<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Adversarial decision making is aimed at determining strategies to anticipate the behavior of an opponent trying to learn from our actions. One defense is to make decisions intended to confuse the opponent, although our rewards can be diminished. This idea has already been captured in an adversarial model introduced in a previous work, in which two agents separately issue responses to an unknown sequence of external inputs. Each agent’s reward depends on the current input and the responses of both agents. In this contribution, (a) we extend the original model by establishing stochastic dependence between an agent’s responses and the next input of the sequence, and (b) we study the design of time varying decision strategies for the extended model. The strategies obtained are compared against static strategies from theoretical and empirical points of view. The results show that time varying strategies outperform static ones</p></abstract>

Adversarial decision making is aimed at determining strategies to anticipate the behavior of an opponent trying to learn from our actions. One defense is to make decisions intended to confuse the opponent, although our rewards can be diminished. This idea has already been captured in an adversarial model introduced in a previous work, in which two agents separately issue responses to an unknown sequence of external inputs. Each agent’s reward depends on the current input and the responses of both agents. In this contribution, (a) we extend the original model by establishing stochastic dependence between an agent’s responses and the next input of the sequence, and (b) we study the design of time varying decision strategies for the extended model. The strategies obtained are compared against static strategies from theoretical and empirical points of view. The results show that time varying strategies outperform static ones

A repeated imitation model with dependence between stages: Decision strategies and rewards

Models of Decision and Optimization (MODO) Research Group CITIC-UGR, Department of Computer Science and Artificial Intelligence University of Granada, ETSIIT, C/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Adversarial decision making is aimed at determining strategies to anticipate the behavior of an opponent trying to learn from our actions. One defense is to...

A repeated imitation model with dependence between stages: Decision strategies and rewards

Publié en ligne: 30 sept. 2015

Pages: 617 - 630

Reçu: 01 avr. 2014

DOI: https://doi.org/10.1515/amcs-2015-0045

© by Pablo J. Villacorta

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.