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Cooperative agents often need to reason about the states of a large and complex uncertain domain that evolves over time. Since exact calculation is usually impractical, we aim at providing a modeling tool that supports approximate online monitoring in such settings. Our proposed framework, the multi-agent dynamic Bayesian networks (MA-DBNs), models the dynamics of a group of cooperative agents approximately...
Extensive research has been done for efficient computation of probabilistic queries posed to Bayesian networks (BNs). One popular architecture for exact inference on BNs is the Junction Tree (JT) based architecture. Among all variations developed, HUGIN is the most efficient JT-based architecture. The Global Propagation (GP) method used in the HUGIN architecture is arguably one of the best methods...
An increasing number of applications require cooperative agents to reason about the state of an distributed uncertainty domain. However, inference process of such system could become overly slow for practical applications, and there has been significant interest in developing faster approximation techniques. In this paper, we focus on the existing MSBN models for cooperative reasoning in multi-agent...
In this paper, we propose an improved architecture that supports exact MSBN belief updating using iterative message passing. Compared with recursive inference algorithms, iterative message passing is more robust in a multiagent environment where agents often have to face unreliable communication channels. Our method improves the overall time efficiency of existing iterative methods by avoiding repeated...
The multiply sectioned Bayesian network (MSBN) model successfully extends the traditional Bayesian network (BN) model for the support of probabilistic inference in distributed multi-agent systems. However, existing MSBN inference methods do not allow agents to reason about their own problem sub-domains right after the initialization process. Extensive amount of inter-agent message passings are needed...
Most of the current applications which use dynamic Bayesian network to model gene regulatory network assume that the time delay between regulators and their targets is one time unit in a time series gene expression dataset. In fact, multiple time units delay is indicated to exist in a gene regulation process. In this paper, we propose using higher-order Markov dynamic Bayesian network (DBN) to model...
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