Application of Model-Based Diagnosis to Multi-Agent Systems Representing Public Administration

Authors
Publication date 2012
Host editors
  • M. Palmirani
  • U. Pagallo
  • P. Casanovas
  • G. Sartor
Book title AI Approaches to the Complexity of Legal Systems : Models and Ethical Challenges for Legal Systems, Legal Language and Legal Ontologies, Argumentation and Software Agents
Book subtitle international workshop AICOL-III, held as part of the 25th IVR Congress Frankfurt am Main, Germany, August 15-16, 2011: revised selected papers
ISBN
  • 9783642357305
ISBN (electronic)
  • 9783642357312
Series Lecture Notes in Computer Science
Event International workshop AICOL-III
Pages (from-to) 235-244
Publisher Heidelberg: Springer
Organisations
  • Faculty of Law (FdR) - Leibniz Center for Law (FdR)
Abstract
In public administration, legal knowledge in the form of critical incidents and, for want of a better word, noncompliance storylines is important for monitoring and enforcement, but has no natural place in traditional forms of legal knowledge representation such as normative rules or legal argument schemes. In this paper we present a model-based diagnosis view on the complex social systems in which large public administration organizations operate. The purpose of diagnosis as presented in this paper is to identify problematic agent role instances in a multi-agent system (MAS). We propose the model-based diagnosis problem as an explanation of the driving forces behind requests for change to the IT, business process design, and policy making departments in public administration. This makes model-based diagnosis an important potential legal knowledge acquisition frame for public administration.
Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-642-35731-2_16
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