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Case-based reasoning and its implications for legal expert systems

Case-based reasoning and its implications for legal expert systems 2025

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The document discusses the implications of Case-Based Reasoning (CBR) for legal expert systems, emphasizing how reasoning with past cases can enhance decision-making in law. It outlines five paradigms of CBR: statistically-oriented, model-based, planning/design-oriented, exemplar-based, and adversarial/precedent-based. Each paradigm has unique methods for generalizing from cases and assessing relevance. The paper argues that CBR can improve rule-based systems by supplementing their reasoning capabilities, enhancing legal data retrieval, and aiding in cognitive modeling of legal reasoning. It highlights the importance of integrating CBR techniques into legal applications to address ill-structured problems and improve explanations and performance.

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What is legal case-based reasoning?

All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases.

What is an example of a case-based reasoning system?

In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning.

What is the difference between case-based reasoning and rule-based reasoning?

For instance, case-based reasoning using past experiences as problem solving tools, is limited to solve problems which match with the old cases stored in the knowledge base of the Decision Support System, while rule-based approach, reasoning from a well constructed domain theory, have difficulty reasoning problems

What is an example of a case-based reasoning application?

For example, Google Maps uses case-based reasoning to tell you how long your journey will take by examining the patterns of past users to see how long it took them to get from point A to point B. Even if your path is from two slightly different points, it makes inferences on how long your journey will take.

What are the problems with case-based reasoning?

Since case-based reasoning is based on anecdotal past experiences, it is very personal and deviates from facts and agreed-upon knowledge. This makes it not the best approach when working on a team.

What is the difference between model-based reasoning and case-based reasoning in expert systems?

In combining CBR and model-based reasoning, model-based reasoning is good at handling wellunderstood components under normal situations, whereas CBR covers the part of the domain that does not have a good model or theory.

What is case-based reasoning in expert system?

Case-based reasoning (CBR) systems simplifies the process by using a collection of past problem solutions (cases) to address new problems (Kolodner, 1993). The basic idea underlining this approach is what was true yesterday is likely to be true today.

What is a case-based reasoning?

Case-Based Reasoning is a strategy that involves using a knowledge base of past problems and solutions to address new situations. It is commonly used in various domains like medicine, law, and technical help desks to solve problems by drawing analogies with similar cases.

Case-based reasoning and its implications for legal expert systems 2025

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