Artificial Intelligence

What you’ll study

An overview of artificial intelligence and AI programming languages such as Prolog; environment and agent types, agent models and reactive agents. Problem solving and search — the 8-puzzle and N-queen problems; uninformed search (breadth-first, uniform-cost, depth-first, iterative deepening and bidirectional search) and informed search (best-first, A*, heuristic and memory-bounded search such as IDA*); local search — hill climbing, simulated annealing and constraint-satisfaction problems; genetic algorithms; and motion planning. Game theory — minimax search, resource limits and heuristic evaluation, alpha-beta pruning, stochastic and partially observable games. Neural networks — biological and artificial neurons, perceptron learning, linear separability, multi-layer networks, backpropagation, cross-entropy, weight decay and momentum. Machine learning — supervised and reinforcement learning. Knowledge representation and reasoning — propositional and first-order logic, equivalence, validity and satisfiability, inference rules and theorem proving, forward and backward chaining, resolution, and planning.