Background
Substantial progress has been made in uncertain inference using Bayesian networks (BNs) [Pearl 88].
- Dependencies of domain variables are represented by a DAG.
- Strength of dependencies is quantified by an associated jpd.
- The jpd is interpreted as the degree of belief of an agent.
- Many effective inference algorithms have been developed.
A single-agent paradigm is commonly assumed:
- A single processor accesses a single global BN, updates the jpd as evidence becomes available, and answers queries.
This research advances the DTMS approach with a representation of agent’s degree of belief consistent with the probability theory, and advances the single-agent BN approach with a multiagent paradigm and distributed inference algorithm.