A regret minimizing set Q is a small size representation of a much larger database P so that user queries executed on Q return answers whose scores are not much worse than those on the full dataset. In particular, a k-regret minimizing set has the property that the regret ratio between the score of the top-1 item in Q and the score of the top-k item in P is minimized, where the score of an item...

Regret minimization is an effective technique for almost surely producing Nash equilibrium policies in coordination games in the strategic form. Decentralized POMDPs offer a realistic model for sequential coordination problems, but they yield doubly exponential sized games in the strategic form. Recently, counterfactual regret has offered a way to decompose total regret along a (extensive form)...

Regret minimization is important in both the Multi-Armed Bandit problem and Monte-Carlo Tree Search (MCTS). Recently, simple regret, i.e., the regret of not recommending the best action, has been proposed as an alternative to cumulative regret in MCTS, i.e., regret accumulated over time. Each type of regret is appropriate in different contexts. Although the majority of MCTS research applies the...

This study explores the plausibility of regret minimization as behavioral paradigm underlying the choice of crash avoidance maneuvers. Alternatively to previous studies that considered utility maximization, this study applies the random regret minimization (RRM) model while assuming that drivers seek to minimize their anticipated regret from their corrective actions. The model accounts for driv...

This article proposes an original approach to predict the electric vehicles (EVs)’ energy demand in a charge station using a regret minimization learning approach. The problem is modelled as a two players game involving: on the one hand the EV drivers, whose demand is unknown and, on the other hand, the service provider who owns the charge station and wants to make the best predictions in order...

The iterated regret minimization solution exhibits the good qualitative behavior as that observed in experiments in many games that have proved problematic for Nash Equilibrium(NE). It is worthy exploring epistemic characterizations unearthing players’rationality for an algorithm of Iterated Eliminations Regret-dominated Strategy (IERS) related to the solution. In this paper, based on the dynam...

The nonstochastic multi-armed bandit problem, first studied by Auer, Cesa-Bianchi, Freund, and Schapire in 1995, is a game of repeatedly choosing one decision from a set of decisions (“experts”), under partial observation: In each round t , only the cost of the decision played is observable. A regret minimization algorithm plays this game while achieving sublinear regret relative to each decisi...

In this paper, we present distributed cooperative and regret-matching-based learning schemes for joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment. Under the total network power minimization criterion, a joint iterative approach is proposed to reduce the mutual interference at each node while ensuring ...

This paper considers a game-theoretic framework for motion coordination challenges. The focus of this work is to minimize the number of interactions agents have when moving through an environment. In particular, agents employ a replanning framework and regret minimization over a set of actions, which correspond to different homotopic paths. By associating a cost to each trajectory, a motion coo...