An important problem to be addr essed within Event-Driven Architecture (EDA) is how to correctly and efficiently capture and process the event/action-based logic. This paper endeavors to bridge the gap between the Knowledge Representation (KR) approaches based on durable events/actions and such formalisms as event calculus, on one hand, and event-condition-action (ECA) reaction rules extending the approach of active databases that view events as instantaneous occurrences and/or sequences of events, on the other. We propose formalism based on reaction rules (ECA rules) and a novel interval-based event logic and present concrete RuleML-based syntax, semantics and implementation. We further evaluate this approach theoretically, experimentally and on an example derived from common industry use cases and illustrate its benefits.

We show how solution concepts in games such as Nash equilibrium, correlated equilibrium, rationalizability, and sequential equilibrium can be given a uniform definition in terms of \emph{knowledge-based programs}. Intuitively, all solution concepts are implementations of two knowledge-based programs, one appropriate for games represented in normal form, the other for games represented in extensive form. These knowledge-based programs can be viewed as embodying rationality. The representation works even if (a) information sets do not capture an agent's knowledge, (b) uncertainty is not represented by probability, or (c) the underlying game is not common knowledge.

Event-driven reactive functionalities are an urgent need in nowadays distributed service-oriented applications and (Semantic) Web-based environments. An important problem to be addressed is how to correctly and efficiently capture and process the event-based behavioral, reactive logic represented as ECA rules in combination with other conditional decision logic which is represented as derivation rules. In this paper we elaborate on a homogeneous integration approach which combines derivation rules, reaction rules (ECA rules) and other rule types such as integrity constraint into the general framework of logic programming. The developed ECA-LP language provides expressive features such as ID-based updates with support for external and self-updates of the intensional and extensional knowledge, transactions including integrity testing and an event algebra to define and process complex events and actions based on a novel interval-based Event Calculus variant.

Rule-based policy and contract systems have rarely been stu died in terms of their software engineering properties. This is a serious omission, because in rule-based policy or contract representat ion languages rules are being used as a declarative programming language to form alize real-world decision logic and create IS production systems upon. This paper adopts an SE methodology from extreme programming, namely t est driven development, and discusses how it can be adapted to verificat ion, validation and integrity testing (V&V&I) of policy and contract sp ecifications. Since, the test-driven approach focuses on the behavioral a spects and the drawn conclusions instead of the structure of the rule base a nd the causes of faults, it is independent of the complexity of the rule lan guage and the system under test and thus much easier to use and understand f or the rule engineer and the user.

In this report, a novel approach to intelligence and learning is introduced; this approach is based upon what we called percep tion logic. W h at we call ' perception automata ' is introduced in which learning is accom p lished at different perception resolution. Learning in this autom a ta is not heuristic, rather it guarantees the convergence of the approxim a ted function to whatever precision required. Furthe rm ore, the learning process can take place on-line and in at m o st O(log(N)) epochs, where N is the num ber of sam p les. The perception autom a ta is based on hierarchal leve ls of resolution in which each level adds som e details to the constructed function till th e final level can successfully reconstruct the whole function. This approach com b ines the favors of com putational approach in the sense that it is precise, structural and rigorous, and the features of distributed processing and adaptivity of soft com puting, as well as continuity and real-tim e response of dynam i cal system s.

We present here a formal foundation for an iterative and incremental approach to constructing and evaluating preference queries. Our main focus is on query modification: a query transformation approach which works by revising the preference relation in the query. We provide a detailed analysis of the cases where the order-theoretic properties of the preference relation are preserved by the revision. We consider a number of different revision operators: union, prioritized and Pareto composition. We also formulate algebraic laws that enable incremental evaluation of preference queries. Finally, we consider two variations of the basic framework: finite restrictions of preference relations and weak-order extensions of strict partial order preference relations.

Although a number of Computer Aided Process Planni ng (CAPP) systems have been implemented, human planners are still irreplaceable for actual manufacturing. Because process planning requires multiple types of human expertise, there is a common trend to apply knowledge-based techniques for solving the process planning tasks. This circumstance is conducive to developing so-called CAPP Expert Systems (CAPPES). A few approaches to building CAPPES can be found through means-aids analysis of the research literature since 1980. At the same time, it can be seen that authors' efforts in those papers have mostly been made in special cases of CA PPES implementation, whereas the problem of "How to develop CAPPES" on the whole is still open. Se veral general conceptions and methodologies for CAPP have been published, but no fairly versatile technology is yet known. The aim of the paper is to consider the us age of logical models for development of a CAPPES building technology.

The research results described are concerned with: - developing a domain modeling method and tools to provide the design and implementation of decision-making support systems for computer integrated manufacturing; - building a decision-making support system based on know-how and its software environment. The research is funded by NEDO, Japan.

In the late 90th, the complex of concepts, theories, technologies and software called knowledge-based systems has become a key point in development of many future-oriented manufacturing paradigms, such as Agile Manufacturing and Intelligent Manufacturing Systems. Besides, the progress from craft production, to automated and flexible production and to wards'next generation' production is now realized to be in many respects determined by the human/systems ability to handle the domain knowledge rather than simply by a given standard of knowledge in the domain. This is the motivation for a continuously growing research interest to utilization of manufacturing knowledge. It should be noted however, that while a great many reports on different theoretical and applied aspects of knowle dge utilization have been published, the issues of the specificity of manufacturing knowledge and the appropriateness of the methodologies brought into manufacturing from other domains to build knowledge-based systems have not been given due attention. One instance of this research lack is given in this paper with the phenomenon of know-how. It was discovered rather long ago that know-how plays an important role during the solving of professional tasks in manufacturing (e.g.

Through the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarative source-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its plp front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.