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Logic Programming

Logic Programming. Lecture 7: Search Strategies. Outline for today. Problem representation Depth-First Search Iterative Deepening Breadth-First Search Advanced techniques taster Best-first search AND/OR (alternating) search. Search spaces. Set of states s 1 , s 2 ,...

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Logic Programming

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  1. Logic Programming Lecture 7: Search Strategies

  2. Outline for today Problem representation Depth-First Search Iterative Deepening Breadth-First Search Advanced techniques taster Best-first search AND/OR (alternating) search

  3. Search spaces Set of states s1,s2,... Goal predicate goal(X) Step predicate s(X,Y) that says we can go from state X to state Y A solution is a path leading from some start stateS to a goal state g satisfying goal(G).

  4. Example: Blocks world C B A [[c,b,a],[],[]]

  5. Example: Blocks world B A C [[b,a],[],[c]]

  6. Example: Blocks world A B C [[a],[b],[c]]

  7. Example: Blocks world A B C [[],[a,b],[c]]

  8. Representation in Prolog State is a list of stacks of blocks. [[a,b,c],[],[]] Transitions move a block from the top of one stack to the top of another s([[A|As],Bs,Cs], [As,[A|Bs],Cs]). s([[A|As],Bs,Cs], [As,Bs,[A|Cs]]). ... goal([[],[],[a,b,c]]).

  9. An abstract problem space s(a,b). s(b,c). s(c,a). s(c,f(d)). s(f(N), f(g(N))). s(a,d). goal(d). a d b c ...

  10. Depth-first search dfs(Node,Path) Path is a path to a goal starting from Node dfs(S,[S]) :- goal(S). dfs(S,[S|P]) :- s(S,T), dfs(T,P).

  11. Problem 1: Cycles C B A

  12. Solution 1: Remember where you've been Avoid cycles by avoiding previously visited states dfs_noloop(Path,Node,[Node|Path]) :- goal(Node). dfs_noloop(Path,Node,Path1) :- s(Node,Node1), \+(member(Node,Path)), dfs_noloop([Node|Path],Node1,Path1).

  13. Problem 2: Infinite state space We may miss solutions because state space is infinite Even if state space is finite, may wind up finding "easy" solution only after a long exploration of pointless part of search space ...

  14. Solution 2: Depth bounding Keep track of depth, stop if bound exceeded dfs_bound(_,Node,[Node]) :- goal(Node). dfs_bound(N,Node,[Node|Path]) :- N > 0, s(Node,Node1), M is N-1, dfs_bound(M,Node1,Path).

  15. Problem 3: What is a good bound? Don't know this in advance, in general Too low? Might miss solutions Too high? Might spend a long time searching pointlessly

  16. Solution 3: Iterative deepening dfs_id(N,Node,Path) :- dfs_bound(N,Node,Path) ; M is N+1, dfs_id(M,Node,Path).

  17. Breadth-first search Keep track of all possible solutions, try shortest ones first Maintain a "queue" of solutions bfs([[Node|Path],_], [Node|Path]) :- goal(N). bfs([Path|Paths], S) :- extend(Path,NewPaths), append(Paths,NewPaths,Paths1), bfs(Paths1,S). bfs_start(N,P) :- bfs([[N]],P).

  18. Extending paths extend([Node|Path],NewPaths) :- bagof([NewNode,Node|Path], (s(Node,NewNode), \+ (member(NewNode,[Node|Path]))), NewPaths), !. %% if there are no next steps, %% bagof will fail and we'll fall through. extend(_Path,[]).

  19. Problem: Speed Concatenating new paths to end of list is slow Avoid this using difference lists?

  20. A difference list implementation bfs_dl([[Node|Path]|_],_, [Node|Path]) :- goal(Node). bfs_dl([Path|Paths], Z, Solution) :- extend(Path,NewPaths), append(NewPaths,Z1,Z), Paths \== Z1, %% (Paths,Z1) is not empty DL bfs_dl(Paths,Z1,Solution). bfs_dl_start(N,P) :- bfs_dl([[N]|X],X,P). We'll come back to this next week

  21. Best-first search Often want a minimum cost solution and maybe a heuristic cost measure Idea: Maintain "frontier" of solutions with similar cost Expand least-cost unexplored node

  22. Heuristic h(x) = map distance between points Example: Shortest paths

  23. Heuristic h(x) = map distance between points Example: Shortest paths

  24. Heuristic h(x) = map distance between points Example: Shortest paths

  25. AND/OR search So far we've considered graph search problems Just want to find some path from start to end Other problems have more structure e.g. 2-player games AND/OR search is a useful abstraction

  26. AND/OR search Search space has 2 kinds of states: OR: "we get to choose next state" AND: "opponent gets to choose" we need to be able to handle any opponent move

  27. Example: Tic tac toe ... x x x x ... ... ... ...

  28. Representation or(S,Nodes) S is an Or node with possible next states Nodes "We move" and(S,Nodes) S is an And node with possible next states Nodes "Opponent moves" goal(S) S is a "win" for us

  29. Example: A simple game a and(a,[b,c]). or(b,[d,a]). or(c,[d,e]). goal(e). b c d e

  30. Basic idea andor(Node) :- goal(Node). andor(Node) :- or(Node,Nodes), member(Node1,Nodes), andor(Node1). andor(Node) :- and(Node,Nodes), solveall(Nodes).

  31. Solutions For each AND state, we need solutions for all possible next states For each OR state, we just need one choice A "solution" is thus a tree, or strategy Can adapt previous program to produce solution tree. Can also incorporate iterative deepening, loop avoidance, heuristic measures of "good" positions

  32. Next time Further reading: Bratko, ch. 8 (difference lists), ch. 11 (DFS/BFS) also Ch. 12 (BestFS), 13 (AND/OR) Next time: Advanced programming with terms (var/1, nonvar/1, =../2, ==/2, ...) Meta-programming

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