Multi-agent Team Formation for Design Problems

Marcolino, Leandro Soriano; Xu, Haifeng; Gerber, David Jason; Kolev, Boian; Price, Samori; Pantazis, Evangelos; Tambe, Milind

doi:10.1007/978-3-319-42691-4_20

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…The problem studied by Lappas et al [2009] focuses on finding a single group of agents who possess a given set of skills, so as to minimise the communication cost within such a group. Marcolino et al [2013] focus on forming a single group of agents that has the maximum strength in the set of world states. Finally, Liemhetcharat and Veloso [2014] are interested in modelling the values of the characteristic function, based on observations of the agents.…”

Section: Csg Algorithms Based On Heuristicsmentioning

confidence: 99%

Algorithms for Graph-Constrained Coalition Formation in the Real World

Bistaffa

Farinelli

Cerquides

et al. 2017

ACM Trans. Intell. Syst. Technol.

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Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called m + a functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).

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Section: Csg Algorithms Based On Heuristicsmentioning

confidence: 99%

Algorithms for Graph-Constrained Coalition Formation in the Real World

Bistaffa

Farinelli

Cerquides

et al. 2017

ACM Trans. Intell. Syst. Technol.

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“…Other open questions include the effect of proxy voting on diversity, fairness, and participation. It is argued that diverse representatives often reach better outcomes [16], and fairness attracts much attention in the analysis of voting and other multiagent systems [4,29,7]. The effect on participation and engagement may also be quite involved, since allowing voters to use a proxy may increase the participation level of some who would otherwise not be represented, but on the other hand may lower the incentive to vote actively, thereby reducing overall engagement of the society.…”

Section: Strategic Behaviormentioning

confidence: 99%

Proxy Voting for Better Outcomes

Cohensius,

Manor,

Meir

et al. 2016

Preprint

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We consider a social choice problem where only a small number of people out of a large population are sufficiently available or motivated to vote. A common solution to increase participation is to allow voters use a proxy, that is, transfer their voting rights to another voter. Considering social choice problems on metric spaces, we compare voting with and without the use of proxies to see which mechanism better approximates the optimal outcome, and characterize the regimes in which proxy voting is beneficial.When voters' opinions are located on an interval, both the median mechanism and the mean mechanism are substantially improved by proxy voting. When voters vote on many binary issues, proxy voting is better when the sample of active voters is too small to provide a good outcome. Our theoretical results extend to situations where available voters choose strategically whether to participate. We support our theoretical findings with empirical results showing substantial benefits of proxy voting on simulated and real preference data.

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“…1. Forming Diverse Teams Our setting and objectives align with the research on diversity maximization algorithms, a topic that has gained significant traction, particularly with respect to forming diverse teams that capture distinct perspectives [22,27]. In these problems, each agent corresponds to a point in a metric space: this point represents the agents's beliefs, skills, or opinions.…”

Section: Model and Problem Statementsmentioning

confidence: 99%

Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering

Anshelevich,

Sekar

2015

Preprint

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We study Matching and other related problems in a partial information setting where the agents' utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph, and look to design algorithms that approximate the true optimum matching using only the preference orderings for each agent (induced by the hidden weights) as input. If no restrictions are placed on the weights, then one cannot hope to do better than the simple greedy algorithm, which yields a half optimal matching. Perhaps surprisingly, we show that by imposing a little structure on the weights, we can improve upon the trivial algorithm significantly: we design a 1.6-approximation algorithm for instances where the hidden weights obey the metric inequality. Using our algorithms for matching as a black-box, we also design new approximation algorithms for other closely related problems: these include a a 3.2-approximation for the problem of clustering agents into equal sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a 2.14-approximation algorithm for Max TSP. These results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that we can design robust algorithms even when we are agnostic to the precise agent utilities.

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Multi-agent Team Formation for Design Problems

Cited by 6 publications

References 21 publications

Algorithms for Graph-Constrained Coalition Formation in the Real World

Algorithms for Graph-Constrained Coalition Formation in the Real World

Proxy Voting for Better Outcomes

Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering

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