elements. For this, also techniques known from ontology alignment and ontology merging field are used.

Článek se zabývá úlohami, které je třeba řešit při nematerializované integraci dat. Zaměřuje se na hledání korespondencí mezi schématy a mapování schémat. Návrh přístupu řešení těchto úloh na Sémantickém webu těží z dostupných ontologiích popisujících integrované zdroje. Ontologie jsou využity jak k hledání mapování, tak i při jejich popisu.

Článek se zabývá přístupem, jak se pokusit zautomatizovat mnohdy netriviální úlohu nalezení pravidel pro integraci dat. Předkládaný přístup automaticky generuje kandidáty pravidel včetně jejich ohodnocení pomocí nepřímé míry definující jejich prioritu. Priorita může následně být použita buďto návrhářem (člověkem) jako pomocný prvek pro přípravu návrhu, nebo při automatickém návrhu integračního procesu zahrnující pravidla s maximální prioritou. Studie v příspěvku se detailně věnuje dvěma základním typům pravidel, ekvivalenci a hierarchii, přičemž ohodnocení kandidátů je založeno na (strukturální) analýze aktivních domén atributů. V neposlední řadě příspěvek ukazuje možnost decentralizovaného přístupu k integraci dat, jenž je inspirován webovými technologiemi.

The paper deals with expressing preferences in the framework of the relational data model. Preferences have usually a form of a partial ordering. Therefore the question arises how to provide the relational data model with such an ordering.

This paper describes an option to express our preferences in the framework of relational databases. Preferences have usually a form of a partial ordering. Therefore the question is how to deliver the semantics of ordering to a database system. The answer is quite straightforward.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. To allow nonmonotonic operations, the preferences are defined between sets of relational instances. The aim is the generalization of the relational algebra that is as minimal as possible, in the sense that the formal fundamentlas of the relational data model are preserved. At the same time, the extended model should be formal enough to provide a sound basis for the investigation of other new preference constructors and operations and for new possible applications.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. The preferences are defined between sets of relational instances, which is a nontrivial generalization of the approach aiming at incorporating ordered attribute domains into the relational data model. The main goals are as follows: an effective representation of information representable by a partial order, an intuitive preference construction and its processing throughout the query execution plan, and a suitable data structure to support it all.

The notion of preference poses a new prospect of personalization of database queries. In addition, it can be exploited to optimize query execution. Indeed, a novel optimization technique involving preference is developed, and its algorithm presented.

The paper resumes recent advances in the field of logic of preference and presents their application in the field of database queries. Namely, non-monotonic reasoning mechanisms including various kinds of preferences are reviewed, and a way of suiting them to practical database applications is shown: reasoning including sixteen strict and non-strict kinds of preferences, inclusive of ceteris paribus preferences, is feasible. However, to make the mechanisms useful for practical applications, the assumption of preference specification consistency has to be relinquished. This is achieved in two steps: firstly, all the kinds of preferences are de- fined so that some uncertainty is inherent, and secondly, not a notion of a total pre-order but a partial pre-order is used in the semantics, which enables to indicate some kind of conflict among preferences. Most importantly, the semantics of a set of preferences is related to that of a disjunctive logic program.

The paper resumes a logical framework for formulating preferences and proposes their embedding into relational algebra through a single preference operator parameterized by a set of user preferences of sixteen various kinds, inclusive of ceteris paribus preferences, and returning only the most preferred subsets of its argument relation. Most importantly, conflicting set of preferences is permitted and preferences between sets of elements can be expressed. Formal foundation for algebraic optimization, applying heuristics like push preference, also is provided: abstract properties of the preference operator and a variety of algebraic laws describing its interaction with other relational algebra operators are presented.

The paper gives an overview of recent advances in the field of logic of preference and discusses their applicability in the frame of the relational data model. Namely, non-monotonic reasoning mechanisms with various kinds of preferences are reviewed in detail, and a way of suiting them to practical database applications is presented. These mechanisms enable to reason simultaneously about sixteen strict and non-strict kinds of preferences, including ceteris paribus preferences. To make the mechanisms useful for practical applications, the assumption of preference specification consistency has to be loosened. This is achieved in two steps: firstly, all the preference specifications are generalized to permit uncertainty, and secondly, not a total pre-order on worlds but a partial pre-order on worlds is used in the semantics, which enables to indicate some kind of conflict among worlds by their incomparability. Most importantly, the semantics of set of preferences is related to that of a disjunctive logic program.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid.

A multi-agent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence of new models.

In this paper we present an architecture for decision making of software agents that allows the agent to behave autonomously. Our target area is computational agents — encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and belief-desire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture.

