2012/07/04 by Vibhav Gogate, Gogate, Vibhav, Rina Dechter +9
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Transportation Planning and Optimization #cs.AI
paper · pdf · doi:10.48550/arxiv.1207.1384
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)
arxiv created 2012/07/04 · openalex publication_date 2012/07/04 · arxiv updated 2012/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filtering and Constraint Propagation to address the complexity of modeling and reasoning in HDMNs. We use this framework to model a person's travel activity over time and to predict destination and routes given the current location. We present a preliminary empirical evaluation demonstrating the effectiveness of our modeling framework and algorithms using several variants of the activity model.