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2009 Eighth Mexican International Conference on Artificial Intelligence
In this paper we present a Causal Artificial Intelligence Design (CAID) theory that borrows notions from Classical philosophy for modeling intelligent agents. Principles introduced by this theory are used for extending a goal-driven BDI architecture and implementing what we call Causal Agent. This architecture incorporates causal formalisms like Pearl's Do calculus and C+ which are adapted to Semantic Web knowledge representations. Our approach includes an ontological agent description thatdoi:10.1109/micai.2009.12 fatcat:nasyvlfhoffkrcqevyac7vogny