Lecture05 - CS440/ECE448: Intro to Articial Intelligence!...

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Lecture 5: Constraint satisfaction problems Prof. Julia Hockenmaier juliahmr@illinois.edu http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence
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Thursday ʼ s key concepts Heuristic search: Actions and solutions have costs Heuristic function: estimate of future cost Uniform cost, best-Frst, A* Local search: Agent only sees the next steps. ±eatures of the state space landscape Hill-climbing, random restart, beam, simulated annealing
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Constraint satisfaction problems
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Today ʼ s topics/questions Different kinds of CSPs: - Binary vs. global constraints - Small Fnite vs. large/continuous domains How do constraints interact? - The structure of CSPs Underdetermined CSPs (multiple solutions): - Interleaving search and CSP inference 4 CS440/ECE448: Intro AI
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CSP 1: Map coloring (Binary constraints)
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Map coloring Task: Color each region of the map with one of N colors. Constraints: No neighboring regions have the same color 6 CS440/ECE448: Intro AI
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Map coloring 7 CS440/ECE448: Intro AI
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Map coloring: a solution for N=3 8 CS440/ECE448: Intro AI
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Map coloring is defned by… - the regions on the map: {WA, NT, QLD, NSW, VA, SA, TA} - the colors : {red, blue, green} - the neighbor constraints : {WA NT, WA SA, NT QLD, NT SA, QLD NSW, QLD SA, NSW VA, NSW SA, VA SA} 9 CS440/ECE448: Intro AI
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Map coloring as search The state space is defned by: a set oF variables (the regions): {WA, NT, QLD, NSW, VA, SA, TA} the domain oF each variable (its set oF possible values): D WA = {red, blue, green} D NT = {red, blue, green} 10 CS440/ECE448: Intro AI
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Map coloring as search Each state is a complete or partial assignment of values to variables: state35 = {WA=red, NT=blue, QLD= green, NSW= red, VA= green, SA= blue, TA= red};
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Lecture05 - CS440/ECE448: Intro to Articial Intelligence!...

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