The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents’ preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models
Multi-agent resource allocation is an important and well-studied problem within AI and economics. It...
We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of a...
The efficient use of resources is a crucial problem of our time. Besides the constraints of efficien...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in social choice, matching, and disc...
The assignment problem is one of the most well-studied settings in social choice, matching, and disc...
Reallocating resources to get mutually beneficial outcomes is a fundamental problem in various multi...
We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based ...
We investigate the probabilistic feasibility of randomized solutions to two distinct classes of unce...
We consider the problem of multi-robot task-allocation when robots have to deal with uncertain utili...
Selecting a set of alternatives based on the preferences of agents is an important problem in commit...
The problem of multi-agent resource allocation is important and well-studied within AI and economics...
Multi-agent resource allocation is an important and well-studied problem within AI and economics. It...
We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of a...
The efficient use of resources is a crucial problem of our time. Besides the constraints of efficien...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. ...
The assignment problem is one of the most well-studied settings in social choice, matching, and disc...
The assignment problem is one of the most well-studied settings in social choice, matching, and disc...
Reallocating resources to get mutually beneficial outcomes is a fundamental problem in various multi...
We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based ...
We investigate the probabilistic feasibility of randomized solutions to two distinct classes of unce...
We consider the problem of multi-robot task-allocation when robots have to deal with uncertain utili...
Selecting a set of alternatives based on the preferences of agents is an important problem in commit...
The problem of multi-agent resource allocation is important and well-studied within AI and economics...
Multi-agent resource allocation is an important and well-studied problem within AI and economics. It...
We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of a...
The efficient use of resources is a crucial problem of our time. Besides the constraints of efficien...