mychapter4 - Solving problems by searching 1 CHAPTER 3 CMPT...

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CHAPTER 3 CMPT 310 - Blind Search 1 Solving problems by searching
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Outline CMPT 310 - Blind Search 2 Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms
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Environment Type Discussed In this Lecture Static Environment CMPT 310 - Blind Search 3 Fully  Observable Deterministic Sequential yes yes Discrete  Discrete  yes Planning,  heuristic  search yes Control,  cybernetics no no Continuous Function  Optimization Trivial given  performance  measure no yes
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Choice in a Deterministic Known Environment Without uncertainty, choice is trivial in principle:  choose what you know to be the best option. Trivial if the problem is represented in a look-up  table. CMPT 310 - Blind Search 4 Option Value Chocolate 10 Wine 20 Book 15 This is the standard problem representation in decision theory  (economics).
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Computational Choice Under Certainty But choice can be  computationally  hard if the  problem information is represented differently. Options may be  structured  and the best option  needs to be constructed. E.g., an option may consist of a path, sequence of actions, plan,  or strategy. The value of options may be given  implicitly  rather  than explicitly. E.g., cost of paths need to be computed from map. CMPT 310 - Blind Search 5
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Sequential Action Example CMPT 310 - Blind Search 6 Deterministic, fully observable     single-state problem Agent knows exactly which state it will be in; solution is a  sequence Romania   The full map is observed Single-state:  Start in #5. Solution?? [Right, Suck] Vacuum world   everything observed  
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Problem types CMPT 310 - Blind Search 7 Non-observable    sensorless problem (conformant  problem) Agent may have no idea where it is; solution is a sequence Romania   No map just know operators(cities you can move to) Conformant:   Start in {1, 2, 3, 4, 5, 6, 7, 8} e.g., Right goes to {2, 4, 6, 8}. Solution?? [Right, Suck,Left, Suck] Vacuum world   No sensors
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CMPT 310 - Blind Search 8 Nondeterministic and/or partially observable     contingency problem percepts provide  new  information about current state Unknown state space     exploration problem Vacuum world   know state of current location Romania   know current location and neighbor cities Contingency: [L,clean]  Start in #5 or #7 Murphy’s Law: Suck can dirty a clean carpet Local sensing: dirt, location only.
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This note was uploaded on 07/04/2011 for the course CMPT 310 taught by Professor Oliver during the Summer '11 term at Simon Fraser.

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mychapter4 - Solving problems by searching 1 CHAPTER 3 CMPT...

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