During the implementation of an environmental accounting system in a company, one of the most important pieces of information to obtain is a detailed understanding about material flows (raw materials, semi-finished products, final products and wastes) and flows of different types of energy inputs (buying, selling and wastage) when thinking about the consequences on the company. The authors, in the article: 1) study the question of the quantification of the flows, and the accuracy of their measurement, 2) provide an environmental accounting statement, with help of standards and indices, statistical trends analysis.

We study and design multi-agent systems for computational intelligence modeling. Agents typically reside in a high-performance parallel environment, such as a cluster of computational nodes, and utilize a non-blocking asynchronous communication. The need of accurate predictions of run-time and other characterizations of complex parallel asynchronous processes bring us to design a new parallel model creation methodology. In this article our approach is briefly described and a test case is shown and discussed.

In this paper, a multi agent platform targeted toward the area of computational intelligence modeling is presented. We show the design of various computational agents creating multi agent systems, as well as the infrastructure capabilities. The focus of the system is the cooperation of agents, their interchangeability autonomous behavior, and emergence of new models. Two main areas of cooperation are presented: automated creation of a multi-agent system satisfying given constraints, and decision support for agent partner selection. It is demonstrated that such a system is able to assist in building hybrid artificial intelligence models based on data in a distributed environment.

In this paper we present an approach where a hybrid computational model is represented as a set of communicating agents composing a multi-agent system. A general concept of representation of connected groups of agents is introduced and utilized for automatic building of schemes to solve a given computational task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to generate and verify new schemes. Furthermore, the adaptive cooperation support of individual computational agents is described, which improves their efficiency in time. These features are implemented within a software system and demonstrated on several examples.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

A formalism for the logical description of computational agents and multi-agent systems is given. It is explained how it such a formal description can be used to configure and reason about multi-agent systems realizing computational intelligence models. A usage within a real software system Bang 3 is demonstrated. A way to extend the system toward dynamic environments with migrating agents is discussed.

This work deals with a problem of automatic composition of multi-agent system satisfying given constraints. A general concept of representation of connected groups of agents (schemes) within a multi-agent system is introduced and utilized for automatic building of schemes to solve a given computational intelligence task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to propose and verify new schemes. The approach is illustrated on simple examples.

We design a formal model of an amorphous computer suitable for theoretical investigation of its computational properties. The model consists of a finite set of nodes created by RAMs with restricted memory, which are dispersed uniformly in a given area. Within a limited radius the nodes can communicate with their neighbors via a single-channel radio. The assumptions on low-level communication abilities are among the weakest possible: the nodes work asynchronously, there is no broadcasting collision detection mechanism and no network addresses. For the underlying network we design a randomized communication protocol and analyze its efficiency. The subsequent experiments and combinatorial analysis of random networks show that the expectations under which our protocol was designed are met by the vast majority of the instances of our amorphous computer model.

We design a formal model of an amorphous computer suit- able for theoretical investigation of its computational properties. The model consists of a ¯nite set of nodes created by RAMs with restricted memory, which are dispersed uniformly in a given area. Within a limited radius the nodes can communicate with their neighbors via a single- channel radio. The assumptions on low-level communication abilities are among the weakest possible: the nodes work asynchronously, there is no broadcasting collision detection mechanism and no network addresses. For the underlying network we design a randomized communication pro- tocol and analyze its e±ciency. The subsequent experiments and combi- natorial analysis of random networks show that the expectations under which our protocol was designed are met by the vast majority of the instances of our amorphous computer model.

Amorphous computing differs from the classical ideas about computations almost in every aspect. The architecture of amorphous computers is random, since they consist of a plethora of identical computational units spread randomly over a given area. Within a limited radius the units can communicate wirelessly with their neighbors via a single-channel radio.We consider a model whose assumptions on the underlying computing and communication abilities are among the weakest possible: all computational units are finite state probabilistic automata working asynchronously, there is no broadcasting collision detection mechanism and no network addresses. We show that under reasonable probabilistic assumptions such amorphous computing systems can possess universal computing power with a high probability. The underlying theory makes use of properties of random graphs and that of probabilistic analysis of algorithms. To the best of our knowledge this is the first result showing the universality of such computing systems.

Zatímco dílčí ontologie pokrývají jeden pohled na úzce vymezenou oblast, mnohé aplikace vyžadují obecnější přístup k popisovaným datům. Z tohoto důvodu se přistupuje ke srovnávání ontologií (Ontology Matching), které, pokud je to možné, transformuje několik různých ontologických popisů do jediného. Příspěvek popisuje případovou studii takového procesu za využití různých metod, srovnává jejich úspěšnost a diskutuje možnost využití dílčích výsledků k definici výsledné ontologie. Pro experiment byly nezávisle vytvořeny dvě triviální ontologie, které byly různými nástroji a metodami integrovány do jedné.

