Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

Stefano V. Albrecht, Subramanian Ramamoorthy

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Journal track. Pages 5085-5089. https://doi.org/10.24963/ijcai.2017/727

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity.
Keywords:
Knowledge Representation, Reasoning, and Logic: Belief Change
Planning and Scheduling: POMDPs
Uncertainty in AI: Approximate Probabilistic Inference
Uncertainty in AI: Bayesian Networks