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Related papers: Complexity of Terminating Preference Elicitation

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There has been considerable work on reasoning about the strategic ability of agents under imperfect information. However, existing logics such as Probabilistic Strategy Logic are unable to express properties relating to information…

Artificial Intelligence · Computer Science 2025-01-07 Chunyan Mu , Nima Motamed , Natasha Alechina , Brian Logan

This paper studies delegation in a model of discrete choice. In the delegation problem, an uninformed principal must consult an informed agent to make a decision. Both the agent and principal have preferences over the decided-upon action…

Computer Science and Game Theory · Computer Science 2024-06-25 Ali Khodabakhsh , Emmanouil Pountourakis , Samuel Taggart

This paper examines the integration of computational complexity into game theoretic models. The example focused on is the Prisoner's Dilemma, repeated for a finite length of time. We show that a minimal bound on the players' computational…

Computer Science and Game Theory · Computer Science 2007-05-23 Yishay Mor , Jeffrey S. Rosenschein

In a voting problem with a finite set of alternatives to choose from, we study the manipulation of tops-only rules. Since all non-dictatorial (onto) voting rules are manipulable when there are more than two alternatives and all preferences…

Theoretical Economics · Economics 2022-10-24 R. Pablo Arribillaga , Agustin G. Bonifacio

We discuss the computational complexity and feasibility properties of scenario based techniques for uncertain optimization programs. We consider different solution alternatives ranging from the standard scenario approach to recursive…

Optimization and Control · Mathematics 2014-12-16 Nikolaos Kariotoglou , Kostas Margellos , John Lygeros

Given a learning problem with real-world tradeoffs, which cost function should the model be trained to optimize? This is the metric selection problem in machine learning. Despite its practical interest, there is limited formal guidance on…

Machine Learning · Statistics 2022-08-22 Gaurush Hiranandani

Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this paper, we provide a computer-aided proof…

Computer Science and Game Theory · Computer Science 2017-09-07 Florian Brandl , Felix Brandt , Manuel Eberl , Christian Geist

The stable marriage problem and its extensions have been extensively studied, with much of the work in the literature assuming that agents fully know their own preferences over alternatives. This assumption however is not always practical…

Computer Science and Game Theory · Computer Science 2016-03-21 Baharak Rastegari , Paul Goldberg , David Manlove

In the celebrated stable-matching problem, there are two sets of agents M and W, and the members of M only have preferences over the members of W and vice versa. It is usually assumed that each member of M and W is a single entity. However,…

Computer Science and Game Theory · Computer Science 2024-08-21 Leora Schmerler , Noam Hazon , Sarit Kraus

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested…

Machine Learning · Statistics 2018-05-08 Paolo Dragone , Stefano Teso , Mohit Kumar , Andrea Passerini

We study the computational complexity of an important property of simple, regular and weighted games, which is decisiveness. We show that this concept can naturally be represented in the context of hypergraph theory, and that decisiveness…

Computer Science and Game Theory · Computer Science 2013-07-10 Andreas Polyméris , Fabián Riquelme

Decision-making problems in uncertain or stochastic domains are often formulated as Markov decision processes (MDPs). Policy iteration (PI) is a popular algorithm for searching over policy-space, the size of which is exponential in the…

Artificial Intelligence · Computer Science 2013-01-30 Yishay Mansour , Satinder Singh

Let L be some extension of classical propositional logic. The non-iterated probabilistic logic over L, is the logic PL that is defined by adding non-nested probabilistic operators in the language of L. For example in PL we can express a…

Logic in Computer Science · Computer Science 2019-02-12 Ioannis Kokkinis

Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy so far is based on the assumption that users can most…

Machine Learning · Statistics 2022-12-08 Safinah Ali , Sohini Upadhyay , Gaurush Hiranandani , Elena L. Glassman , Oluwasanmi Koyejo

Selecting a set of alternatives based on the preferences of agents is an important problem in committee selection and beyond. Among the various criteria put forth for the desirability of a committee, Pareto optimality is a minimal and…

Computer Science and Game Theory · Computer Science 2018-03-20 Haris Aziz , Jerome Lang , Jerome Monnot

The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning. This process, referred to as distillation, has been used to great success, for example, by enhancing the optimisation of agents,…

In building Bayesian belief networks, the elicitation of all probabilities required can be a major obstacle. We learned the extent of this often-cited observation in the construction of the probabilistic part of a complex influence diagram…

Artificial Intelligence · Computer Science 2013-01-30 Linda C. van der Gaag , Silja Renooij , Cilia L. M. Witteman , Berthe M. P. Aleman , Babs G. Taal

Strategic voting, or manipulation, is the process by which a voter misrepresents his preferences in an attempt to elect an outcome that he considers preferable to the outcome under sincere voting. It is generally agreed that manipulation is…

Computer Science and Game Theory · Computer Science 2022-11-10 Egor Ianovski , Daria Teplova , Valeriia Kuka

The Coalitional Manipulation (CM) problem has been studied extensively in the literature for many voting rules. The CM problem, however, has been studied only in the complete information setting, that is, when the manipulators know the…

Artificial Intelligence · Computer Science 2015-05-01 Palash Dey , Neeldhara Misra , Y. Narahari

The probabilistic serial (PS) rule is one of the most prominent randomized rules for the assignment problem. It is well-known for its superior fairness and welfare properties. However, PS is not immune to manipulative behaviour by the…

Computer Science and Game Theory · Computer Science 2014-01-28 Haris Aziz , Serge Gaspers , Nick Mattei , Nina Narodytska , Toby Walsh