Abstract
This paper reports the first results of a simulation experiment. There are two databases, one containing true propositions and the other containing their respective negations. Five agents in turn access one of them. Each agent has a “capacity” that will be used as the frequency with which the agent accesses (unconsciously) the database with the correct knowledge. Agents randomly exchange information with the others. Since they have limited degrees of capacity, their “cognitive state” quickly becomes inconsistent. Each agent is equipped with the same belief revision mechanism to detect and solve these contradictions. This adopts the Dempster's Rule of Combination to evaluate the credibility of the various pieces of information and Bayesian Conditioning to estimate the relative degrees of reliability of the agents (itself included). The purpose of the experiments was that of evaluating on a statistical basis, the emergent cognitive behavior of the group.
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Dragoni, A.F., Giorgini, P. (1997). Learning agents' reliability through Bayesian Conditioning: A simulation experiment. In: Weiß, G. (eds) Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. LDAIS LIOME 1996 1996. Lecture Notes in Computer Science, vol 1221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62934-3_47
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DOI: https://doi.org/10.1007/3-540-62934-3_47
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