Authors:
Fadel Adoe
;
Yingke Chen
and
Prashant Doshi
Affiliation:
University of Georgia, United States
Keyword(s):
GPU, Multiagent Systems, Planning, Speed Up.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Formal Methods
;
Group Decision Making
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
;
Task Planning and Execution
;
Uncertainty in AI
Abstract:
Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond parallelizing the standard Bayesian inference, the computation of decisions' expected utilities are parallelized. The GPU-based approach provides significant speedup on two benchmark problems.