State-space Abstraction for Anytime Evaluation of Probabilistic Networks

TL;DR

This paper introduces an anytime evaluation method for probabilistic networks that adjusts state-space granularity to balance accuracy and computational efficiency, enabling real-time probabilistic reasoning.

Contribution

It proposes a novel anytime approach using state-space abstraction for approximate probabilistic network evaluation, enhancing real-time reasoning capabilities.

Findings

Smooth improvement in approximation quality with increased computation time

State-space abstraction effectively balances accuracy and efficiency

Method applicable to simple networks, promising for real-time systems

Abstract

One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an anytime procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.