Application of Fuzzy Numbers to the Assessment of CBR Systems

Case-Based Reasoning (CBR) is the process of solving problems by properly adapting the solutions of similar (analogous) problems solved in the past. As an Artificial Intelligence's method CBR has become recently very popular to information managers increasing the effectiveness and reducing the cost of various human activities by substantially automated processes, such as diagnosis, scheduling, design, etc. In this paper a combination is utilized of the Centre of Gravity defuzzification technique and of the Fuzzy Numbers for assessing the effectiveness of CBR systems. Our new fuzzy assessment approach is validated by comparing its outcomes in our applications with the corresponding outcomes of two traditional assessment methods, the calculation of the mean values and the GPA index

On the concept of relevance in legal information retrieval

The concept of 'relevance' is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems

Legal analogical reasoning - the interplay between legal theory and artificial intelligence

This thesis examines and critiques attempts by researchers in the field of artificial intelligence and law to simulate legal analogical reasoning. Supported by an analysis of legal theoretical accounts of legal analogising, and an examination of approaches to simulating analogising developed in the field of artificial intelligence, it is argued that simulations of legal analogising fall far short of simulating all the is involved in human analogising. These examinations of legal theory and artificial intelligence inform a detailed critique of simulations of legal analogising. It is argued that simulations of legal analogising are limited in the kind of legal analogising they can simulate - these simulations cannot simulate the semantic flexibility that is characteristic of creative analogising. This thesis argues that one reason for current restrictions on simulations of legal analogising is that researchers in artificial intelligence and law have ignored the important role played by legal principles in legal analogising. It is argued that improvements in simulations of legal analogising will come from incorporating the influence of legal principles on legal analogising and that until researchers address this semantic flexibility and the role that legal principles play in generating it, simulations of legal analogising will be restricted and of benefit only for limited uses and in restricted areas of the law. Building on the analysis of legal theoretical accounts of legal reasoning and the examination of the processes of analogising, this thesis further argues that legal theoretical accounts of legal analogising are insufficient to account for legal analogising. This thesis argues that legal theorists have themselves ignored important aspects of legal analogising and hence that legal theoretical accounts of legal analogising are deficient. This thesis offers suggestions as to some of the modifications required in legal theory in order to better account for the processes of legal analogising