Artificial Intelligence Techniques in Prolog - download pdf or read online

By Yoav Shoham

ISBN-10: 1483214494

ISBN-13: 9781483214498

Man made Intelligence thoughts in Prolog

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Fail. as in the previous implementation: bfs( _, [ [ Node | Path ] | _ ], _, _, GoalPred, [Node | Path ] ) :call( GoalPred, Node ). bfs( Arc, [ [ Node | Path ] | MoreOPEN ], Qtail, CLOSED, GoalPred, Sol ) :findall( [ Next, Node | Path ], ( call( Arc, Node, Next ), % dlmember/3 determines % (see Chapter membership in a difference list 1) not( dlmember( [ Next | _ ], [ [ Node | Path ] | MoreOPEN ], Qtail ) ), not( member( Next, CLOSED ) ) ), NewPaths ), % and here is where the difference list pays off: append( NewPaths, NewQtail, Qtail ), bfs( Arc, MoreOPEN, NewQtail, [ Node | CLOSED ], GoalPred, Sol ).

Again, this is true for example whenever the heuristic function is based on only the last node in the p a t h . Let us call an algorithm t h a t has this property strongly admissible. Strongly admissible algorithms need not even examine the O P E N list, and we end up with the following further simplified program. 7. 37 Best-first search % % Best-first search assuming 'strong' admissibility % best_first_search_st_ad( Arc, Start, Hfun, GoalPred, Sol ) :bstfs_st_ad( Arc, [ 0 - [ Start ] ] , [ ] , Hfun, GoalPred, Sol ).

L = no meta(abs(-3,L)). 3 ; meta(meta(meta(abs(-3,L)))). - 3-3 A modified depth-first meta-interpreter So far we have merely reproduced the behavior of the Prolog interpreter in the meta-interpreter. We now begin to reap the fruits of our efforts, by modifying the design of the interpreter. 1 and will not consider special Prolog features such as all-solutions operations or !. We start with 3 I n addition, in Prologs that distinguish between dynamic and static predicates, the predicates abs / 2, meta / 1, meta_cut / 2 and system / 1 must be declared dynamic.

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Artificial Intelligence Techniques in Prolog by Yoav Shoham


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