Lecture note Artificial Intelligence

Lecture note Artificial Intelligence - Chapter 1: Introduce presents the following content: What is AI? A brief history, the state of the art, thinking humanly: Cognitive Science,...

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Lecture note Artificial Intelligence - Chapter 2: Intelligent agents include all of the following: Agents interact with environments through actuators and sensors, the agent function describes what the agent does in all circumstances, the performance measure evaluates the environment sequence, a perfectly rational agent maximizes expected performance, agent programs implement (some) agent functions, PEAS descriptions define task environments Environments are categorized along several dimensions.

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The main contents of "Lecture note Artificial Intelligence - Chapter 3: Problem solving and search" include all of the following: Problem-solving agents, problem types, problem formulation, example problems, basic search algorithms, informed search algorithms.

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Chapter 4a: Informed search algorithms. The main contents of this chapter include all of the following: Heuristic functions estimate costs of shortest paths, good heuristics can dramatically reduce search cost, greedy best-first search expands lowest h – incomplete and not always optimal, admissible heuristics can be derived from exact solution of relaxed problems.

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The main contents of "Lecture note Artificial Intelligence - Chapter 4b: Local search algorithms" include all of the following: Hill-climbing, simulated annealing, genetic algorithms (briefly), local search in continuous spaces (very briefly).

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Chapter 5: Constraint Satisfaction Problems. The main contents of this chapter include all of the following: CSP examples, backtracking search for CSPs, problem structure and problem decomposition, local search for CSPs.

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Chapter 6: Game playing. The main contents of this chapter include all of the following: Games, Perfect play, Resource limits and approximate evaluation, Games of chance, Games of imperfect information.

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Chapter 7: Logical agents. The main contents of this chapter include all of the following: Knowledge-based agents, wumpus world, logic in general—models and entailment, propositional (boolean) logic; equivalence, validity, satisfiability; inference rules and theorem proving.

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Chapter 9: Inference in first-order logic. The main contents of this chapter include all of the following: Reducing first-order inference to propositional inference, unification, generalized modus ponens, forward and backward chaining, logic programming, resolution.

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Chapter 13: Uncertainty. The main contents of this chapter include all of the following: Probability is a rigorous formalism for uncertain knowledge; joint probability distribution specifies probability of every atomic event; queries can be answered by summing over atomic events; for nontrivial domains; we must find a way to reduce the joint size; independence and conditional independence provide the tools.

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Chapter 16: Rational decisions. The main contents of this chapter include all of the following: Rational preferences, utilities, money, multiattribute utilities, decision networks, value of information.

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Chapter 20a: Statistical learning. The main contents of this chapter include all of the following: Bayesian learning, maximum a posteriori and maximum likelihood learning, bayes net learning.

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Chapter 22: Communication and Language. The main contents of this chapter include all of the following: Recall the principles of effective communication, identify the responsibilities of women and health care providers in the consultation process, discuss and list the principles of informed consent.

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Chapter 8: First-order logic. The main contents of this chapter include all of the following: First-order logic: objects and relations are semantic primitives; syntax: constants, functions, predicates, equality, quantifiers; Increased expressive power: sufficient to define wumpus world; Situation calculus: conventions for describing actions and change in FOL; situation calculus: can formulate planning as inference on a situation calculus KB.

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Chapter 14b: Inference in Bayesian networks. The main contents of this chapter include all of the following: Exact inference by enumeration, exact inference by variable elimination, approximate inference by stochastic simulation, approximate inference by Markov chain Monte Carlo.

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