A model-based approach to the automatic revision of secondary legislation

Authors:
Tingting Li;Tina Balke;Marina De Vos;Julian Padget;Ken Satoh
Affiliations:
University of Bath, Bath, UK;University of Surrey, Surrey, UK;University of Bath, Bath, UK;University of Bath, Bath, UK;National Institute of Informatics, Tokyo, Japan
Venue:
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
Year:
2013

Citing 6
Cited 0

Normative conflict resolution in multi-agent systems

Autonomous Agents and Multi-Agent Systems
Conflict-driven answer set solving

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Answer set programming for representing and reasoning about virtual institutions

CLIMA VII'06 Proceedings of the 7th international conference on Computational logic in multi-agent systems
An algorithm for conflict resolution in regulated compound activities

ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
Potassco: The Potsdam Answer Set Solving Collection

AI Communications - Answer Set Programming
Legal modelling and reasoning using institutions

JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conflicts between laws can readily arise in situations governed by different laws, a case in point being when the context of an inferior law (or set of regulations) is altered through revision of a superior law. Being able to detect these conflicts automatically and resolve them, for example by proposing revisions to one of the modelled laws or policies, would be highly beneficial for legislators, legal departments of organizations or anybody having to incorporate legal requirements into their own procedures. In this paper we present a model based approach for detecting and finding legal conflicts through a combination of a formal model of legal specifications and a computational model based on answer set programming and inductive logic programming. Given specific scenarios (descriptions of courses of action), our model-based approach can automatically detect whether these scenarios could lead to contradictory outcomes in the different legal specifications. Using these conflicts as use cases, we apply inductive logic programming (ILP) to learn revisions to the legal component that is the source of the conflict. We illustrate our approach using a case-study where a university has to change its studentship programme after the government brings in new immigration regulations.