This paper describes extensions to a corpus annotation scheme for the manual annotation of attributions, as well as opinions, emotions, sentiments, speculations, evaluations and other private states in language. It di...
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Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful application of the approach depends on...
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ISBN:
(纸本)1577352009
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful application of the approach depends on the choice of an appropriate set of feature functions defining the value function, and efficient methods for generating constraints that determine the convex space of the solution. The application of the ALP in continuous state-space settings poses an additional challenge - the number of constraints defining the problem is infinite. The objective of this work is to explore various heuristics for selecting a finite subset of constraints defining a good solution policy and for searching the space of such constraints more efficiently. The heuristics that we developed rely upon: (1) the structure of the factored model and (2) stochastic state simulations to generate an appropriate set of constraints. The improvements resulting from such heuristics are illustrated on three large factored MDP problems with continuous states.
Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods cannot adequately address these proble...
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This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework BLCD ...
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This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD...
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The Hopfield and bi-directional associative memory (TJAM) models are well developed and carefully studied models tor associative memory that are patterned after the memory structure of the animal brain. Their basic li...
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