3_AkilliSP_ProblemModelVeKorArama

# 3_AkilliSP_ProblemModelVeKorArama - Course 3 Solving...

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1 Course 3: Solving Problems By Searching Outline ± Problem-solving agents ± Problem types ± Problem formulation ± Example problems ± Basic search algorithms Problem-solving agents ± In this course, ² We start by defining the elements that constitute a problem. ² Problem-solving agents (goal based) : decides what to do by finding sequences of actions that leads to desirable states. ² Solutions: general-several purpose search algorithms ± Algorithms are ² uninformed if they are given no information about the problem other than definition ² informed if they have some idea of where to look for solutions

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2 Problem-solving agents ± First step in problem solving: Goal formulation-based on the current situation and agent’s performance measures- ² Agent’s task is to find out which sequence of actions will get it to a goal state ± Second step: Problem formulation- what actions and states to consider. ± Third step: Search- finding sequence of actions ² A search algorithm takes a problem as input and return solutions ± Fourth step: Execution- of actions Problem-solving agents
3 Example: Romania- route finding Example: Romania ± On holiday in Romania; currently in Arad. ± Flight leaves tomorrow from Bucharest. ± Formulate goal : ² be in Bucharest. ± Formulate problem : ² states : various cities ² actions : drive between cities. ± Find solution : ² sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest.

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4 Problem-solving agents ± Before plunging into details- where problem-solving agents fit into discussion of agents and environments. ± Environment is static ± Initial state is known: observable ± The idea of enumerating “alternative course of actions”- discrete , ± Design assumes that the environment is deterministic ± Easiest kind of the environment Well-defined problems and solutions A problem is defined by four items: 1. initial state e.g., "at Arad" 2. actions or successor function S(x) = set of action–state pairs ² e.g., S(Arad) = { <Arad Æ Zerind, Zerind>, } 3. goal test , can be ² explicit , e.g., x = "at Bucharest" ² implicit , e.g., in chess: Checkmate(x 4. path cost (additive) ² e.g., sum of distances, number of actions executed, etc. ² c(x,a,y) is the step cost , assumed to be 0 ± A solution is a sequence of actions leading from the initial state to a goal state
5 Formulating Problems ± Real world is absurdly complex Æ state space must be abstracted for problem solving. ± (Abstract) state = set of real states. ± (Abstract) action = complex combination of real actions ² e.g., "Arad Æ Zerind" represents a complex set of possible routes, detours, rest stops, etc. ± For guaranteed realizability, any real state "in Arad“ must get to some real state "in Zerind“. ± (Abstract) solution = ² set of real paths that are solutions in the real world.

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3_AkilliSP_ProblemModelVeKorArama - Course 3 Solving...

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