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Multi-agent confidential abductive reasoning. (English) Zbl 1245.68234
Gallagher, John P. (ed.) et al., Technical communications of the 27th international conference on logic programming (ICLP 2011), Lexington, Kentucky, USA, July 6–10, 2011. Wadern: Schloss Dagstuhl – Leibniz Zentrum für Informatik (ISBN 978-3-939897-31-6). LIPIcs – Leibniz International Proceedings in Informatics 11, 175-186, electronic only (2011).
Summary: In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. Most collaborative reasoning systems operate on the assumption that agents can share or communicate any information they have. However, in application domains like multi-agent systems for healthcare or distributed software agents for security policies in coalition networks, confidentiality of knowledge is an additional primary concern. These agents are required to collaborately compute consistent answers for a query whilst preserving their own private information. This paper addresses this issue showing how this dichotomy between “open communication” in collaborative reasoning and protection of confidentiality can be accommodated. We present a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. A case study on security policy analysis in distributed coalition networks is described, as an example of many applications of this system.
For the entire collection see [Zbl 1237.68017].
68T42 Agent technology and artificial intelligence
68T37 Reasoning under uncertainty in the context of artificial intelligence
68N17 Logic programming
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