《人工智能》课程教学课件 ch13 不确定性.pptVIP

第十三章、不确定性 不确定环境下的行动 不确定性推理的方法 概率公理 使用全概率分布进行推理 独立性 贝叶斯法则及其应用 Uncertainty Let action At = leave for airport t minutes before flight Will At get me there on time? Problems: partial observability (road state, other drivers plans, etc.) noisy sensors (traffic reports) uncertainty in action outcomes (flat tire, etc.) immense complexity of modeling and predicting traffic Hence a purely logical approach either risks falsehood: “A25 will get me there on time”, or leads to conclusions that are too weak for decision making: “A25 will get me there on time if theres no accident on the bridge and it doesnt rain and my tires remain intact etc etc.” (A1440 might reasonably be said to get me there on time but Id have to stay overnight in the airport …) 关于不确定性的推理 必要性：处理智能体的精度和有效性的折中 理性决策的含义 假设A90是智能体该做的事，这意味着在给定所拥有的环境信息下，它是所有可以被执行的规划中被期望能够使智能体的性能度量达到最大的那个。 第十三章、不确定性 不确定环境下的行动 不确定性推理的方法 概率 概率公理 使用全概率分布进行推理 独立性 贝叶斯法则及其应用 Methods for handling uncertainty Default or nonmonotonic logic (Section 10.7) Assume my car does not have a flat tire Assume A25 works unless contradicted by evidence Issues: What assumptions are reasonable? How to handle contradiction? Rules with fudge factors A25 |→0.3 get there on time Sprinkler |→ 0.99 WetGrass WetGrass |→ 0.7 Rain Issues: Problems with combination, e.g., Sprinkler causes Rain?? Dempster-Shafer theory Use interval-valued degrees of belief to represent an agent’s knowledge of the probability of a proposition Ex: coin flipping, the interval for Head is [0.1] before expert testimony and [0.45,0.55] after. Fuzzy logic: an event can be “sort of”—vagueness, different from uncertainty. Methods for handling uncertainty (contd) Probability Model agents degree of belief Ex: Given the available evidence, A25 will get me there on time with probability 0.04 Probability Probabilistic assertions summarize effects of laziness: failure to enumerate exceptions, qualifications, etc. ignorance: lack of relevant facts, initial conditions, etc.