Modeling Legal Argument: Reasoning with Cases and Hypotheticals 1 is the second book in the series on Artificial Intelligence and Legal Reasoning edited by L. Thome McCarty and Edwina Rissland, two of the leaders in a relatively new field that attempts to apply techniques of computer science, formal decision theory, and artificial intelligence ("AT") to law and legal reasoning. Like the previou...

1.0 Introduction Researchers in Artificial Intelligence and Law (AI & Law) have developed a number of computational models of case-based legal reasoning. For example, see Hypo (Ashley, 1987; 1990), CABARET (Rissland & Skalak, 1991), GREBE (Branting, 1991; 1999), CATO (Aleven, 1997; 2003), BankXX (Rissland, Skalak, et al. 1996), and Split-Up (Zeleznikow, Stranieri, et al., 1995-1996). The models...

In this paper rOWLer, a hybrid rule engine for legal reasoning is presented. The engine combines the expressiveness of rules and ontologies to enable legal reasoning – hence the name “rOWLer”. It is tailored for use in public administration (tax law, pension law, social benefits law, etc.) and provides a flexible architecture, in particular concerning amendments, which allows for adaption to di...

We consider a problem of causality in legal reasoning. Conditio sine qua non (c.s.q.n) is a frequently used heuristics which determines a causality in legal reasoning. We argue that a paradox of c.s.q.n.is derived from a confusion between disjunction of causes and disjunctive cause and give a logical solution to the paradox using minimal abduction.

Most legal decision support systems have generally operated in domains with well-understood norms. Hence reasoning has been represented by a combination of rule-based and case-based reasoning. However, we analyse legal domains in which decision makers are allowed a significant amount of discretion. We argue that if the domain is bounded, and a sufficient number of commonplace cases exist, then ...

Motivation This paper introduces DANIEL,1 an architecture for the integration of case-based reasoning and rule-based reasoning for legal interpretation. Rather than interleaving the reasoners and assuming their complementarity, like in previous approaches, they are applied concurrently. Conflicting interpretations are handled explicitly, based on domain knowledge and on the notion of redundancy...