@article {593,
	title = {Fair Resource Allocation in a Simple Multi-Agent Setting: Search Algorithms and Experimental Evaluation},
	journal = {International Journal on Artificial Intelligence Tools (IJAIT)},
	volume = {14},
	year = {2005},
	month = {December},
	pages = {887{\textendash}899},
	abstract = {We study the problem of fair resource allocation in a simple cooperative multi-agent setting where we have k agents and a set of n objects to be allocated to those agents. Each object is associated with a weight represented by a positive integer or real number. We would like to allocate all objects to the agents so that each object is allocated to only one agent and the weight is distributed fairly. We adopt the fairness index popularized by the networking community as our measure of fairness, and study centralized algorithms for fair resource allocation. Based on the relationship between our problem and number partitioning, we devise a greedy algorithm for fair resource allocation that runs in polynomial time but is not guaranteed to find the optimal solution, and a complete anytime algorithm that finds the optimal solution but runs in exponential time. Then we study the phase transition behavior of the complete algorithm. Finally, we demonstrate that the greedy algorithm actually performs very well and returns almost perfectly fair allocations.},
	keywords = {Fairness; resource allocation; number partitioning; phase transitions},
	author = {Paraskevi Raftopoulou and Manolis Koubarakis and Kostas Stergiou and Peter Triantafillou}
}
