Lecture
This lecture explores deliberative agents that construct complex plans to achieve goals, reviewing basic AI planning techniques. It covers planning with adversaries, dealing with uncertainty, and the use of heuristic search algorithms like A*. The lecture delves into state-based planning, depth-first search, iterative deepening, and Monte-Carlo Tree Search. It also discusses game tree search, Alpha-Beta pruning, and strategies for games with chance, including counterfactual regret minimization. The instructor presents practical applications in computer poker and highlights the significance of deliberative agents in generating and optimizing strategies for various scenarios.