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1.
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3.
What Is an Agent?, Rational Agents, How Is an Agent Different from Other Software?, PEAS, Environment Types, Agent Types
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4.
Knowledge and Reasoning, Knowledge-based agents, Knowledge representation, Knowledge base, Wumpus World
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5.
Logic in General, Propositional Logic, Entailment, Inference, Proof Methods, Normal Clausal Form
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6.
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7.
Inference with Quantifiers, Reduction to propositional inference, Unification, Generalized Modus Ponens (GMP)
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8.
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9.
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10.
Uncertain Agent, Types of Uncertainty, How do we deal with uncertainty?, How do we represent uncertainty?, Probability, Prior probability, Conditional probability, Independence
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11.
Computing with Probabilities, Conditional Independence, Bayesian Networks, Examples of Simple Bayesian Networks, Inference (Reasoning) in Bayesian Networks
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13.
Learning Decision Trees, Attribute-based Representations, Finding Compact Decision Trees, Choosing an Attribute, Information Gain