Efficient learning equilibrium
Artificial Intelligence
Prediction, Learning, and Games
Prediction, Learning, and Games
Game-theoretic recommendations: some progress in an uphill battle
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
| Hi-index | 0.00 |
Decision-Making lies in the foundations of fields such as Economics, Operations Research, and Artificial Intelligence. The question of what should be the action to be taken by a decision-maker when facing an uncertain environment, potentially including other decision makers, is a fundamental problem which has led to a wide variety of models and solutions. One of the only types of situations (of more than a single agent) for which this question got an agreed-upon answer is in the context of two-player zero-sum games. This setting can model any situation in which a decision-maker aims at maximizing her guaranteed payoff. When mixed strategies are allowed, such desired behavior, termed an agent's maximin (or safety level) strategy, leads to a well defined expected payoff (known as the value of the game). Moreover, when presented explicitly in a matrix form, the computation of a maximin strategy is polynomial (by solving a linear program).