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Midterm Exam Review for ECE 4524: Artificial Intelligence - Prof. Amos L. Abbott, Study notes of Electrical and Electronics Engineering

An overview of the major topics covered in the midterm exam for the ece 4524 artificial intelligence course. The topics include ai fundamentals, python programming, agents, search algorithms, constraint satisfaction problems, and games. The document also covers various search algorithms, constraint satisfaction benefits, and game theory concepts.

Typology: Study notes

Pre 2010

Uploaded on 12/14/2008

argentine
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Download Midterm Exam Review for ECE 4524: Artificial Intelligence - Prof. Amos L. Abbott and more Study notes Electrical and Electronics Engineering in PDF only on Docsity! Review for Midterm Exam ECE 4524 Major course topics (tentative) 2 Topic Location in R&N Intro Chapter 1 Agents Chapter 2 Uninformed search Chapter 3 Search with heuristics Chapter 4 Constraint satisfaction Chapter 5 Games Chapter 6 Propositional logic Chapter 7 Predicate logic Chapter 8 Logic and inference Chapter 9 Knowledge representation Chapter 10 Planning Chapter 11 Dealing with uncertainty Chapter 13 Probabilistic reasoning Chapter 14 Utility theory Chapter 16 Learning from observations Chapter 18 Statistical learning & neural nets Chapter 20 Natural language processing Chapter 22 Computer vision Chapter 24 Agents 5 Agents and environments Rational agents Task environments (PEAS) Fully observable, partially observable Deterministic, stochastic Episodic, sequential Static, dynamic Discrete, continuous Single-agent, multiagent Agent structure Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents Search 6 Informed search vs. uninformed search Local search vs. non-local search On-line vs. off-line search Search 7  Uninformed (blind) search  Breadth-first search  Uniform-cost search  Depth-first search  Depth-limited search  Iterative deepening search  Informed (heuristic) search [f(n) = g(n)+h(n)]  Greedy best-first search  A* search  Admissible heuristics,  Evaluation function (or cost, or distance, . . .)  Local search and optimization  Hill-climbing  Gradient descent  Simulated annealing  Local beam search  Genetic algorithms Games / adversarial search 10 Multi-agent environments Cooperating agents Adversarial agents  Games Static evaluation function (SEF) Minimax search Alpha-beta pruning Dealing with resource limitations Horizon effects Quiescence Feedover Secondary search Type A, Type B systems > Could extend to situations with chance, imperfect information deterministic chance perfect information chess, checkers, backgammon go, othello monopoly imperfect information bridge, poker, scrabble nuclear war Logic 12 Logical inference Deductive Inductive Predicate calculus Propositional logic First-order logic General concepts Models and entailment Soundness Completeness Inference rules and theorem proving Modus tolens, modus polens, resolution Proof by refutation Forward/backward chaining Knowledge-based agents Equivalence, validity, satisfiability Wumpus world
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