The paper describes a method for relational data model estimation from input web data and usage of this method. It includes also its principal limitations and shows the model usage for a more effective storage into a repository. The repository is implemented as the universal relation. The properties of the model are described as well.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

The paper proposes a data structure modelling method, which aim is to estimate a structure model from a given input data set. The model can be seen as an estimate of data semantics ֠the obtained relations can be transformed into an RDF or OWL semantic web format documents to be included into the semantic web portfolio. The proposed method makes a connection between current web sources and the semantic web vision to be realized. Finally, the method usage and conversion rules are illustrated on an example.

Metoda odhadování struktury dat spojuje vizi sémantického webu a dnešní webové datové zdroje, které převážně neobsahují žádnou doprovodnou sémantiku prezentovaných informací. Aby bylo možné tyto zdroje použít pokročilými nástroji sémantického webu, je potřeba sémantiku prezentovaných dat alespoň odhadnout. Příspěvek popisuje takovou metodu, ukazuje její použití pro úlohy induktivního logického programování a jmenuje výhody použití pravidlových systémů pro její implementaci.

Odhadování struktury dat je jednou z možností, jak automatizovaným způsobem interpretovat data. Ta mohou být popsána pomocí modelu funkčních závislostí, vytváření takového modelu lze srovnat s některými technikami strojového učení. Tento příspěvek shrnuje vybrané základní techniky induktivního logického programování a analyzuje je z pohledu metody odhadování struktury dat. Ukazuje se, že techniky induktivního logického programování lze v některých případech převést právě odhadování struktury dat.

Odhad struktury dat získaných například z webových zdrojů lze využít jednak pro uložení dat, tak pro netriviální dotazování nad těmito daty. Článek rozšiřuje metodu odhadu struktury dat získávající odpovídající schéma relačního modelu ze vstupních dat a popisuje metodu uložení dat pomocí jednoduchého asociativního úložiště dat právě na základě odhadnutého modelu. Článek diskutuje dvě možné implementace úložiště: první uchovávající data jako instance funkčních závislostí, druhou uchovávající pouze instance funkčních závislosti mezi jednoduchými atributy rozšířenou o podporu komplexních atributů pomocí metainformace.

Použití asociativního úložiště dat je jednou z možností, jak efektivním způsobem reprezentovat data. Článek se zabývá z převážné části metodou učení takového úložiště, přičemž využívá myšlenek vize sémantického webu. Dále ukazuje souvislosti této metody s teoriemi organizace paměti živých organismů včetně člověka a její učení bez snahy tyto procesy zpětně modelovat. Jelikož se nabízí možnost využít současných webových stránek jako vstupních dat, je učící algoritmus navržen inkrementálnì a výhody použití takového adaptivního přístupu jsou detailně popsány. Výsledkem algoritmu je asociativní úložiště navržené na základě všech dostupných (meta)informací, na které je možné pohlížet jako na extensionální úrovni odhadnutou sémantiku dat.

Mechanisms for accessing and training the data repository using a binary matrix formalism are presented. The repository is designed for a data storage through corresponding instances of simple attribute functional dependencies, which can be seen as similar to the binary predicate formalism being used by the RDF semantic web format. Two mechanisms for querying a repository, the generalisation and the specialisation, are given. Furthermore, the incremental repository training mechanism with no extra requirements on the input data form is described: The extensional functional dependency system is used as a generalised view on the stored data; the algorithm is inspired by the functional dependency discovery approach.

The paper compares features of learning and querying process in the situation, when values in the input data set are annotated by attributes or this information is not available. The attribute annotation enables to consider global relationships, which are useful to express the data semantics in a explicit way. It will be shown data can be accessed with no semantic interpretation and then, after the evaluation process, the result can be interpreted.

Machine learning methods can be seen as an optimalisation task reducing differences between an expected and returned result on a given data set. A corresponding knowledge base can be expressed in many ways, for example, by a binary predicate formalism. The talk deals with a minimalisation of predicate ammount in such a repository, which is enabled by a transitivity. The transitive reduction algorithm will be detaily given for an incremental (attribute annotated data driven) building of a knowledge base; a base model with higher expressiveness will be prefered. Finally, an effect of the selected model to estimated explicit semantic definitions of symbols (internal base interpretation) will be mentioned as well.

