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电脑功能越来越厉害 却就是不能像人一样智能 难道只是时间问题 人与机械 人脑与电脑 未来在哪里 遥远有多远 这个可能是收山之作 |
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| 1. Introduction |
|
|---|---|
| What is AI? | |
| Acting humanly: The Turing Test approach | |
| Acting rationally: The rational agent approach | |
| The Foundations of Artificial Intelligence | |
| Philosophy | |
| Mathematics | |
| Economics | |
| Psychology | |
| Computer engineering | |
| Linguistics | |
| The History of Artificial Intelligence | |
| The birth of artificial intelligence | |
| Early enthusiasm, great expectations | |
| AI becomes an industry | |
| The return of neural networks | |
| AI becomes a science | |
| 2. Intelligent Agents |
|
| Agents and Environments | |
| Good Behavior: The Concept of Rationality | |
| Performance measures | |
| Rationality | |
| The Nature of Environments | |
| Specifying the task environment | |
| Properties of task environments | |
| The Structure of Agents | |
| Agent programs | |
| Goal-based agents | |
| Problem-Solving Agents | |
| Well-defined problems and solutions | |
| Formulating problems | |
| Example Problems | |
| Toy problems | |
| Real-world problems | |
| 3. Adversarial Search |
|
| Games | |
| Optimal Decisions in Games | |
| Optimal strategies | |
| The minimax algorithm | |
| Optimal decisions in multiplayer games | |
| Position evaluation in games with chance nodes | |
| Card games | |
| 4. Planning |
|
| The Planning Problem | |
| The language of planning problems | |
| Expressiveness and extensions | |
| Planning with State-Space Search | |
| Forward state-space search | |
| Backward state-space search | |
| Partial-Order Planning | |
| A partial-order planning example | |
| Partial-order planning with unbound variables | |
| Heuristics for partial-order planning | |
| 5. Probabilistic Reasoning over Time |
|
| Time and Uncertainty | |
| States and observations | |
| Stationary processes and the Markov assumption | |
| Inference in Temporal Models | |
| Filtering and prediction | |
| Smoothing | |
| Finding the most likely sequence | |
| Learning with Complete Data | |
| Bayesian parameter learning | |
| Learning Bayes net structures | |
| 6. Making Decisions |
|
| Utility Functions | |
| The utility of money | |
| Utility scales and utility assessment | |
| Multiattribute Utility Functions | |
| Dominance | |
| Preference structure and multiattribute utility | |
| The Value of Information | |
| Properties of the value of information | |
| Implementing an information-gathering agent | |
| Sequential Decision Problems | |
| An example | |
| Optimality in sequential decision problems | |
| 7. Learning from Observations |
|
| Forms of Learning | |
| Learning Decision Trees | |
| Decision trees as performance elements | |
| Expressiveness of decision trees | |
| Inducing decision trees from examples | |
| 8. Reinforcement Learning |
|
| Passive Reinforcement Learning | |
| Direct utility estimation | |
| Adaptive dynamic programming | |
| Active Reinforcement Learning | |
| Exploration | |
| Learning an Action-Value Function | |
| Generalization in Reinforcement Learning | |
| Applications to game-playing | |
| Application to robot control | |
| 9. Robotics |
|
| Introduction | |
| Robot Hardware | |
| Sensors | |
| Effectors | |
| Robotic Perception | |
| Localization | |
| Mapping | |
| Other types of perception | |
| Planning uncertain movements | |
| Robust methods | |
| Application Domains | |
| 10. Philosophical Foundations |
|
| Weak AI: Can Machines Act Intelligently? | |
| The argument from disability | |
| The mathematical objection | |
| The argument from informality | |
| Strong AI: Can Machines Really Think? | |
| The mind-body problem | |
| The brain prosthesis experiment | |
| The Ethics and Risks of Developing | |
| 11. AI: Present and Future |
|
| Are We Going in the Right Direction? | |
| What if AI Does Succeed? |
|
