Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoning

The present magisterial conference dictated on October 21, 2015 in the frame of the activities for 45th anniversary of the Anthropologic Sciences Faculty of the Autonomous University of Yucatan.La presente conferencia magistral se dictó el día 21 de octubre de 2015 en el marco de las actividades por el 45 aniversario de la Facultad de Ciencias Antropológicas de la Universidad Autónoma de Yucatán

Steps Along the On-Line Assistance Spectrum

In this paper, we discuss the spectrum of on-line assistance ranging from passive, canned to active, user-customized. We discuss various of on-line assistance: interactive introductory tutorials, on-line help, and on-line manuals. We then describe two steps to make on-line assistance more intelligent: (1) inclusion and customization of examples in the information provided the user; and (2) integration of various aspects of on-line assistance like tutorials and help

A Case-Based Approach to Intelligent Information Retrieval

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..

The Law as a Learning System

In this paper, we present three examples of the development of legal concepts and rules analyze them in therms of AI learning methods, in particular, the candidate elimination algorithm. The three areas of legal doctrine are: (1) the doctrine of the liability of manufacturers to third party buyers; (2) the Fourth Amendment doctrine relating to stop-and-frisk searches; and (3) the doctrine of attractive nuisance. For each of the legal areas, we give a synopsis of the legal developments and show how it can be viewed from an Ai point of view

Integrating IR and CBR to Locate Relevant Texts and Passages.

This paper presents the SPIRE system, a hybrid case-based reasoning (CBR) and information retrieval (IR) system that (1) from a large text collection, retrieves documents that are relevant to a presented problem case, and (2) highlights within those retrieved documents passages that contain relevant information about specific case features. We present an overview of SPIRE, run through an extended example, and present results comparing SPIRE's with human performance. We also compare the results obtained by varying the method by which queries are generated. SPIRE aids not only problem-solving but knowledge acquisition by focusing a text extractor--person or program--on areas of text where needed information is likely to be found. Once extracted, this information can be used to create new cases or data-base objects thus closing the loop in the problem-solving-knowledge-acquisition process. 1 Introduction The quantity of text available on-line is growing explosively. On the World Wide We..

What you saw is what you want: Using Cases to seed Information Retrieval.

. This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpts from past case texts. Both are used by SPIRE to automatically generate queries, which are then run by the INQUERY full-text retrieval engine on a large text collection in the case of document retrieval and on individual text documents for passage retrieval. 1 Introduction A good indication of what to look for in a new problem situation is often given by examples of what has worked in the past. This idea---the fundamental tenet of case-based reasoning---is applicable in information retrieval (IR) as well. We have employed this idea at two levels in a hybrid CBR-IR approach: 1. within a corpus..