Vize sémantického webu byla představena před skoro již 10 lety, avšak žádná z její aplikací prozatím nedokázala oslovit takové množství lidí, jaké dnes používá web v současné podobě. Příspěvek se věnuje možnostem sémantického webu a přínosům, které může přinést pro koncové uživatele. Nejprve podává přehled o současných technologiích i jejich použití a následně diskutuje možnosti plynoucí z použití odkazů v prostředí sémantického webu tak, jak je známe z webu současného, tedy rozšiřující, zpřesňující či udávající kontext prezentované informace.

Přıspěvek se zabývá aspekty optimalizace paměťových nároků binárního úložiště atributově anotovaných dat na základě transitivní redukce zobecněného systému funkčních závislostí. Tento systém buď může být předem daný modelem, v tomto případě se ukazuje, že je možné optimalizaci použít jednorázově; a nebo tento model je inkremetálním způsobem odhadován a pak se ukazuje vhodným pouze již jednou naoptimalizované úložiště pouze upravovat opět inkrementálním způsobem. V poslední sekci se příspěvek zaobírá rozborem nejednoznačnosti výsledku včetně detailního rozboru vlastností základních konfigurací částí modelu způsobující tuto nejednoznačnost. V neposlední řadě je analyzována složitost dílčích operací v úložišti.

Cílem příspěvku je předložit vizi nových přístupů pro sdílení a vyhledávání dat na internetu. Opírá se o prověřené technologie pracující nad textovými webovými dokumenty a propojuje je se sémantickým webem, moderním prostředkem pro výměnu dat a aktuálními trendy ve vývoji internetu jako celku.

The vision of the semantic web as a distributed and trusted environment for data sharing together with related issues are presented. The paper brings a basic binary matrix formalism for the internal representation of sources and shows the clasical issues as a data inconsistency and a data integration. Aspects of these issues lead to the binary formalism to be generalised into the <0,1> interval one to enable the consideration of uncertainty at various level. Finally, the need of a source trust definition is presented and discussed with respect to a semantic web.

When dealing with image databases, we often need to solve the problem of how to retrieve a desired set of images effectively and efficiently. As a representation of images, there are commonly used some high-dimensional vectors of extracted features, since in such a way the content-based image retrieval is turned into a geometric-search problem. In this article we present a study of feature extraction from raw image data by means of the LSI method (singular-value decomposition, respectively). Simultaneously, we show how such a kind of feature extraction can be used for efficient and effective similarity retrieval using the M-tree index. Because of the application to image retrieval, we also show some interesting effects of LSI, which are not directly obvious in the area of text retrieval (where LSI came from).

We study behavioural patterns learned by a robotic agent by means of two different control and adaptive approaches - a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement Q-learning algorithm. In both cases, a set of rules controlling the agent is derived from the learned controllers, and these sets are compared. It is shown that both procedures lead to reasonable and compact, albeit rather different, rule sets.

It is well known that ontologies will become a key piece, as they allow making the semantics of Semantic Web content explicit. In spite of the big advantages that the Semantic Web promises, there are still several problems to solve. Those concerning ontologies include their availability, development and evolution. In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher index dimensions the indexing structures suffer from the "curse of dimensionality" and their efficiency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI is a numerical method, which discovers latent semantics in documents by creating concepts from existing terms. In this paper we present a basic method of mapping LSI concepts on given ontology (WordNet), used both for retrieval recall improvement and dimension reduction. We offer experimental results for this method on a subset of TREC collection, consisting of Los Angeles Times articles.

In the area of information retrieval, the dimension of document vectors plays an important role. We may need to find a few words or concepts, which characterize the document based on its contents, to overcome the problem of the "curse of dimensionality", which makes indexing of highdimensional data problematic. To do so, we earlier proposed a Wordnet and Wordnet+LSI (Latent Semantic Indexing) based model for dimension reduction. While LSI works on the whole collection, another procedure of feature extraction (and thus dimension reduction) exists, using binary factorization. The procedure is based on the search of attractors in Hopfield-like associative memory. Separation of true attractors (factors) and spurious ones is based on calculation of their Lyapunov function. Being applied to textual data the procedure conducted well and even more it showed sensitivity to the context in which the words were used. In this paper, we suggest that the binary factorization may benefit from the Wordnet filtration.

In this paper, the web pages concerning products sale are analyzed with the aim to create clusters of similar web pages and characterize these by GUI patterns. We applied GD-CLS (gradient descent - constrained least squares) method which combines some of the best features of other methods. Both traditional methods for searching clusters and nonnegative matrix factorization are used.

Advances in cellular mobile technology have engendered a new paradigm of computing, called mobile computing. New challenges have arisen and solutions are proposed based on various approaches. One of the most important challenges is security and now a day has been found ubiquitous in computing as whole. The paper is intended as a quick survey emphasizing security paradigm and also ad hoc networks are kept in mind and briefly discussed.

