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dc.contributor.authorSmith, Tony C.
dc.contributor.authorvan de Molen, Chris
dc.coverage.spatialConference held at Adelaide, Australiaen_NZ
dc.identifier.citationSmith, T.C. & van de Molen, C. (2011). Unbounded knowledge acquisition based upon mutual information in dependent questions. In J. Li (Ed.): AI 2010: Advances in Artificial Intelligence, Proceedings of the 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010 (pp. 233-242). Springer-Verlag Berlin Heidelberg.en_NZ
dc.description.abstractThis paper describes an experimental system for knowledge acquisition based on a general framework exemplified in the game of twenty questions. A sequence of propositional questions is put to the user in an attempt to uncover some hidden concept, and the answers are used to expand and refine the system’s knowledge of the world. Previous systems adopting this framework typically represent knowledge as a matrix of truth values or weights that relate entities to attributes—such that if the hidden concept is “a bird”, for example, then the answer to a question about whether the target entity can fly is based on the extent to which “flying” is generally attributable to “a bird” as measured by the value in the matrix element indexed by the attribute-entity pair. Our system adopts a subtly different approach wherein knowledge is a measure of the extent to which answers to pairs of questions are co-dependent. Thus, knowledge about birds being able to fly is captured by the mutual information in the answers to a pair of questions like “Can it fly?” and “Is it a bird?”. We present a case that this offers a practical and epistemologically sound basis for acquiring knowledge.en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectknowledge acquisitionen_NZ
dc.subjectmutual informationen_NZ
dc.subjecttwenty questionen_NZ
dc.subjectMachine learning
dc.titleUnbounded knowledge acquisition based upon mutual information in dependent questionsen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOf23rd Australasian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLNAI 6464, Lecture Notes in Artificial Intelligenceen_NZ

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