Towards a Causal Framework for Intelligent Agents Development
Héctor G. Ceballos, Francisco J. Cantú-Ortiz · 2009
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 that enables and justifies the instantiation of agents as part of a plan. An experimental prototype used for validating experimentally our approach is commented.