In the paper, we present a quality of service and energy aware communication protocol, called RollingBall. We do believe that QoS and energy awareness are two of the most important parameters in wireless sensor networks. The protocol is completely distributed with no centralized control. The key idea is to introduce a resistance calculation for every connection in the network. The resistance reflects the distance to the sink together with energy capabilities of particular sensor. While the resistance is continually re-calculated, packets are sent to the sink via an appropriate path. Such a scheme allows to spend minimum messages on network management, whereby sensor network lifetime is extended and throughput remains high.

The paper describes a possible treatment of sharing data in a large network of users. The mathematical model is based on weighted hypergraphs whose nodes and edges denote the users and their relations, respectively. Its flexibility guarantees to have basic relations between users robust under frequent changes in the network connections. Approach copes with the communication/computing issues from different point of view based on a structure evolution and its further optimization in sense of keeping the parallel space and time complexities low. Although the idea is aimed to the field of mobile computing, it can be generalized in straightforward way to other similar environment. An experimental application is also proposed and discussed in the paper.

The paper describes selected problems and possible solutions for the position management in mobile computing. A proposed scheme extends existing approaches. The main idea is to reduce amount of possible solutions given by a movement prediction algorithm by constrains ubiquitously found in the real-life. Existing solutions and possibilities for a future research are also described.

The thesis deals with security hazards in distributed environments where traditional centralized approaches are only of limited serviceability. One of the very successful model for treating security and access management in distributed systems are so called reputation systems. The main goal of the rep- utation systems is to provide entities in the environment with mechanisms for inferring and building trust consequently used for access control. If the trust between two entities is high enough, transactions are likely to be allowed. The thesis proposes a new security model with trust management system for dynamic and distributed environments with huge number of entities. In dynamic systems new entities or relationships are likely to emerge or existing entities or relationships may often disappear. Such dynamics pose severe problems even for traditional reputation systems. Therefore our approach differs from the traditional ones in the way adopted for establishment and management of trust between entities in our point of view trust is not assigned to particular relationships but the trust is common for a group of entities. In this way, our proposal significantly enhances ability to infer trust between entities with no previous personal experiences with each other or in environments with huge number of entities. For the proposal differs in understanding of trust, it uses a hypergraph model for representation of system of entities. The security model proposed in the thesis contains two algorithms for transformation of a general input graph structure into hypergraph model, an algorithm treating dynamics of the distributed environment and a security subsystem. Our experimental implementation SecGrid utilizes proposed algorithms and it is used for experimental verification of the security models. The experiments investigate ability of the transformation algorithms; in details the dynamic part of our proposal together with the security subsystem proposed specially for the hypergraph model. Experiments show that our model overcame the traditional graph model in many ways especially in dynamic environments with huge amount of entities.

The paper presents a new approach for treating security issues in various environments with special emphasis on Mobile databases, Semantic web and Grids. A brief overview on possible security models and a discussion on their advantages and disadvantages is given. Our model based on virtual organization and is build up on mathematical background based on hypergraphs. We show that hypergraphs are the way how to reduce space complexity of the model. The complexity is important with respect to target environments where number of users might be huge. To verify our model an experimental implementation was programed and some graphical outputs are mentioned.

This paper gives a short overview on security issues widely found in the Semantic Web environment. It goes through each level of the proposed Semantic Web layers and discusses security, privacy and trust for each. Then, a list of possible solutions is given. In particular XML security, RDF security, secure information integration and trust on the Semantic Web are mentioned and short discussion is given. Finally, an approach for treating security and trust based on Virtual organization is described and its advantages are provided.

The paper describes a new approach for treatment security issues in reconfigurable groups of users (Virtual Organizations-VO). The proposed strategy combines a convenient mathematical model, efficient combinatorial algorithms which are robust with respect to changes in the VO structure, and an efficient implementation. The mathematical model uses properties of weighted hypergraphs. Model flexibility enables description of basic security relations between the nodes such that these relations are preserved under frequent changes in connections of the hypergraph nodes. The proposed implementation makes use of the techniques developed for time and space-critical applications in numerical linear algebra. The ideas can be generalized to other concepts describable by weighted hypergraphs. The consistency of the proposed ideas for security management in the changing VO was verified in a couple of tests with our pilot implementation SECGRID.

The technical report presents a brief overview on web search engines with deeper insight into their linear algebra background. The linear algebra plays very important role in modern web search algorithms (e.g. Google). The report presents two algorithms, particularly HITS and PageRank. The algorithms are discussed on their convergence problems and also some improvements to their personalization abilities. The computation complexity is also mentioned and briefly sketched.

