A platform for massive agent-based simulation and its evaluation
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
This article presents a class of approximation algorithms that extend the idea of bounded-complexity inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algorithms. This yields a parameterized scheme, called mini-buckets, that offers adjustable trade-off between accuracy and efficiency. The mini-bucket approach to optimization problems, such as finding the most probable explanation (MPE) in Bayesian networks, generates both an approximate solution and bounds on the solution quality. We present empirical results demonstrating successful performance of the proposed approximation scheme for the MPE task, both on randomly generated problems and on realistic domains such as medical diagnosis and probabilistic decoding.
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Benjamin N. Grosof
AAAI-SS 1993