Chaotic time series prediction for the game, Rock-Paper-Scissors
| Autor: | Paolo Patelli, Franco Salvetti, Simone Nicolo |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Computer Science::Computer Science and Game Theory
Exploit business.industry ComputingMilieux_PERSONALCOMPUTING Chaotic Lyapunov exponent Chaos theory symbols.namesake symbols Entropy (information theory) Reinforcement learning Artificial intelligence Time series business Algorithm Game theory Software Mathematics |
| Zdroj: | Applied Soft Computing. 7:1188-1196 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2006.01.006 |
| Popis: | Two players of Rock-Paper-Scissors are modeled as adaptive agents which use a reinforcement learning algorithm and exhibit chaotic behavior in terms of trajectories of probability in mixed strategies space. This paper demonstrates that an external super-agent can exploit the behavior of the other players to predict favorable moments to play against one of the other players the symbol suggested by a sub-optimal strategy. This third agent does not affect the learning process of the other two players, whose only goal is to beat each other. The choice of the best moment to play is based on a threshold associated with the Local Lyapunov Exponent or the Entropy, each computed by using the time series of symbols played by one of the other players. A method for automatically adapting such a threshold is presented and evaluated. The results show that these techniques can be used effectively by a super-agent in a game involving adaptive agents that exhibit collective chaotic behavior. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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