The paper describes a new approach for treating trust in reconfigurable groups of users with special accent on trust in the next generations of the Internet. The proposed model uses properties of weighted hypergraphs. Model flexibility enables description of relations between nodes such that these relations are preserved under frequent changes. The ideas can be straightforwardly generalized to other concepts describable by weighted hypergraphs. The consistency of the proposal was verified in a couple of experiments with our pilot implementation SecGRID.

The paper presents the BlueGame project an ad-hoc multiplayer mobile game based on the Dungeons&Dragons board game. The main idea lies in the adoption of Bluetooth Piconet configuration and direct face to face contact of players in real environments.

Despite the tremendous research activity in the field of searching engines for the Internet, current searching engines still face some severe limitations. The paper presents an idea of a distributed data source environment to be build on the current state of the art technologies available on the Internet. The paper combines recent advances in the fields of a data inconsistency, a data integration and reputations of sources for further refinements of data searching and sharing processes. The paper generalizes the data binary formalism narrowly connected with the ideas of the semantic web into the <0,1> interval to enable the consideration of uncertainty at various levels.

While partial ontologies cover view at one-track area, many applications require much more general approach to describe their data. On this account it approaches to ontology matching as a headstone of further operations, that can transform several ontological descriptions into one. This paper describe case study of such process with using different methods, confront their fruitfulness and discuss a possibility of using particular results to definition of final ontology. Two trivial ontologies were created (independently of any tool) and they were matched using various selected tools.

Internet je ohromným zdrojem provázaných, ale většinou neuspořádaných dat. Sémantický web, jako rozšíření webu současného, se snaží tuto neuspořádanost řešit a to nejen bezprostředně pro lidského uživatele, ale zejména z hlediska možnosti strojového zpracování informací. Cílem je doplnit data o metadata, která mají být srozumitelná jak pro člověka, tak pro počítač. Tato metadata jsou nejčastěji vyjádřena pomocí ontologií, které jsou jedním ze základních stavebních prvků sémantického webu. V příspěvku se snažím nastínit některé z možností integrace (slučování) ontologií za účelem sdílení informací.

In this paper we present an architecture for decision making of software agents that allows the agent to behave autonomously. Our target area is computational agents—encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and beliefdesire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture

In MANETs and similar link-free networks of communicating objects there is no central authority for naming and connection management. Autoconfiguration of network nodes is therefore desirable and, building on approaches in IPv6, a number of ‘zero configuration’ networking protocols have been proposed for this case. Typically these protocols do not easily scale and have difficulty with network partitioning and merging. In this paper we propose a number of novel, decentralized techniques for name resolution in zero configuration protocols that are more flexible and yet lead to name extensions of smallest possible length, assuming that objects sufficiently mix within their ranges. Finally, the techniques are converted into a fully decentralized, scalable autoconfiguration protocol for use in ad hoc networks with directional antennas. The protocol is ‘lazy’ in the sense that name resolution is postponed until the moment that conflicts prevent the correct functioning of the communication structure.

We present an overview of our results on model theoretic and fixpoint semantics for a relational algebra using a model of many valued Datalog with similarity. Using our previous results on equivalence of our model and certain variant of generalized annotated programs, we base our querying on fuzzy aggregation operators (also called annotation terms, combining functions, utility functions). Using of fuzzy aggregation operators (distinct from database aggregations) enables us to reduce tuning of various linguistic variables. In practice we can learn fuzzy aggregator operators by an ILP procedure for every user profile. Our approach enables also integration of data from different sources via aggregation and similarity. Extending domains we discuss difference between fuzzy elements and fuzzy subsets. We also discuss an alternative, when all extensional data are stored crisp and fuzziness is in rules interpreting data, context and in user query.

We present a design of cognitive system architecture with an internal world model. The internal world model is realized with the help of artificial mirror neurons. We consider generalized artificial mirror neurons acting both as a mechanism for assembling and learning multimodal sensorimotor information and as associative memory for invoking multimodal information given only some of its components. We show that within an artificial cognitive system a network of generalized mirror neurons can simultaneously serve as an internal world model recognized by the agent and as that of the agent`s position within this world. We also specify a self-organizing control mechanism, which is based on the basic operations over concepts that were essentially identified by the British 18th century philosopher David Hume. This control mechanism makes use of the internal world model constructed in agent`s interaction with real world and straightforwardly supports imitation learning. Building heavily on the properties of the generalized mirror net and on automatic abstract concept creation, we offer an algorithmic explanation of computational language acquisition, thinking and consciousness in our model. Rather than describing an implementation of the respective mechanisms, the aim of the paper is to establish a proof of the principle of algorithmic nature of higher cognitive functions.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretikù je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i "klasicky", jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů - a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

