A model for reasoning about persistence and causation
Computational Intelligence
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
Temporally Invariant Junction Tree for Inference in Dynamic Bayesian Network
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Forecasting market prices in a supply chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamic multiagent probabilistic inference
International Journal of Approximate Reasoning
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Comparison of tightly and loosely coupled decision paradigms in multiagent expedition
International Journal of Approximate Reasoning
The multi-agent system for prediction of financial time series
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Time series are found widely in engineering and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time series. We model the domain as a dynamic multiply sectioned Bayesian network (DMSBN) and populate the domain by a set of proprietary, cooperative agents. We propose an algorithm suite that allows the agents to perform one-step forecasts with distributed probabilistic inference. We show that as long as the DMSBN is structural time-invariant (possibly parametric time-variant), the forecast is exact and its time complexity is exponentially more efficient than using dynamic Bayesian networks (DBNs). In comparison with independent DBN-based agents, multiagent DMSBNs produce more accurate forecasts. The effectiveness of the framework is demonstrated through experiments on a supply chain testbed.