Basic ideas and techniques underlying the design of intelligent computer systems. Topics include heuristic search, problem solving, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, expert systems, learning, perception, robotics, language understanding.
Prerequisites: CS 61A or CS 61B and consent of instructor; 70 or Mathematics 55
Course objectives: An introduction to the full range of topics studied in artificial intelligence, with emphasis on the "core competences" of intelligent systems - problem solving, reasoning, decision making, and learning - and on the logical and probabilistic foundations of these activities.
inference in first-order logic
resolution, logic programming
planning, plan execution
uncertainty, probability theory, probabilistic inference
Bayesian networks and associated inference algorithms
optimal decisions under uncertainty
optimal sequential decisions, Markov decision processes
inductive learning, decision trees
natural language processing