We introduce a computational model of a so-called globular universe which represents generalization of both classical cellular automata and contemporary models of self-assembly. Similarly as the latter mentioned model our model utilizes a multiset of globules which are endowed by self-organizing ability controlled by a finite state mechanism; these computational units are not fixed in a predetermined structure. The environment abounds in these units which are available at places where needed for a self-assembly of various objects. Within a globular universe we define the notion of self-reproducing evolutionary automaton. This notion refers to an automaton being at the beginning of a lineage of self-reproducing automata which leads to self-reproducing automata with arbitrary complex finite state control mechanisms via a series of mutations of intermediate automata. The ideas presented in this paper complement von Neumann`s results on self-reproducing automata in a static universe by offering a precise definition of what is meant by "evolutionary self-reproduction" and by designing a dynamic nondeterministic universe with a self-reproducing self-assembling evolutionary automaton.

We introduce so-called biomata which represent a novel approach to the construction of self-reproducing automata within the automata theory. The design of our automata has been motivated by the ideas of cellular biology on the origin of life. Unlike the von Neumann`s model our model replicates by fission and need not give much attention to the exact guiding of its own assemblage; rather, this process relies on self-assembly abilities of the respective parts produced by the biomaton from input objects not possessing such quality. The model represents an interesting fusion of computational and self-organizational processes.We believe that by capturing the basic aspects of the assumed origin of real life our modelling leads to a conceptually simpler and hence more plausible scenario of natural self-reproduction than the previous attempts did.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretiků je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i „klasicky“, jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů − a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

Existujú v súčasnej informatike výzvy zrovnateľné s veľkými problémami, s ktorými je konfrontované ľudstvo v oblasti fyziky, biológie, či iných vedných a inžinierskych oblastí? Má súčasná informatika výskumné zámery svojim významom zrovnateľné s odhalením podstaty gravitácie? S cestou na Mars? S rozlúštením genetického kódu? V prednáške sa zamyslíme, ktoré problémy v informatike majú podobný charakter, prečo je dobré sústrediť sa na ich riešenie a aké riešenia sa rysujú.

We present two original computational models - globular universe and autopoietic automata - capturing the basic aspects of an evolution: a construction of self-reproducing automata by self-assembly and a transfer of algorithmically modified genetic information over generations. Within this framework we show implementation of autopoietic automata in a globular universe. Further, we characterize the computational power of lineages of autopoietic automata via interactive Turing machines and show an unbounded complexity growth of a computational power of automata during the evolution. Finally, we define the problem of sustainable evolution and show its undecidability.

We look for computational limits of artificial, natural and hybrid cognitive and intelligent systems. The common basis for such studies is offered by computationalism, i.e., the belief that cognitive or intelligent processes, respectively, are in essence computational processes. We show that in principle cognitive systems might exist whose computational power outperforms that of Turing machines and that even in practice we observe the rudiments of such systems. These results point to the fact that the so - called Church - Turing Thesis, dealing with the central position of Turing machines in the world of computations and algorithms, must be seen in the context of physical principles exploited by the cognitive systems, and in that of the communication scenario between the system and its environment.

Popíšeme původní výpočetní modely – globulární vesmír a autopoietické automaty – které zachycují podstatné výpočetní aspekty evoluce: konstrukci sebereprodukčních evolučních automatů pomocí sebesestavování a přenos algoritmicky modifikovatelné genetické informace na potomka. V tomto rámci ukážeme neomezený růst výpočetní síly automatů během evoluce a pomocí interaktivního Turingova stroje chrakterizujeme výpočetní sílu rodových linií automatů.

Nastíníme jednoduchou, ale přesto kognitivně účinnou architekturu kognitivního agenta. Náš model se liší od jiných podobných modelů především využíváním dvou komplementárních vnitřních modelů světa, které mají jiný úkol než v podobných modelech známých z odborné literatury. První z nich zachycuje senzorimotorickou „syntaxi“ agentova chování a je využíván pro situování agenta v jeho prostředí. Druhý model popisuje senzorimotorickou dynamiku světa agenta a je využíván pro řízení agentova chování. Informace v obou vnitřních modelech závisí od agentova ztělesnění a jeho zkušeností. Ukážeme, že kognitivní potenciál našeho modelu podstatně překračuje možnosti dřívějších modelů tím, že podporuje algoritmické procesy podobající se ve svých důsledcích vyšším kognitivním funkcím, jakými jsou imitační učení a rozvoj komunikace, řeči, myšlení a vědomí.

