Varol Akman

In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.

Sir John Lyons’s Linguistic Semantics: An Introduction (Cambridge, UK: Cambridge University Press, 1995) is a tolerable addition to the list of half a dozen or so impressive titles he has produced on linguistic subjects over the years. This book was initially planned to be a second edition of his Language, Meaning and Context (Lyons, 1981). However, in the end it turned out to be a successor and replacement. For it is, in the author’s words, a very different book compared to the 1981 volume: it is much longer, treats topics missing in the earlier volume, and is written in a different style.

Situated semantics can be regarded as an attempt at placing situational context (context of situation) at the center of all discussions of meaning. Situation theory is a theory of information content that takes context very seriously. Individuals, properties, relations, and spatiotemporal locations are basic constructs of situation theory. Individuals are conceived as invariants; having properties and standing in relations, they tend to persist in time and space. An anchoring function binds the location parameters to appropriate objects present in the grounding situation. Anchoring plays a major role in the working of constraints that include nomic constraints, conventional constraints and conditional constraints. Situation semantics develops a theory of meaning that is based on relations between situations. Situation semantics provides a fundamental framework for realistic semantics. The ideas emerging from research into situation semantics have been combined with linguistic work and have led to numerous useful proposals.

The success of set theory as a foundation for mathematics inspires its use in artificial intelligence, particularly in commonsense reasoning. In this survey, we briefly review classical set theory from an AI perspective, and then consider alternative set theories. Desirable properties of a possible commonsense set theory are investigated, treating different aspects like cumulative hierarchy, self-reference, cardinality, etc. Assorted examples from the ground-breaking research on the subject are also given.

"Language has never been accessible to me in the way that it was for Sachs. I'm shut off from my own thoughts, trapped in a no-man's-land between feeling and articulation, and no matter how hard I try to express myself, I can rarely come up with more than a confused stammer. Sachs never had any of these difficulties. Words and things matched up for him, whereas for me they are constantly breaking apart, flying off in a hundred different directions. I spend most of my time picking up the pieces and gluing them back together, but Sachs never had to stumble around like that, hunting through garbage dumps and trash bins, wondering if he hadn't fit the wrong pieces next to each other. His uncertainties were of a different order, but no matter how hard life became for him in other ways, words were never his problem." -Paul Auster, Leviathan

Here, S is a sentence—or possibly a smaller or larger unit of meaningful expression for a language—that's written by an author and c is the circumstance in which S is used. R is defined as the language conventions holding between an author and a reader (or better yet, his readership). P, probably the most important part of the equation, is the content of S or, the intended meaning of the author. We assume that the communication between an author and a reader is limited only to written text. Consequently, it is not possible to ask the author about his intention for writing S; that will have to be discovered by a reader.

In traditional linguistic accounts of context, one thinks of the immediate features of a speech situation, that is, a situation in which an expression is uttered. Thus, features such as time, location, speaker, hearer and preceding discourse are all parts of context. But context is a wider and more transcendental notion than what these accounts imply. For one thing, context is a relational concept relating social actions and their surroundings, relating social actions, relating individual actors and their surroundings, and relating the set of individual actors and their social actions to their surroundings.

Founded in 1993, ELEKTRIK: Turkish Journal of Electrical Engineering and Computer Sciences, has gradually become better known and is fast establishing itself as a research oriented publication outlet with high academic standards. In a modest attempt to advance this trend, this special issue of ELEKTRIK brings together five papers exemplifying the state of the art in artificial intelligence (AI). Written by experts, the papers are especially aimed at readers interested in gaining a better appraisal of the applications side of the AI enterprise. In all papers there is a strong emphasis on measuring the benefits of the proposed approaches in experimental contexts. The papers broadly fall into five actively researched, contemporary domains of AI: pattern recognition, genetic algorithms, fuzzy sets, intelligent design, and agents.

This position paper argues that in addition to the familiar approach using formal contexts, there is now a need in AI to study contexts as social constructs. As a successful example of the latter approach, I draw attention to 'interpretation' (in the sense of literary theory), viz. the reconstruction of intended meaning of a literary text that takes into account the context in which the author assumed the reader would place the text. An important contribution here comes from Harris (1988), enumerating the seven crucial dimensions of context: knowledge of reality, knowledge of language, and the authorial, generic, collective, specific, and textual dimensions. Finally, two thought-provoking papers in interpretation, (Barwise 1989) and (Hobbs 1990), are analyzed as useful attempts which also come to grips with the notion of context.

The notion of context arises in assorted areas of artificial intelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. My aim in this paper is to offer a swift review of context in AI. I will first identify the role of context in various fields of AI. I will then consider the enterprise of formalising context. The focus of my discussion will be McCarthy's (1993) proposal – a landmark study that inspired almost every logical theory of context since then – and its close variants, studied by his students. (The author is grateful to Prof. Carlo Penco for his invaluable assistance with this work.)

Answer Set Programming is a new paradigm based on logic programming. The main component of answer set programming is a system that finds the answer sets of logic programs. During the computation of an answer set, systems are faced with choice points where they have to select a literal and assign it a truth value. Generally, systems utilize some heuristics to choose new literals at the choice points. The heuristic used is one of the key factors for the performance of the system. A new heuristic for answer set programming has been developed. This heuristic is inspired by hierarchical planning. The notion of criticality, which was introduced for generating abstraction hierarchies in hierarchical planning, is used in this heuristic. The resulting system (CSMODELS) uses this new heuristic in a static way. CSMODELS is based on the system SMODELS. The experimental results show that this new heuristic is promising for answer set programming. A comparison of search times with SMODELS demonstrate CSMODELS’ usefulness.

Action Languages are formal methods of talking about actions and their effects on fluents. One recent approach in planning is to define the domains of the planning problems using action languages. The aim of this research is to find a plan for a system defined in the action language C by translating it into a causal theory and then finding an equivalent logic program. The planning problem will then be reduced to finding the answer set (stable model) of this logic program. This planner will be added as an extension to the Causal Calculator (CCALC) which is a model checker for the language of the causal theories