Constructing shared interpretations in a team of intelligent agents: the effects of communication intensity and structure
In this paper we explore a model of a team of intelligent agents constructing a shared interpretation of the state of their environment. Each agent is modeled as a constraint satisfaction network of the Hopfield (1982) type.
We show that in a noisy environment communication intensity often has a non-monotonic effect on team interpretive accuracy. We also investigate how team communication can correct erroneous individual interpretations stored in agents’ memories — errors concerning what they know, not only what they perceive.
We also compare the effects of different communication structures, and show that communication structure matters only when agents are cognitively heterogeneous (each has a different repertoire of interpretations in his memory), while it has only a minor impact on team performance when agents are homogeneous.
KeywordsTeam Neural Nets Hopfield Nets Reliability Groupthink
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