SP10 cs188 lecture 6 -- adversarial search (2PP)

SP10 cs188 lecture 6 -- adversarial search (2PP) - CS 188:...

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
1 CS 188: Artificial Intelligence Spring 2010 Lecture 6: Adversarial Search 2/4/2010 Pieter Abbeel – UC Berkeley Many slides adapted from Dan Klein 1 Announcements s Project 1 is due tonight s Written 2 is going out tonight, due next Thursday 2
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 Today s Finish up Search and CSPs s Intermezzo on A* and heuristics s Start on Adversarial Search 3 CSPs: our status s So far: s CSPs are a special kind of search problem: s States defined by values of a fixed set of variables s Goal test defined by constraints on variable values s Backtracking = depth-first search with incremental constraint checks s Ordering: variable and value choice heuristics help significantly s Filtering: forward checking, arc consistency prevent assignments that guarantee later failure s Today: s Structure: Disconnected and tree-structured CSPs are efficient s Iterative improvement: min-conflicts is usually effective in practice 4
Background image of page 2
3 Example: Map-Coloring s Variables: s Domain: s Constraints: adjacent regions must have different colors s Solutions are assignments satisfying all constraints, e.g.: 6 Constraint Graphs s Binary CSP: each constraint relates (at most) two variables s Binary constraint graph: nodes are variables, arcs show constraints s General-purpose CSP algorithms use the graph structure to speed up search. E.g., Tasmania is an independent subproblem! 7
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
4 Tree-Structured CSPs s Theorem: if the constraint graph has no loops, the CSP can be solved in O(n d 2 ) time s Compare to general CSPs, where worst-case time is O(d n ) s This property also applies to probabilistic reasoning (later): an important example of the relation between syntactic restrictions and the complexity of reasoning. 8 Tree-Structured CSPs s Choose a variable as root, order variables from root to leaves such that every node’s parent precedes it in the ordering s For i = n : 2, apply RemoveInconsistent(Parent(X i ),X i ) s For i = 1 : n, assign X i consistently with Parent(X i ) s Runtime: O(n d 2 ) (why?) 9
Background image of page 4
5 Tree-Structured CSPs s Why does this work? s
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 15

SP10 cs188 lecture 6 -- adversarial search (2PP) - CS 188:...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online