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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.