'Optimal goal' presentation slideshows

Solving Problems: Intelligent Search

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CS 2710, ISSP 2160

CS 2710, ISSP 2160. Chapter 3, Part 2 Heuristic Search. Heuristic Search. Take advantage of information about the problem. Best-First-Search. An evaluation function f determines order of nodes on the fringe (there are variations, depending on the search algorithm). Best-First-Search.

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Reading Material

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Uninformed (also called blind) search algorithms)

Uninformed (also called blind) search algorithms). This Lecture Chapter 3.1-3.4 Next Lecture Chapter 3.5-3.7 (Please read lecture topic material before and after each lecture on that topic). Outline. Overview of uninformed search methods Search strategy evaluation

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Uninformed (also called blind) search algorithms)

Uninformed (also called blind) search algorithms). This Lecture Chapter 3.1-3.4 Next Lecture Chapter 3.5-3.7 (Please read lecture topic material before and after each lecture on that topic). Outline. Overview of uninformed search methods Search strategy evaluation

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Informed search algorithms

Informed search algorithms. Chapter 4. Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms. Best-first search.

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Informed search algorithms

Informed search algorithms. Chapter 4 Thanks: Prof Dan Weld, Univ of Washington, Seattle USA. Material. Chapter 4 Section 1 - 3 Exclude memory-bounded heuristic search . Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms

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Informed search algorithms

Informed search algorithms. Chapter 4. Material. Chapter 4 Section 1 - Exclude memory-bounded heuristic search. Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search

613 views • 41 slides

Informed search algorithms

Informed search algorithms. Chapter 4. Material. Chapter 4 Section 1 - Exclude memory-bounded heuristic search. Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search

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Informed search algorithms

Informed search algorithms. Chapter 4. Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms. Review: Tree search.

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Lecture 3

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CS 2710, ISSP 2610

CS 2710, ISSP 2610. Chapter 4, Part 1 Heuristic Search. Heuristic Search. Take advantage of information about the problem. Best-First-Search. More general use of the term than in the 1st edition of the text

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CSC344: AI for Games Lecture 4: Informed search

CSC344: AI for Games Lecture 4: Informed search. Patrick Olivier p.l.olivier@ncl.ac.uk. Depth-first. Iterative deepening. Breadth-first. Uninformed search: summary. Informed search strategies. Best-first search Greedy best-first search A* search Heuristics Local search algorithms

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Heuristic search

Heuristic search. Reading: AIMA 2 nd ed., Ch. 4.1-4.3. (Blind) Tree search so far. function TreeSearch ( problem , fringe ) n = InitialState ( problem ); fringe = Insert ( n ); while (1) If Empty ( fringe ) return failure; n = RemoveFront ( fringe );

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Informed Search II

Informed Search II. CIS 391 Fall 2008. Outline. PART I Informed = use problem-specific knowledge Best-first search and its variants A * - Optimal Search using Knowledge Proof of Optimality of A * A * for maneuvering AI agents in games Heuristic functions? How to invent them PART II

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Lecture 3

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Lecture 6: 25/4/1435 Informed search strategies

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CSC3203: AI for Games Informed search (1)

CSC3203: AI for Games Informed search (1). Patrick Olivier p.l.olivier@ncl.ac.uk. Depth-first. Iterative deepening. Breadth-first. Uninformed search: summary. Informed search strategies. Best-first search Greedy best-first search A* search Local beam search Simulated annealing search

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Ch 3.5 and 3.6

Informed Search Algorithms Use problem-specific knowledge beyond the definition of the problem itself. Ch 3.5 and 3.6. Outline. Heuristic Search Methods Best-first search algorithms Greedy best-first search A * search Dominance Relaxed Problems. Heuristic Search Methods.

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Admissible heuristics

Admissible heuristics. A heuristic h(n) is admissible if for every node n , h(n) ? h * (n), where h * (n) is the true cost to reach the goal state from n . An admissible heuristic never overestimates the cost to reach the goal, i.e., it is optimistic

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