INTRODUCTION There is mounting evidence that human experts rely heavily on memory of past cases when solving problems in domains such as law, mathematics, design, and strategic planning. Thus, it seems natural to exploit this idea in constructing Al systems. This is the focus of systems using case-based reasoning; it constitutes a fifth major paradigm of machine learning research. A related approach is that of reasoning by analogy. In case-based reasoning ("CBR"), one uses memory of relevant "past" cases to interpret or to solve a new problem case. Rather than creating a solution from scratch, a reasoner using case-based reasoning recalls cases similar to its current problem situation and solves or interprets a problem by reasoning with past solutions and interpretations. A reasoner using case-based reasoning can derive shortcuts and anticipate problems in new situations that might arise by having previously spotted and dealt with them. This can lead to improvement in the quality and efficiency of the reasoning. Case-based reasoning as a learning paradigm has several advantages. First, there are several performance enhancements it provides for its associated performance element: shortcuts in reasoning, the capability of avoiding past errors; the capability of anticipating and therefore avoiding other previously made mist akes, the capability of focusing in on the most important parts of a problem first. Second, learning can be fairly uncomplicated.

In this paper, we consider a distinctly bottom-up which generates arguments by performing a heuristic approach.

In them, debate was heard on four partisan argument.

The research pursued in the early 1980s by Rissland, Ashley, Branting, and Skalak explored the rich vein of case-based reasoning in the context of legal argument. Some of these seminal projects were presented in a special 1991 pair of issues of the International Journal of Man-Machine Studies (e.g., Ashley 1991; Branting, 1991; Rissland & Skalak, 1991). Ideas from these research projects lay the foundation of what is now termed interpretive CBR, that is, how to interpret new cases in light of past interpretations. This work has also influenced the community that develops formal models of argumentation and defeasible reasoning, and these models have in turn contributed more formal models to CBR (e.g., Bench-Capon & Sartor, 2003). The AI and law community continues to provide a rich tributary of ideas and techniques about CBR and for integrating it with other reasoning modalities in CBR hybrids, such as rule-based reasoning, heuristic search, and information retrieval.

As an indispensable supplement to reasoning cases, or mopcs) to outline an argument regarding deductively with legal rules, attorneys and judges reason how to decide the cfs based on its significant similarities to analogically with precedent cases; rule predicates are and differences from mopes. A claim lattice projects the case knowledge seldom exists to legal questions. Legal experts make base (CKB) onto the problem situation to create a neighborhood competing arguments instead, pitting conflicting interpretations of cases surrounding the problem situation in which of cases and facts against each other. We will present a Hypo, a computer program that performs case-based detailed example of a claim lattice actually generated by reasoning in the legal domain, helps attorneys analyze and Hypo to analyze a real legal case. To perform this task, indexing and retrieving relevant cases are not enough.

Also to appear in Proceedings NIE--LRDC Conference on Thinking and Learning Skills, Lawrence Erlbaum Associates.

In mathematics, examples can be said to be as important to understanding as the traditionally exalted definitions, theorems, and proofs (Rissland 1978). In fact, some mathematical areas developed in response to troublesome counter-examples like modern real function theory which has been called'the branch of mathematics which deals with counter-examples" (Munroe 1953). In the law, examples -- that is, legal cases -- are the basis from which the law derives (at least in common law systems like those of the United States and England): the law is made through court decisions by consideration of specific problems in specific cases. The cases lead to rule-like decisions which are then refined (or perhaps refuted, i.e., overturned) in subsequent cases. In linguistics, for instance in the study of syntax, linguistic rules are derived from study of examples of actual language and are then subjected to testing on more examples. Some examples are taken from the infinite store of run of the mill sentences available to every natural speaker; others, like certain difficult garden path sentences, are fabricated and used as counter-examples are in mathematics.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

B.RANDELL 3 PROGRAM PROOF AND MANIPULATION 2 Some techniques for proving correctness of programs which alter data structures.