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. (shrink)

In this paper we introduce a computational control framework that can keep AI-driven military autonomous devices operating within the boundaries set by applicable rules of International Humanitarian Law (IHL) related to targeting. We discuss the necessary legal tests and variables, and introduce the structure of a hypothetical IHL-compliant targeting system.

This work presents elements for an alternative operationalization of monitoring and diagnosis of multi-agent systems, developed in the context of compliance checking. In contrast to traditional accounts of model-based diagnosis, and most proposals concerning non-compliance, our method does not consider any commitment towards the individual unit of agency. Identity is considered to be mostly an attribute to assign responsibility, and not as the only referent to a source of intentionality. The proposed method requires as input a set of prototypical agent-roles (...) known to be relevant for the domain, and an observation, i.e. evidence collected by a monitor agent. We elaborate on a concrete example concerning tax frauds in real-estate transactions. (shrink)

We understand regulatory policy problems against the backdrop of existing implementations of a regulatory framework. There are argument schemes for proposing a policy and for criticising a proposal, rooted in a shared understanding that there is an existing regulatory framework which is implemented in social structures in society, yet has problems. The problems with the existing implementations may be attributed either to those implementations or to the constraints imposed by the regulatory framework. In this paper we propose that calls for (...) change of regulatory policy, and case-based and statistical evidence produced in support of policy proposals, are based in model-based problem solving activities. This perspective suggests schemes for a good argument pro or con a policy proposal, while avoiding the problem of backing up claims and evidence on the policy level with a conjectural deep model of the policy domain. (shrink)

The Leibniz Center for Law is involved in the project Digitale Uitwisseling Ruimtelijke Plannen [DURP (http://www.vrom.nl/durp); digital exchange of spatial plans] which develops a XML-based digital exchange format for spatial regulations. Involvement in the DURP project offers new possibilities to study a legal area that hasn’t yet been studied to the extent it deserves in the field of Computer Science & Law. We studied and criticised the work of the DURP project and the Dutch Ministry of internal affairs on metadata (...) for regulatory documents, and made an inventory of issues related to legal knowledge representation that it felt were not sufficiently covered by current initiatives in the Geographic Information Systems (GIS) field. This inventory was an input to the DURP standardisation effort. In a second phase of the project we extended the METALex XML schema (cf. Boer et al. 2002; Boer et al. 2003) for ‚regular’ legal sources that we developed in the past for geospatial regulatory information, in order to support exchange of spatial regulations, including the associated geospatial information in the form of maps. We developed a prototype application and demonstrated how the spatial planning information in GML can be combined with XML with only minimal changes, using the Web Ontology Language (OWL). This paper describes our experiences. (shrink)