Abstract not found

The main premise of this paper is that certain kinds of non-monotonic reasoning can be solved within first order logic in a simple monotonic way by formulating problems in a suitable environment. Any problem is formalized as a set of contexts, where a context is a (first order) formalization of a piece of the problem. Reasoning comes out as a result of deduction in different contexts. The claim is that proofs built in this way are clearer and better resemble the kind of explanation that humans give when describing some phenomenon. This thesis is articulated discussing the example about non-monotonic reasoning reported in [MD80].

A computer simulation model, can produce some interesting and surprising results which one would not expect from initial analysis of the algorithm and data. We question however, whether the description of such a computer simulation modelling procedure (data + algorithm + results) can constitute an explanation as to why the algorithm produces such an effect. Specifically, in the field of theoretical biology, can such a procedure constitute real scientific explanation of biological phenomena? We compare computer simulation modelling to explicit mathematical treatment concluding that there are fundamental differences between the two. Since computer simulations can model systems that mathematical models can not, we look at ways of improving explanatory power of computer simulations through empirical style study and mechanistic decomposition.

The notion of an instance is ubiquitous in knowledge representations for domain modeling. Most languages used for domain modeling offer syntactic or semantic restrictions on specific language constructs that distinguish individuals and classes in the application domain. The use, however, of instances and classes to represent domain entities has been driven by concerns that range from the strictly practical (e.g. the exploitation of inheritance) to the vaguely philosophical (e.g. intuitive notions of intension and extension). We demonstrate the importance of establishing a clear ontological distinction between instances and classes, and then show modeling scenarios where a single object may best be viewed as a class and an instance. To avoid ambiguous interpretations of such objects, it is necessary to introduce separate universes of discourse in which the same object exists in different forms. We show that a limited facility to support this notion exists in modeling languages like Smalltalk and CLOS, and argue that a more general facility should be made explicit in modeling languages.

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The articulation process of dynamical networks is studied with a functional map, a minimal model for the dynamic change of relationships through iteration. The model is a dynamical system of a function f, not of variables, having a self-reference term f ◦ f, introduced by recalling that operation in a biological system is often applied to itself, as is typically seen in rules in the natural language or genes. Starting from an inarticulate network, two types of fixed points are formed as an invariant structure with iterations. The function is folded with time, until it has finite or infinite piecewise-flat segments of fixed points, regarded as articulation. For an initial logistic map, attracted functions are classified into step, folded step, fractal, and random phases, according to the degree of folding. Oscillatory dynamics are also found, where function values are mapped to several fixed points periodically. The significance of our results to prototype categorization in language is discussed. 1

This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined ìscriptî to experiments for

The KBSE community is actively engaged in finding ways to represent software and the activities that relate to various stages in its lifecycle. While the wealth of modeling activities have, necessarily, been founded on first order logic based representations, this paper reports on research into Software Information Systems that has found the domain of software knowledge to be inherently second order. A facility for accurately representing second order constructs such as are found in the software domain is also presented. Keywords: Knowledge Representation, Domain Modeling, Program Understanding, Software Information Systems. 1 Introduction The fields of Knowledge Representation, Domain Modeling, and KBSE deal primarily with representation systems that are first order. Often the users of these systems take for granted the fact that their representations are limited to first order, and forget that the world is full of knowledge requiring higher order reasoning. This paper begins with a...

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information con...

The paper discusses what kind of entity the proposed Semantic Web (SW) is, and does so principally by reference to the relationship of natural language structure to knowledge representation (KR). It argues that there are three distinct views on the issue: first, that the SW is basically a renaming of the traditional AI knowledge representation task, with all its problems and challenges. Secondly, there is a view that the SW will be, at a minimum, the WorldWideWeb (WWW) with its constituent documents annotated so as to yield their content, or meaning structure, more directly. This view of the SW makes natural language processing central as the procedural bridge from texts to KR, usually via some form of automated Information Extraction. This view is discussed in some detail and it is argued that this can also be seen as a way of justifying the structures used as KR for the SW. There is a third view, possibly Berners-Lee's own, that the SW is about trusted databases as the foundation of a system of web processes and services, but it is argued that this ignores the whole history of the web as a textual system, and gives no better guarantee of agreed meanings for terms than the other two approaches. There is also a fourth view, much harder to define and discuss, which is that if the SW just keeps moving as an engineering development and is lucky (as the successful scale-up of the WWW seems to have been luckier, or better designed, than many cynics expected) then real problems will not arise