On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Calculating Dempster-Shafer Plausibility
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reasoning about knowledge and probability
Journal of the ACM (JACM)
Multiagent systems
The consensus operator for combining beliefs
Artificial Intelligence
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Deliberation Process in a BDI Model with Bayesian Networks
Agent Computing and Multi-Agent Systems
Reasoning about risk in agent's deliberation process: a Jadex implementation
AOSE'07 Proceedings of the 8th international conference on Agent-oriented software engineering VIII
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Existing cognitive agent programming languages that are based on the BDI model employ logical representation and reasoning for implementing the beliefs of agents. In these programming languages, the beliefs are assumed to be certain, i.e. an implemented agent can believe a proposition or not. These programming languages fail to capture the underlying uncertainty of the agent's beliefs which is essential for many real world agent applications. We introduce Dempster-Shafer theory as a convenient method to model uncertainty in agent's beliefs. We show that the computational complexity of Dempster's Rule of Combination can be controlled. In particular, the certainty value of a proposition can be deduced in linear time from the beliefs of agents, without having to calculate the combination of Dempster-Shafer mass functions.