Abstract
Sapient agents have been characterized as systems that learn their cognitive state and capabilities through experience, considering social environments and interactions with other agents or humans. The BDI (belief, desire, intention) model of cognitive agency offers philosophical grounds on intentionality and practical reasoning, as well as an elegant abstract logical semantics. However, the lack of learning and social competences of this model constitutes a serious limitation when sapient agents are the issue. This chapter discusses some ideas on intentional and social learning, that support the revision of practical reasons by the BDI agents. The resulting agents can learn, and then update, their plans’ contexts to avoid forming intentions that eventually fail. Individual and social learning have been successfully attained in our own BDI interpreter based on dMARS, and ported to the Jason interpreter based on AgentSpeak(L). Multiagent consistency is guaranteed through a protocol based on cooperative goal adoption. These BDI learning agents seems closer to the intended characterization of sapient agents.
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