. In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study. 1 Introduction Case-based reasoning (CBR) systems solve new problems by retrieving prior solutions of similar previous problems and performi...

We have built a hybrid Case-Based Reasoning (CBR) and Information Retrieval (IR) system that generates a query to the IR system by using information derived from CBR analysis of a problem situation. The query is automatically formed by submitting in text form a set of highly relevant cases, based on a CBR analysis, to a modified version of INQUERY's relevance feedback module. This approach extends the reach of CBR, for retrieval purposes, to much larger corpora and injects knowledge-based techniques into traditional IR. 1 Introduction One strength of Case-Based Reasoning (CBR) systems is the ability to reason about a problem case and perform highly intelligent problem-solving, such as the generation of legal arguments or detailed operational plans [9]. In particular, CBR systems have at their core the ability to retrieve highly relevant cases. However, CBR systems are limited by the availability of cases actually represented in their case bases. Among current case-based reasoning syst...

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that locates passages likely to contain information about legally relevant features of cases found in full-text court opinions. SPIRE uses an example base of excerpts from past opinions to form queries, which are run by the INQUERY IR text retrieval engine on individual case opinions. These opinions can be those found by SPIRE in a prior stage of processing, which also employs a hybrid CBR-IR approach to retrieve relevant texts from large document corpora. (This aspect of SPIRE was reported on at ICAIL95.) We present an overview of SPIRE, run through an extended example, and give results comparing SPIRE's with human performance. 1 Introduction There is an enormous amount of legal text available on-line and it is growing every day. While this is a decided benefit for legal research, it also presents a problem of how to search it effectively. In particular, it is no easy task to...

We discuss the use of Case-Based Reasoning (CBR) to drive an Information Retrieval (IR) system. Our hybrid CBR-IR approach takes as input a standard frame-based representation of a problem case, and outputs texts of relevant cases retrieved from a document corpus dramatically larger than the case base available to the CBR system. While the smaller case base is accessible by the usual case-based indexing, and is amenable to knowledge-intensive methods, the larger IR corpus is not. Our approach provides two benefits: it extends the reach of CBR (for retrieval purposes) to much larger corpora, and it enables the injection of knowledge-based techniques into traditional IR. Our system works by first performing a standard HYPO-style CBR analysis, and then using texts associated with certain important cases found in this analysis to "seed" a modified version of INQUERY's relevance feedback mechanism in order to generate a query. We describe our approach and report on experiments performed in two different legal domains.

This position paper sketches a framework for modeling introspective reasoning and discusses the relevance of that framework for modeling introspective reasoning about memory search. It argues that effective and flexible memory processing in rich memories should be built on five types of explicitly represented self-knowledge: knowledge about information needs, relationships between different types of information, expectations for the actual behavior of the information search process, desires for its ideal behavior, and representations of how those expectations and desires relate to its actual performance. This approach to modeling memory search is both an illustration of general principles for modeling introspective reasoning and a step towards addressing the problem of how a reasoner— human or machine—can acquire knowledge about the properties of its own knowledge base.

is a huge manual undertaking, particularly when there are fifty or more texts. Unfortunately, full-text understanding is not yet feasible as an alternative and information extract techniques themselves rely on large numbers of training texts with manually encoded answer keys. By locating and presenting relevant passages to the user, we will have significantly reduced the time and effort expenditure. Alternatively, we could save an automated informationextraction system from processing an entire text by focusing the system on those portions of the text most likely to contain the desired information. This work integrates a case-based reasoner with an IR engine to reduce the information bottleneck. SPIRE [Se- This research was supported byNSF Grant no. EEC-9209623, State/Industry/University Cooperative Research on Intelligent Information Retrieval, Digital Equipment Corporation and the National Center for Automated Information Research. lection of Passages for Inf

Most work in artificial intelligence and law has concentrated on modelling the type of reasoning done by trial lawyers. In fact, most lawyers' work involves planning -- for example, wills and trusts, real estate deals, and business mergers and acquisitions. Certain planning issues, such as the use of underspecified, or "open-textured" rules, are illustrated especially clearly in this domain. In this thesis, I set forth the characteristic features of planning in law, place it in the context of past artificial intelligence work in both law and planning, and describe CHIRON, a system that I have developed implementing my theory of open-textured planning in the domain of personal income tax law.

The case-based reasoning paradigm models how reuse of stored experiences contributes to expertise. In a case-based problem-solver, new problems are solved by retrieving stored information about previous problem-solving episodes and adapting it to suggest solutions to the new problems. The results are then themselves added to the reasoner's memory in new cases for future use. Despite this emphasis on learning from experience, however, experience generally plays a minimal role in models of how the case-based reasoning process is itself performed. Case-based reasoning systems generally do not refine the methods they use to retrieve or adapt prior cases, instead relying on static pre-defined procedures. The thesis of this article is that learning from experience can play a key role in building expertise by refining the case-based reasoning process itself. To support that view and to illustrate the practicality of learning to refine case-based reasoning, this article presents ongoing resear...

The case-based reasoning (CBR) process solves problems by retrieving prior solutions and adapting them to fit new circumstances. Many studies examine how casebased reasoners learn by storing new cases and refining the indices used to retrieve cases. However, little attention has been given to learning to refine the process for applying retrieved cases. This paper describes research investigating how a case-based reasoner can learn strategies for adapting prior cases to fit new situations, and how its similarity criteria may be refined pragmatically to reflect new capabilities for case adaptation. We begin by highlighting psychological research on the development of similarity criteria and summarizing our model of case adaptation learning. We then discuss initial steps towards pragmatically refining similarity criteria based on experiences with case adaptation. Introduction Case-based reasoning (CBR) is a reasoning process that solves new problems by retrieving similar prior problemsol...

Investigations of learning in case-based reasoning (CBR) have traditionally focused on learning two types of knowledge: new cases and new indexing criteria for case retrieval. However, there is increasing recognition that other types of knowledge also play crucial roles in the case-based reasoning process. The effectiveness of a CBR system depends not only on having and retrieving relevant cases, but also on selecting which retrieved cases to apply and determining how to adapt them to fit new situations. Consequently, case-based reasoning can benefit from using multiple learning strategies to acquire, in addition to new cases and indices, new case adaptation strategies and similarity criteria. This paper describes ongoing research that studies how multiple types of learning can improve the case-based reasoning process and examines their interrelationship in contributing to the overall performance of a CBR system. Introduction Case-based reasoning (CBR) solves new problems by retrievin...