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Adversarial Search Board games. Games 2 player zero-sum games Utility values at end of game – equal and opposite Games that are easy to represent Chess.

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Presentation on theme: "Adversarial Search Board games. Games 2 player zero-sum games Utility values at end of game – equal and opposite Games that are easy to represent Chess."— Presentation transcript:

1 Adversarial Search Board games

2 Games 2 player zero-sum games Utility values at end of game – equal and opposite Games that are easy to represent Chess – average branching factor 35 Games need to make decisions even when optimal decisions are infeasible in limited time

3 Evaluation fn for Tic-Tac-Toe if position p is win for MAX – E(p) = 100 If position p is win for MIN – E(p) = -100 If not win position for either – E(p) = open lines for MAX – open lines for MIN

4 Alpha - beta Backed up Lower bound is alpha value Backed up upper bound is beta value Alpha at max can never decrease Beta at MIN can never increase

5 pruning If beta at any MIN <= alpha of any of its MAX ancestors final backed up value = its beta value If alpha at any MAX >= beta at any of its MIN ancestors final backed up value = its alpha value

6 Updating alpha and beta Alpha at MAX node = current largest final backed up value of its successors Beta at MIN node = current smallest final backed up value of its successors

7 Search efficiency In time alpha-beta search proceeds to depth d, simple minimax just proceeds to depth d/2 Search reduces effective branching factor from b to √b

8 Good heuristics Examine best moves first – Capture piece – take care of threats – Move forward – Move backward Use Iterative deepening – Evaluate best moves for one ply – Evaluate best moves for 2 ply… – Abort search if time constraint enforced

9 More efficiency Take care of repeated states, resulting from different permutations Use Transposition table Read 6.6 for state of art news

10 State of the art IBM’s Deep Blue defeated grandmaster Gary Kasparov in 1997, in a 6 game match 30 processors with 480 custom VLSI chess processors Average search speed – 126 million nodes per second Evaluation function had 8000 features Database of 700,000 grandmaster games were used

11 Power? IBM contributed the success to hardware Developers maintained that search extensions and evaluation functions more critical Deep blue team declined a chance for a rematch with Kasparov.

12 Chess on PCs 2002 FRITZ program on a PC against Vladimir Karamnik. The 8 game match ended in a draw


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