Deontic logic is the logic that deals with actual as well as ideal behavior of systems. In this paper, we survey a number of applications of deontic logic in computer science that have arisen in the eighties, and give a systematic framework in which these applications can be classified. Many applications move in the direction of programming a computer in deontic logic to make the computer prohibit, permit or obligate people to do something. We discuss conditions under which this possibility is realistic and conditions under which it would be admissible to do so.

This article is an exercise in computational jurisprudence. It seems clear that the field of AI and Law should draw upon the insights of legal philosophers, whenever possible. But can the computational perspective offer anything in return? I will explore this question by focusing on the concept of OWNERSHIP, which has been debated in the jurisprudential literature for centuries. Although the intellectual currents here flow mostly in one direction — from legal philosophy to AI — I will show that there are also some insights to be gained from a computational analysis of the OWNERSHIP relation. In particular, the article suggests a computational explanation for the emergence of abstract property rights, divorced from concrete material objects.

Most legal expert systems attempt to implement complex models of legal reasoning. Yet the utility of a legal expert system lies not in the extent to which it simulates a lawyer's approach to a legal problem, but in the quality of its predictions and of its arguments. A complex model of legal reasoning is not necessary: a successful legal expert system can be based upon a simplified model of legal reasoning. Some researchers have based their systems upon a jurisprudential approach to the law, yet lawyers are patently able to operate without any jurisprudential insight. A useful legal expert system should be capable of producing advice similar to that which one might get from a lawyer, so it should operate at the same pragmatic level of abstraction as does a lawyer—not at the more philosophical level of jurisprudence. A legal expert system called SHYSTER has been developed to demonstrate that a useful legal expert system can be based upon a pragmatic approach to the law. SHYSTER has a simple representation structure which simplifies the problem of knowledge acquisition. Yet this structure is complex enough for SHYSTER to produce useful advice. SHYSTER is a case-based legal expert system (although it has been designed so that it can be linked with a rule-based system to form a hybrid legal expert system). Its advice is based upon an examination of, and an argument about, the similarities and differences between cases. SHYSTER attempts to model the way in which lawyers argue with cases, but it does not attempt to model the way in which lawyers decide which cases to use in those arguments. Instead, it employs statistical techniques to quantify the similarity between cases. It decides which cases to use in argument, and what prediction it will make, on the basis of that similarity measure. SHYSTER is of a general design: it provides advice in areas of case law that have been specified by a legal expert using a specification language. Four different, and disparate, areas of law have been specified for SHYSTER, and its operation has been tested in each of those legal domains. Testing of SHYSTER in these four domains indicates that it is exceptionally good at predicting results, and fairly good at choosing cases with which to construct its arguments. SHYSTER demonstrates the viability of a pragmatic approach to legal expert system design.

Most work in artificial intelligence and law has concentrated on modelling the type of reasoning done by trial lawyers. In fact, most lawyers' work involves planning -- for example, wills and trusts, real estate deals, and business mergers and acquisitions. Certain planning issues, such as the use of underspecified, or "open-textured" rules, are illustrated especially clearly in this domain. In this thesis, I set forth the characteristic features of planning in law, place it in the context of past artificial intelligence work in both law and planning, and describe CHIRON, a system that I have developed implementing my theory of open-textured planning in the domain of personal income tax law.

This paper investigates the theoretical foundations of a clausal action specification language designed to support artificial intelligence models of legal reasoning. We focus on two issues. First, action compositionality, which we achieve by treating actions as propositions, so that we may admit logical relations between actions and treat conjunction of actions as parallel composition. Second, we consider deontic actions, which change the interpretation of modal operators on actions, specifically the modality of permission. Each specification in the language is guaranteed to have a unique model, a Kripke structure which describes the possible actions, the effects of these actions and how the permissible actions change from state to state. This model is obtained as the fixpoint of an operator on a class of Kripke structures. We establish completeness of a top-down proof theory for query a...

te EULE2 checks whether the knowledge base is still valid with respect to the underlying regulations and federal law. If a violation occurs, EULE2 would reject the modification and offer an explanation to the office worker about the violation. Has a knowledge base manipulation proved valid, EULE2 checks for each action that leads from the current state to a subsequent state whether it is now executable or not. Certain actions may be illegal, certain others possible, while still others are obligatory. The office worker may decide to initiate one of the possible actions, may inquire why an 1 EULE2 is the knowledge-based version of a hypertext-based information system called EULE. obligatory action must be executed, or may ask why an illegal action is not permitted. Finally, the office worker selects an action for execution. The action causes new instances to be created or existing instances to be modified and leads to a new state. To achieve the functiona

This paper discusses the potential for providing knowledge based support for the task of formulating policy, and determining what legislation is required to implement the policy. From a discussion of previous work in this area, certain major obstacles are identified. Chief among these is the need to match what the KBS can do with the way in which policy makers conceptualise and perform their task. Effective support can only be provided by a system which can be fully integrated into the working practice of its users. Some examples of an alternative approach, based on hypertext, are discussed, and some proposals for overcoming the obstacles with a combination of the hypertext and knowledge based approaches are given. 1.