Knowledge-Based Search in Competitive Domains

1. Knowledge-Based Search in Competitive Domains 作者：Steven Walczak 出處：IEEE Transactions on Knowledge and Data Engineering, May/June 2003 報告者：梁秦宜

2. outline • Introduction • The IAM Pattern Acquisition and Application Methodology • Analysis of IAM Pattern-Based Tree Search Reductions ．Search Size and Speed ．Better Search Paths • Extending IAM to Other Domains

3. Introduction • at a search depth over 14 ply today, computer chess programs face the horizon effect in chess • Bratko and Michie have demonstrated that the simple endgame which is solvable by most average strength chess players has a solution that may be 52 ply deep in a search tree • preventing the chess program from discovering a guaranteed win using brute-force search due to the horizon effect • Due to the lack of any strategic knowledge in chess playing computer systems that rely on the brute-force search strategy, chess grandmasters and masters typically try to play unusual moves to place the computer into unfamiliar territory

4. Introduction • One group believes that the depth of the search is the most important criterion affecting the playing ability of game programs • The second group focuses on increasing performance through the use of domain knowledge • patterns are limited to specific portions • storage and usage of patterns requires large amounts of space that slows down the program’s playing speed

5. Introduction • Walczak introduced the idea of using pattern-based, tree-search reductions based on cognitive processing versus domain-specific knowledge with the Inductive Adversary Modeler (IAM) program

6. The IAM Pattern Acquisition and Application Methodology • Acquisition of cognitive patterns is based on the empirical evidence and theory provided by Chase and Simon, who claim that chess experts acquire domain relevant chunks • reducing the complexity of the current situation • provides a better evaluation of the current game situation • indicate that human chess experts may acquire from 10,000 to 100,000 chunks during their progress towards expertise

7. The IAM Pattern Acquisition and Application Methodology • IAM is quasi-domain-independent method • Learns cognitive chunks for individual opponents by examining the records of previous games played by the opponent • The perceptual patterns are then used to automatically prune search trees based on the chunks of pieces displayed by chess-like game players during the course of a game

8. The IAM Pattern Acquisition and Application Methodology • Limitation of IAM is that it only acquires contiguous same-colored pieces • Once a piece is identified as having another similarly colored contiguous piece, the operation is performed recursively on all of the contiguous pieces to form larger patterns • All identified chunks are placed into a temporary database of probable cognitive chunks

9. The IAM Pattern Acquisition and Application Methodology • If the same chunk occurs at least once in each game, the chunk is removed from the temporary knowledge base and placed into a permanent knowledge base of cognitive chunks for the specific opponent being studied • Knowledge concerning the time of the game, the color of the pieces making up the pattern, the eventual win/loss result of the game, and chunk frequency

10. The IAM Pattern Acquisition and Application Methodology

11. The IAM Pattern Acquisition and Application Methodology • no partial patterns currently exist on the game board, in which case no move prediction is made • the more frequently a pattern has been associated with an opponent’s moves in the past, the more likely it is to be used by the opponent in future games • the larger the pattern, the greater the cognitive economy • Increases the move prediction by IAM up to an average of 25 percent of all opponents’ moves

12. Search Size and Speed • This concern arises in part from the large size of the knowledge bases • hardware has been used to speed up database accesses as well as to generate new positions for the game tree search • patterns associated with the current opponent need to be loaded • the amount of relevant data that needs to be loaded is much smaller than the space required for standard opening books

13. Better Search Paths • For adversarial domains, the use of domain knowledge can be dangerous by leading us into a false sense of security • Samuel warns that programs using a mini-max algorithm must take into consideration the intent of the adversary • Traditional domain knowledge in evaluation algorithms may produce incorrect game tree analysis is given by examining some personal computer chess programs

14. Better Search Paths

15. Better Search Paths • Use of these IAM cognitive patterns for Kasparov would have alerted Deep Thought to give a much stronger evaluation to the castle move that was eventually made by Kasparov • various pawn chains, bishop with pawn patterns, and rook with rook or queen patterns

16. Extending IAM to Other Domains