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.
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Boer, A., van Engers, T. (2012). Application of Model-Based Diagnosis to Multi-Agent Systems Representing Public Administration. In: Palmirani, M., Pagallo, U., Casanovas, P., Sartor, G. (eds) AI Approaches to the Complexity of Legal Systems. Models and Ethical Challenges for Legal Systems, Legal Language and Legal Ontologies, Argumentation and Software Agents. AICOL 2011. Lecture Notes in Computer Science(), vol 7639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35731-2_16
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