Induction of defeasible logic theories in the legal domain

The market for intelligent legal information systems remains relatively untapped and while this might be interpreted as an indication that it is simply impossible to produce a system that satisfies the needs of the legal community, an analysis of previous attempts at producing such systems reveals a common set of deficiencies that in-part explain why there have been no overwhelming successes to date. Defeasible logic, a logic with proven successes at representing legal knowledge, seems to overcome many of these deficiencies and is a promising approach to representing legal knowledge. Unfortunately, an immediate application of technology to the challenges in this domain is an expensive and computationally intractable problem. So, in light of the benefits, we seek to find a practical algorithm that uses heuristics to discover an approximate solution. As an outcome of this work, we have developed an algorithm that integrates defeasible logic into a decision support system by automatically deriving its knowledge from databases of precedents. Experiments with the new algorithm are very promising - delivering results comparable to and exceeding other approaches

Representation of Incomplete Knowledge by Induction of Default Theories

We present a method to learn simultaneously definitions for a concept and its negation. This problem is relevant when we have to deal with a complex domain where it is difficult to acquire a complete theory and where we have to reason from incomplete knowledge. We use default logic to represent such incomplete theories. This paper specifies the problem of learning a default theory from a set of examples and a background knowledge. We propose an operational method to inductively construct such a theory. Our learning process relies on a generalization mechanism defined in the field of Inductive Logic Programming. We first consider the case where the initial knowledge is sure because it contains only ground facts. Then, we extend the framework to the case where the initial knowledge is a default theory