By Christopher Thornton

Synthetic INTELLIGENCE techniques, functions, and versions via seek moment version This leading edge new publication on man made intelligence (AI) makes use of the unifying thread of seek to compile the most important program and modeling options that use symbolic AI. all the 11 chapters is split into 3 sections: ** a bit which introduces the process ** a bit which develops a low-level (POP-11) implementation ** a bit which develops a high-level (Prolog) implementation entire but sensible, this e-book can be of significant worth to these skilled in AI, in addition to to scholars with a few programming history and teachers and execs searching for an actual dialogue of man-made intelligence via seek.

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Extra resources for Artificial Intelligence: Strategies, Applications, and Models Through Search, 2nd Edition

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Setting up the Path-Finding Function Now that we have set up a database and a function which lets us find out where we can get to from any given location we can concentrate on writing the code which will actually try out different paths. Imagine we are at some location S and we want to get to some desired location (which will be called the goal below). We can easily find out where we can get to in one hop from S (by calling the successor function) so a reasonable strategy would be to first check whether we are already at the goal location and, if not, test to see whether we can get to the goal starting from any one of the successors of S.

Such a control strategy is Page 6 <><><><><><><><><><><><> A procedural description in POP-11 To search for the path, given a partial path, that ends with a goal state: 1. If the partial path already ends with a goal state, exit with the partial path as the solution. 2. Otherwise, take in turn each extended path, which extends the partial path by one state, and recursively search to see if this extended path can be made to reach the goal; if so exit immediately with the resultant path as the solution.

Comparison of agenda-based search methods in Prolog 102 5-1. Successors function for nim 108 5-2. Search tree for nim 110 5-3. Computing the minimax value of a node 112 5-4. Generating a minimax search tree 115 5-5. Example of a minimax search tree 116 5-6. Alpha-beta pruning 118 5-7. Computing the alpha-beta minimax value for a node 119 5-8. Generating the alpha-beta minimax search tree 122 5-9. An example of an alpha-beta minimax tree 123 5-10. Program to play nim 124 5-11. Successor function for nim in Prolog 126 5-12.

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