Bakteriod je formální abstraktní hybridní systém, který ve své činnosti kombinuje výpočetní a nevýpočetní mechanizmy. Ukážeme, že v prostředí umělých molekul, nadanými jistými samoorganizačními schopnostmi, některé bakteroidy vykazují znaky minimálního života: jsou autonomní, replikují se a mají schopnost darwinovské evoluce. Návrh bakteroidů je inspirován představami současné molekulární biologie o dnes již neexistujících (či zatím neobjevených) formách protoživota.

V příspěvku budeme hledat výpočetní meze kognitivních a inteligentních systémů, a to jak biologických, tak i umělých a hybridních, které jsou kombinacích obou předchozích druhů. Společnou platformu poskytne komputacionalimus, tj. víra, že kognitivní resp. inteligentní procesy jsou v konečném důsledku výpočetními procesy. Ukážeme, že v principu mohou existovat kognitivní systémy, a dokonce i v praxi existují „zárodky“ takových systémů, které předčí svou výpočetní sílou výpočetní sílu Turingových strojů. Tyto výsledky naznačují, že tzv. Church-Turingovu tezi, hovořící o centrálním postavení Turingových strojů ve světě výpočtů a algoritmů, je třeba vidět v souvislosti s fyzikálními principy, které kognitivní systém při své činnosti využívá, a se způsobem, kterým systém komunikuje s okolím.

Amorphous computing differs from the classical ideas about computations almost in every aspect. The architecture of amorphous computers is random, since they consist of a plethora of identical computational units spread randomly over a given area. Within a limited radius the units can communicate wirelessly with their neighbors via a single-channel radio. We consider a model whose assumptions on the underlying computing and communication abilities are among the weakest possible: all computational units are finite state probabilistic automata working asynchronously, there is no broadcasting collision detection mechanism and no network addresses. We show that under reasonable probabilistic assumptions such amorphous computing systems can possess universal computing power with a high probability. The underlying theory makes use of properties of random graphs and that of probabilistic analysis of algorithms. To the best of our knowledge this is the first result showing the universality of such computing systems.

A computational model of molecularly communicating mobile nanomachines is de¯ned. Nanomachines are modelled by timed probabilistic automata augmented by a severely restricted communication mechanism. We show that for molecular communication among such machines an asynchronous stochastic protocol originally designed for wireless communication in so-called amorphous computers with static computational units can also be used. We design an algorithm that using randomness and timing delays selects with a high probability a leader from among sets of anonymous candidates. This enables a simulation of counter automata proving that networks of mobile nanomachines possess universal computing power.

We propose a simple yet cognitively powerful architecture of an embodied conscious agent whose functioning is in a good agreement with neurophysiological evidence. Our model differs from other proposals by exploiting two complementary internal world models having a different purpose than that in similar models known from the literature. The first model captures the sensorimotor "syntax" of the agent's behavior and is used for situating the agent in its environment. The second model describes the sensorimotor dynamics of the world and is used for controlling the agent's behavior. Both internal world models are built automatically, autonomously in the course of agent's interaction with the environment and their coop- eration takes care of symbol grounding. We show that the cognitive potential of the proposed model goes substantially beyond that of earlier models since it supports algorithmic processes underlying phenomena similar to higher cognitive functions such as imitation learning and the development of commu- nication, language, thinking and consciousness.

Nastíníme jednoduchou, ale přesto kognitivně účinnou architekturu inteligentního agenta. Model využívá dvou komplementárních vnitřních modelů světa: jeden pro „syntax“ poznaného světa a druhý pro jeho sémantiku. Tyto modely řeší problém porozumění konceptům a podporují algoritmické procesy, jejichž efekty se pro pozorovatele jeví jako projevy vyšších kognitivních funkcí, jakými jsou imitační učení, rozvoj komunikace, řeči, myšlení a vědomí.

Classical models of computation no longer fully correspond to the current notions of computing in modern systems. Even in the sciences, many natural systems are now viewed as systems that compute. Can one devise models of computation that capture the notion of computing as seen today and that could play the same role as Turing machines did for the classical case? We propose two models inspired from key mechanisms of current systems in both artificial and natural environments: evolving automata and interactive Turing machines with advice. The two models represent relevant adjustments in our apprehension of computing: the shift to potentially non-terminating interactive computations, the shift towards systems whose hardware and/or software can change over time, and the shift to computing systems that evolve in an unpredictable, non-uniform way. The two models are shown to be equivalent and both are provably computationally more powerful than the models covered by the old computing paradigm. The models also motivate the extension of classical complexity theory by non-uniform classes, using the computational resources that are natural to these models. Of course, the additional computational power of the models cannot in general be meaningfully exploited in concrete goal-oriented computations.