We discuss key ingredients of case-based reasoning, in general, and 3. A technique, the "claim lattice", for organizing the the correspondence of these to elements of HYPO.

Most recent work on reasoning with cases in law has taken the style of reasoning used in the CATO system as its model, and uses the notion of factors, as found in that system. Fundamental to CATO, a successor the HYPO system, were factors, which are closely related to HYPO-style dimensions. In this paper, we will argue that the simplification involved in using factors, while it has proved pragmatically useful both for clarifying understanding of certain aspects of reasoning with cases and for implementation, causes problems with domain analysis and precludes certain kinds of argument that we would wish to model. We therefore believe that the time is now ripe to go back to the original notion of dimensions, while retaining the insights that have been gained from working with the simpler notion of factors. The paper uses two case studies to argue that this is so.

Scripts, Plans, Goals, and understanding.

For In this paper we discuss a general approach to detecting instance, a single Supreme Court case created the conceptual change developed in our on-going work on "automobile exception" to the Fourth Amendment's warrant concept drift [Rissland et al., 1994]. We illustrate our requirement for a constitutionally acceptable search; this approach on an actual, still evolving, legal example, the case forever changed the meaning of our Fourth "good faith" concept in the law of personal bankruptcy. In Amendment, which is still evolving today [Rissland, 1989; our approach, we detect that a concept is changing by Rissland & Collins, 19861.

We use the idea of a "dimension," how they are generated.

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.