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dc.contributor.authorTan, Terence K.
dc.contributor.authorDarken, Christian J.
dc.contributor.otherMOVES Institute
dc.date2012
dc.date.accessioned2013-09-25T22:58:01Z
dc.date.available2013-09-25T22:58:01Z
dc.date.issued2012
dc.identifier.citationProceedings of the 15th International Conference on Information Fusion 2012, IEEE, pp. 189-195.
dc.identifier.urihttp://hdl.handle.net/10945/36616
dc.description.abstractTasks at upper levels of sensor fusion are usually concerned with situation or impact assessment, which might consist of predictions of future events. Very often, the identity and relations of target of interest have already been established, and can be represented as relational data. Hence we can expect a stream of relational data arriving at our agent input as the situation updates. The prediction task can then be expressed as a function of this stream of relational data. Run-time learning to predict a stresm of percepts in an unknown and possibly complex environment is a hard problem and especially so when a serious atttempt needs to be made even on the first few percepts. When the percepts are relational (logical atoms), the most common practica technologies require engineering by a human expert and so are not applicable. We briefly describe and compare several approaches which do not have this requirement on the initial hundred precepts of a benchmark domain. The most promising approach extends existing approaches by a partial matching algorithm inspired by theory of conceptual blending. This technique enables predictions in novel situations where the original approach fails, and significantly improves prediction performance overall. However an implementation, based on backtracking, may be too slow for many implementations. We provide an accelerated approximate algorithm based on best-first and A* search, which is much faster than the initial implementation.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleFaster conceptual blending predictors on relational time seriesen_US
dc.contributor.corporateNaval Postgraduate School, Monterey, California
dc.contributor.departmentComputer Science (CS)
dc.subject.authorpattern analysisen_US
dc.subject.authormachine learningen_US
dc.subject.authorreasoning under uncertaintyen_US
dc.subject.authorrelational time seriesen_US
dc.subject.authorlearningen_US
dc.subject.authorpredictionen_US


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