Lecture 3 - Problem Solving by Searching

Lecture 3 - Problem Solving by Searching - MITI 5113...

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MITI 5113 Slid e 1 /57 MITI 5113 ARTIFICIAL INTELLIGENCE LECTURE 3 LECTURE 3 Searching Problems Searching Problems
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MITI 5113 Slid e 2 /57 Why Search? To achieve goals or to maximize our utility we need to predict what the result of our actions in the future will be. There are many sequences of actions, each with their own utility. We want to find, or search for, the best one.
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MITI 5113 Slid e 3 /57 Examples of problems Route planning routing in computer networks, military operations planning, airline travel planning Touring problems Travelling salesman problem Robot Navigation Automatic assembly sequencing Internet searching
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MITI 5113 Slid e 4 /57 Canonical Problem Formulation State: What the world is doing at this time? State Space: A collection of possible states Initial State: Where the search starts Goal State: Where the search ends Path: A sequence of operators leading from one state to another Path Cost: Sum of the costs of operators along the path
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MITI 5113 Slid e 5 /57 Example 1: Route Planning Find Route From Here to There State = Current location on a map Initial State = Starting City, say City A Goal State = Destination City, say City Z Operators: Move along a road to another city Path Costs = Sum of lengths from here to there Solution = Path from here to there Issues What is the Cost of Finding the Route? What is the Cost of Traversing the Route?
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MITI 5113 Slid e 6 /57 Example 2: Vacuum World state space graph States: Integer dirt and robot locations (ignore dirt amounts) Actions: Left, Right, Suck, NoOp Goal test: No dirt Path cost: 1 per action (0 for NoOp )
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MITI 5113 Slid e 7 /57 states? actions? goal test? path cost? Example 2: Vacuum World state space graph
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MITI 5113 Slid e 8 /57 Example 2: Vacuum World state space graph states? integer dirt and robot location actions? Left , Right , Suck goal test? no dirt at all locations path cost? 1 per action
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MITI 5113 Slide 9/57 Example 3: Timetable Find Lecture Timetable by Incrementally Modifying a Draft to Eliminate Conflicts State: A version of a time table Initial state: A draft version of a timetable Goal State: A timetable with no conflicts Operators: exchange a pair of assigned time slots Costs: Time taken to make the exchange and verify conflicts Solution: A timetable with no time conflicts (Here the path is irrelevant)
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MITI 5113 Slide 10/57 Example 4: Romanian holiday search problem You are on holiday in Romania – in a town called Arad It is 1am in the morning and you have a car, but no map, no-one to ask advice, and you have just realised that your flight leaves from Bucharest Airport in the afternoon.
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  • Winter '14
  • STUDENT
  • Depth-first search, Search algorithms, Search algorithm, Graph algorithms, MITI

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