Optimal Doubling Strategy against a Sub-optimal Opponent
نویسنده
چکیده
For two-person, zero-sum games where the probability of each player winning is a continuous function of time and is known to both players, the mutually optimal strategy for proposing and accepting a doubling of the game value is known. We present an algorithm for deriving the optimal doubling strategy of a player who is aware of the sub-optimal strategy followed by the opponent. We also present numerical results about the magnitude of the benefits; the results support the claim that repeated application of the algorithm from both players leads to the mutually optimal strategy.
منابع مشابه
Winning Opponent Counter Strategy Selection in Holdem Poker
The game of poker presents an interesting and complex problem for game theorists and researchers in machine learning. Current work on the subject focuses on how to develop optimal counter strategies, often referring to the Upper Confidence Bounds (UCB1) algorithm to determine which of these counter strategies is optimal for an unknown opponent. We present a new method for taking a learned set o...
متن کاملOf matchers and maximizers: How competition shapes choice under risk and uncertainty.
In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice ta...
متن کاملPlanning for Persuasion
We aim to find a winning strategy that determines the arguments a proponent should assert during a dialogue such that it will successfully persuade its opponent of some goal arguments, regardless of the strategy employed by the opponent. By restricting the strategies we consider for the proponent to what we call simple strategies and by modelling this as a planning problem, we are able to use a...
متن کاملMatch Me if You Can: How Smart Choices are Fueled by Competition
In a world of limited resources, scarcity and rivalry are central challenges for decision makers. We examine choice behavior in competitive probability learning environments that reinforce one of two strategies. The optimality of a strategy is dependent on the behavior of a computerized opponent: if the opponent mimics participant choices, probability matching is optimal; if the opponent is ind...
متن کاملLearning to Negotiate Optimally in Non-stationary Environments
We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In so doin...
متن کامل