maximise over next set of decisions - in order for an agent to behave optimally in an environment. Artificial Intelligence : A Modern Approach Home Part - I Artificial Intelligence Chapter 1 - Introduction Chapter 2 - Intelligent Agents Part - II Problem Solving Chapter 3 - Solving Problems By Searching Chapter 4 - Beyond Classical Search Chapter 5 - Adversarial Search Chapter 6 - Constraint Satisfaction Problems Part - III Knowledge, Reasoning and Planning Chapter 7 - Logical Agents Chapter 8 - First Order Logic Chapter 9 - Inference in First Order Logic Chapter 10 - Classical Planning Chapter 11 - Planning and Acting in Real Life Chapter 12 - Knowledge Representation Part - IV Uncertaing Knowledge and Reasoning Chapter 13 - Quantifying Uncertainty Chapter 14 - Probabilistic Reasoning Chapter 15 - Probabilistic Reasoning Over Time Chapter 16 - Making-Simple Decisions Chapter 17 - Making Complex Decisions Part - V Lerning Chapter 18 - Learning From Examples Chapter 19 - Knowledge In Learning Chapter 20 - Learning Probabilistic Models Chapter 21 - Reinforcement Learning Part - VI Communicating, Perceiving and Acting Chapter 22 - Natural Language Processing Chapter 23 - Natural Language For Communication Chapter 24 - Perception Chapter 25 - Robotics Part - VII Conclusions Chapter 26 - Philosophical Foundations Chapter 27 - AI The Present And Future Currently v1.0. A perfect evaluation function would mean that you only had to do a local search - i.e.
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