背景 基础 Markov Processes Markov Reward Process Markov Decision Process Extensions to MDPs (Full Observable) Markov Decision Processes (MDPs) Partial Observable MDPs (POMDPs)

Uncertainty Probability Syntax and Semantics Inference Independence and Bayes’ Rule Bayesian network Graphical models Bayesian networks Inference in Bayesian networks

中国科学技术大学：《人工智能基础》课程教学资源（课件讲稿）Lecture 07 Logical Agents

Games Perfect play（最优策略） minimax decisions α − β Pruning Resource limits and approximate evaluation Games of chance (包含几率因素的游戏) Games of imperfect information

CSP examples Backtracking search for CSPs Problem structure and problem decomposition Local search for CSPs

Best-first Search (最佳优先搜索) Greedy search A* search Local Search Algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms

Problem-solving Agents Searching for Solutions Uninformed Search Strategies

Agents and Environments PEAS (Performance measure, Environment, Actuators, Sensors) Environment Types Agent Types

• Reducing first-order inference to propositional inference • Unification • Generalized Modus Ponens • Forward chaining • Backward chaining • Resolution

• Why FOL? • Syntax and semantics of FOL • Using FOL • Wumpus world in FOL • Knowledge engineering in FOL