The field of multi-agent systems (MAS) addresses theories, tools and techniques for constructing systems of "software agents" that interact to achieve individual and/or collective design goals. Generally the agents are assumed to be independent and autonomous, and to exist in an open and dynamic system. To facilitate orderly and efficient functioning of such systems, researchers have adopted concepts from human society, such as social norms and trust, and developed computational counterparts. In this talk I will discuss the problem of learning norms based on observation of the interactions of other agents. This is useful when the membership of a multi-agent system is dynamic and there are no pre-specified norms, or when the norms are implicitly given by human participation in the system.
This work takes a Bayesian statistical approach to combine two sources of evidence for and against candidate norms: the observation of sanctioning actions, and reasoning about observed agents' goals and the plans they had available to generate their actions. An evaluation using simulations shows that the approach allows agents to generate norm-compliant behaviour between 70% and 99% of the time, without prior knowledge of the norms.
This is joint work with Felipe Meneguzzi, Nir Oren and Tony Savarimuthu.
Last modified: Wednesday, 20-Jul-2016 17:42:05 NZST
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