Abstract. An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k ...
Abstract. The stable marriage problem has many practical applications in twosided markets like those that assign doctors to hospitals, students to schools, or buyers to vendors. Mo...
Enrico Pilotto, Francesca Rossi, Kristen Brent Ven...
Abstract. Several schemes have been proposed for compactly representing multiattribute utility functions, yet none seems to achieve the level of success achieved by Bayesian and Ma...
Abstract. We propose various models for lobbying in a probabilistic environment, in which an actor (called “The Lobby”) seeks to influence the voters’ preferences of voting ...
We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches fo...
In sports competitions, teams can manipulate the result by, for instance, throwing games. We show that we can decide how to manipulate round robin and cup competitions, two of the ...
Most work in game theory is conducted under the assumption that the players are expected utility maximizers. Expected utility is a very tractable decision model, but is prone to w...
Judgment aggregation is a formal theory reasoning about how a group of agents can aggregate individual judgments on connected propositions into a collective judgment on the same pr...
Gabriella Pigozzi, Marija Slavkovik, Leendert van ...