The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

The Generative Lexicon

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

What is modal logic

http://www.cs.man.ac.uk/~horrocks/Publications/download/2003/HoPH03a.pdf

From SHIQ and RDF to OWL: the making of a Web Ontology Language

http://www.cs.man.ac.uk/~ezolin/dl/bib/Attributive_concept_descriptions_with_complements_(AI_1991).pdf

Attributive concept descriptions with complements

The Oz Programming Model (OPM) is a concurrent programming model subsuming higher-order functional and object-oriented programming as facets of a general model. This is particularly interesting for concurrent object-oriented programming, for which no comprehensive formal model existed until now. The model can be extended so that it can express encapsulated problem solvers generalizing the problem solving capabilities of constraint logic programming. OPM has been developed together with a concomitant programming language Oz, which is designed for applications that require complex symbolic computations, organization into multiple agents, and soft real-time control. An efficient, robust, and interactive implementation of Oz is freely available.

The Oz Programming Model

/pdf/the-oz-programming-model-yi646dhlvd.pdf

This paper studies feature-description languages that have been developed for use in unification grammars, logic programming, and knowledge representation. The distinctive notational primitives of these languages are features that can be understood as unary partial functions on a domain of abstract objects. We show that feature-description languages can be captured naturally as sublanguages of first-order predicate logic with equality and show the equivalence of a loose Tarski semantics with a fixed feature-graph semantics for quantifier-free constraints. For quantifier-free constraints we give a constraint solving method and show the NP-completeness of satisfiability checking. For general feature constraints with quantifiers satisfiability is shown to be undecidable. Moreover, we investigate an extension of the logic with sort predicates and set-denoting expressions called feature terms.

Feature-constraint logics for unification grammars

Publisher Summary This chapter discusses order-sorted equational computation. Many-sorted equational logic is the basis for algebraic specifications, rewriting techniques, unification theory, and equational programming. In the standard approach, sorts are unrelated and can be thought of as denoting disjoint sets. Order-sorted equational logic improves the expressivity of many-sorted equational logic by adding the notion of subsorts. The standard example of an abstract data type, stacks of natural numbers, is specified in order-sorted equational logic. It is known that defining a less or equal test for integers with unconditional equations not using subsorts is complicated; one has to introduce an auxiliary function and an auxiliary sort. These complications disappear if one uses conditional equations, but verification methods for the confluence of conditional rewriting systems are complicated and in most cases not practical. The chapter presents the verification methods for confluence extend to order-sorted unconditional rewriting systems.

Gert Smolka

Papers

Attributive concept descriptions with complements

The Oz Programming Model

The Oz Programming Model

Feature-constraint logics for unification grammars

Order-Sorted Equational Computation