Learning and Prediction in Relational Time Series: a survey
Tan, Terence C.
Darken, Christian J.
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Making sense out of a stream of incoming cercepts is the first step in any agent's cognition process. The purpose of sense-making is usually to facilitate dound decision making, often by making predictions of future events or actions. in the case that the percepts are relational, the technologies available for this task are mainly based on production systems or statistical graphical model inferencing processes such as Bayesian networks. To apply these approaches, it is necessary that domain knowledge be known or that examples are available to a supervised learning process. Darkan (2005) prpposed a situation learning (SL) approah to learn a string of perceptsequence into a set of overlapping situaitons. Th9is approach has much potential for learning and predicting in domains that are characterized by high variability and great number of predicates and terms that become known only at runtime, and which feature a trending or moving context environment. In this paper, we attempt to deefine relational time series (RTS) and in characteristics for evaluating current learning approaches for learning and prediction of RTS. We alsoreport the prediction accuracies of various prediction techniques based on SL in a benchmark environment.
Proceedings of 21st Behavior Representation in Modeling and Simulation (BRIMS) 2012, pp. 93-100, 12-BIRMS-016, Amelia Island, FL.
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.
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