Artificial Intelligence by Ian Pratt (auth.)

By Ian Pratt (auth.)

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1, this policy can have its drawbacks. Indeed , for most practical planning purposes, treating the goals as an indivisible block would lead to unmanageably large search spaces, and the alternative of working on separate goals independently is unavoidable. The difficulty associated with combining interacting plans is largely what makes planning Search andPlanning 29 an interesting and non-trivial topic in AI. Before we investigate this matter in more detail, however, we need to understand one further issue concerning search procedures.

9. 1? 10. 7, using minimax search with alpha-beta pruning . You will need to devise a board evaluation function to calculate utilities. One possibility (no doubt not the best) is simply to give both players one point for every square they have moved towards the opposite end of the board (summed over all pieces) and then to subtract black's score from white's. In addition, of course, board positions in which one player has won will have to be assigned huge (positive or negative) utilities . Once you have decided how to represent a board state and compute its utility, you will need to write a program to generate, for any given board state, all the states that can be reached from it in one move.

Programming) Implement the A* search algorithm, and use it as the basis of a planning program for Bob. 7. 1, it is possible to construct a backward-chaining planning program for Bob. Remember, the nodes of the tree represent collections of goals, not situations. Write a program which, when given a set of goals G), ... G n for Bob, finds all actions a and sets of goals Gi, . G~ I such that, if Gi, .. G~ I can be achieved, performing a will achieve G 1 , .. • Gn • Then use breadth-first search (this works much better than depth-first) to construct a backwardchaining planner.

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