Anytime anyspace probabilistic inference |
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Institution: | Universidade de São Paulo, Escola Politécnica, Cidade Universitária, Av. Prof. Mello Moraes 2231, 05508-900 São Paulo, SP, Brazil |
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Abstract: | This paper investigates methods that balance time and space constraints against the quality of Bayesian network inferences––we explore the three-dimensional spectrum of “time × space × quality” trade-offs. The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination of exact and anytime strategies. The algorithm seeks a balanced synthesis of probabilistic techniques for bounded systems. Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices. |
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