Students are introduced to artificial intelligence theory and practice underlying expert systems. Topics include knowledge bases; inference engines; knowledge representation formalisms; knowledge acquisition; search and reasoning techniques; and other practical issues in the development of expert systems. For logic-based approaches, students explore rule-based systems, semantic networks, frames, and mixed representation formalisms. For uncertainty management, certainty factors, Bayesian network, D-S belief functions, and fuzzy logic are discussed. Prerequisite: C or better in COMP 3710
Students are introduced to artificial intelligence theory and practice underlying expert systems. Topics include knowledge bases; inference engines; knowledge representation formalisms; knowledge acquisition; search and reasoning techniques; and other practical issues in the development of expert systems. For logic-based approaches, students explore rule-based systems, semantic networks, frames, and mixed representation formalisms. For uncertainty management, certainty factors, Bayesian network, D-S belief functions, and fuzzy logic are discussed. Prerequisite: C or better in COMP 3710
