Name
Abstract | ||
|---|---|---|
In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it receives. Fuzzeval employs an evaluation function that adjusts the membership functions linked to the linguistic variables employed in the knowledge base. The membership functions are aligned to the average crisp input that was successfully used in the past winning games. Fuzzeval mechanisms are adaptive and have the simplicity associated with fuzzy controllers. Our experiments show that Fuzzeval improves its performance up to 42% after a match of only one hundred backgammon games played against Pubeval, a strong intermediate level program. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11579427_23 | MICAI |
Keywords | Field | DocType |
linguistic variable,adaptive learning,average crisp input,fuzzy controller,fuzzy controller-based approach,knowledge base,membership function,evaluation function,hundred backgammon game,fuzzeval mechanism,strong intermediate level program,strong computer player program,reinforcement learning,machine learning,artificial neural network | Control theory,Computer science,Fuzzy logic,Evaluation function,Artificial intelligence,Knowledge base,Fuzzy control system,Artificial neural network,Adaptive learning,Machine learning,Reinforcement learning | Conference |
Volume | ISSN | ISBN |
3789 | 0302-9743 | 3-540-29896-7 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikael Heinze | 1 | 0 | 0.34 |
| Daniel Ortiz-Arroyo | 2 | 39 | 6.36 |
| Henrik Legind Larsen | 3 | 545 | 45.16 |
| Francisco Rodriguez-Henriquez | 4 | 33 | 4.20 |