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Published byEmely Cowens Modified over 3 years ago

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Big Ideas in Cmput366

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Search Blind Search State space representation Iterative deepening Heuristic Search A*, f(n)=g(n)+h(n), admissible heuristics Local and Stochastic Search Randomized algorithm Constraint satisfaction CSP representation Constraint propagation, arc consistency Games and Adversarial Search Minimax Alpha-Beta

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Logic Propositional and first order Semantics: model, logic entailment question: KB |= A ? Inference: Inference rules, soundness, completeness, forward-chaining, resolution, unification, DPLL, unit propagation. Knowledge representation: SAT encoding, FOL encoding

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FOL may not be suitable Common sense reasoning - default, non-monotonic reasoning - closed work assumption - beliefs may be conflicting, changing

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Answer Set Programming Variant of SAT but non-monotonic Closed world assumption by negation by failure Stable models capture reasoning by rules rather than clauses General programming methodology: Generate-and-constraint Cardinality constraints make generate easier to express

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Planning STRIPS language: state, goal, action, action effects, frame axioms Search: forward, backward, partial-order Planning in ASP - fix the number of steps - representation of action system

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Probability Uncertainty and Probability Joint probability distribution Cond. Probability/Product rule Inference - summing out/hidden vars - Bayes rule (cause/effect by effect/cause) - Chain rule (joint prob by a chain of cond. prob; independence reduces size)

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Bayesian Network Representation of independence; a node only directly influenced by its parent nodes, not others O(n · 2 k ) numbers where k is the max number of parents Exact computation based on semantics: P (X 1, …,X n ) = π i =1 P (X i | X 1, …, X i-1 ) (chain rule) = π i =1 P (X i | Parents(X i )) (by construction)

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