Artificial Intelligence: A Modern Approach

1. Introduction: In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
2. Intelligent Agents: In which we discuss the nature of agents, perfect or otherwise, the diversity of environments, and the resulting menagerie of agent types.

3. Solving Problems By Searching: In which we see how an agent can find a sequence of actions that achieves its goals when no single action will do.
4. Beyond Classical Search: In which we relax the simplifying assumptions of the previous chapter, thereby getting closer to the real world.
5. Adversarial Search: In which we examine the problems that arise when we try to plan ahead in a world where other agents are planning against us.
6. Constraint Satisfaction Problems: In which we see how treating states as more than just little black boxes leads to the invention of a range of powerful new search methods and a deeper understanding of problem structure and complexity.

7. Logical Agents: In which we design agents that can form representations of a complex world, use a process of inference to derive new representations about the world, and use these new representations to deduce what to do.
8. First Order Logic: In which we notice that the world is blessed with many objects, some of which are related to other objects, and in which we endeavor to reason about them.
9. Inference In First Order Logic: In which we define effective procedures for answering questions posed in firstorder logic.
10. Classical Planning: In which we see how an agent can take advantage of the structure of a problem to construct complex plans of action.
11. Planning and Acting in the Real World: In which we see how more expressive representations and more interactive agent architectures lead to planners that are useful in the real world.
12. Knowledge Representation: In which we show how to use first-order logic to represent the most important aspects of the real world, such as action, space, time, thoughts, and shopping.

13. Quantifying Uncertainity: In which we see how an agent can tame uncertainty with degrees of belief.
14. Probabilistic Reasoning: In which we explain how to build network models to reason under uncertainty according to the laws of probability theory.
15. Probabilistic Reasoning Over Time: In which we try to interpret the present, understand the past, and perhaps predict the future, even when very little is crystal clear.
16. Making Simple Decisions: In which we see how an agent should make decisions so that it gets what it wants on average, at least.
17. Making Complex Decisions: In which we examine methods for deciding what to do today, given that we may decide again tomorrow.

18. Learning From Examples: In which we describe agents that can improve their behavior through diligent study of their own experiences.
19. Knowledge In Learning: In which we examine the problem of learning when you know something already.
20. Learning Probabilistic Models: In which we view learning as a form of uncertain reasoning from observations.
21. Reinforcement Learning: In which we examine how an agent can learn from success and failure, from reward and punishment.

22. Natural Language Processing: In which we see how to make use of the copious knowledge that is expressed in natural language.
23. Natural Language For Communication: In which we see how humans communicate with one another in natural language, and how computer agents might join in the conversation.
24. Perception: In which we connect the computer to the raw, unwashed world.
25. Robotics: In which agents are endowed with physical effectors with which to do mischief.

26. Philosophical Foundations: In which we consider what it means to think and whether artifacts could and should ever do so.
27. AI The Present and Future: In which we take stock of where we are and where we are going, this being a good thing to do before continuing.

Artificial Intelligence