Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

Modeling Legislation Using Natural Language Processing

This paper describes the possibilities of the translation of legislation, which is written in natural language, into a formal language, i.e. UML/OCL. The tool OPAL (Object-oriented Parsing and Analysis of Legislation) is developed to support the automatic modelling of legislation with the use of appropriate NLP techniques. The aim is not to perform this modelling in a batch fashion from legislation to final model, but interactively in dialogue with the knowledge engineer. The main components of OPAL are a parser (based on a chart-parser algorithm) and a model generator. A special component called modelling interface is added to OPAL to give the knowledge engineer the possibility to keep track of the modelling process and to make adjustments to the final model

GoldminePower: Decisions in Governmental Process Design

The success of governmental policy depends much on the success of governmental services. Governmental services in its turn depend much on governmental processes. Governmental processes therefore are a determining factor for the success and especially the justification of governmental behaviour. Process design is an important means of realising good en well-performing governmental processes. Governments raise specific demands and wishes in the field of process design, such as transparency and justification of policy. The process design method applied should consequently reflect these important aspects of governmental process design. A reasonabl

The POWER light-version: Improving Legal Quality under Time Pressure

The Dutch Tax and Customs Administration conducts a research program Program for an Ontology-based Working Environment for Rules and regulations (POWER). In this research program that was started in 1999 and is sponsored by the European Commission (E-POWER) since September 2001 an ICT-based methodology has been developed that enables the formalization of legal sources and finally the design of legal knowledge-based systems. The full-scale POWER-method however although much less time consuming than normal software design methodologies is still too elaborate especially if we want to apply this method in legal drafting or policy making processes. We therefore created the POWER-light version, a variant of the POWER-method that helps to improve legal quality and can be used with relatively little effort and in short time. Although the POWER-light version lacks many of the advantages of the regular POWER-method (e.g. its verification, simulation and knowledge-based component generation abilities) it offers a first step. The POWER-light approach offers the tools to get the best possible legal quality given the time restrictions

UvA-DARE (Digital Academic Repository) From Inter-Agent to Intra-Agent Representations: Mapping Social Scenarios to Agent-Role Descriptions From Inter-Agent to Intra-Agent Representations: Mapping Social Scenarios to Agent-Role Descriptions

Abstract: The paper introduces elements of a methodology for the acquisition of descriptions of social scenarios (e.g. cases) and for their synthesis to agent-based models. It proceeds along three steps. First, the case is analyzed at signal layer, i.e. the messages exchanged between actors. Second, the signal layer is enriched with implicit actions, intentions, and conditions necessary for the story to occur. This elicitation is based on elements provided with the story, common-sense, expert knowledge and direct interaction with the narrator. Third, the resulting scenario representation is synthesized as agent programs. These scripts correspond to descriptions of agent-roles observed in that social setting

UvA-DARE (Digital Academic Repository) Implementing Explanation-Based Argumentation using Answer Set Programming Implementing Explanation-Based Argumentation using Answer Set Programming

Abstract. This paper presents an implementation for an explanationbased theory of argumentation. Instead of referring to attack/support relationships between arguments, as in traditional argumentation theories, we focus on the relation of messages with the space of hypothetical explanations. The consequences of this choice are two-fold. First, attack and support relationships become derivative measures. Second, we unveil a natural integration with probabilistic reasoning. The proposed operationalization is based on stable models semantics for logic programming