scholarly journals Coalition Lattice: A Data Structure considering Robustness for Robust Coalition Structure Generation Problem

2015 ◽  
Author(s):  
Katsuya Nakano ◽  
Shun Shiramatsu ◽  
Tadachika Ozono ◽  
Toramatsu Shintani
Keyword(s):  
Data Structure ◽  
Coalition Structure ◽  
Structure Generation ◽  
Coalition Structure Generation ◽  
Generation Problem

scholarly journals On the Online Coalition Structure Generation Problem

2021 ◽  
Vol 72 ◽  
pp. 1215-1250
Author(s):  
Michele Flammini ◽  
Gianpiero Monaco ◽  
Luca Moscardelli ◽  
Mordechai Shalom ◽  
Shmuel Zaks
Keyword(s):  
Online Algorithm ◽  
Coalition Structure ◽  
Weight Functions ◽  
Value Functions ◽  
Multi Agent Systems ◽  
Structure Generation ◽  
Coalition Structures ◽  
Multi Agent ◽  
Coalition Structure Generation ◽  
Generation Problem

We consider the online version of the coalition structure generation problem, in which agents, corresponding to the vertices of a graph, appear in an online fashion and have to be partitioned into coalitions by an authority (i.e., an online algorithm). When an agent appears, the algorithm has to decide whether to put the agent into an existing coalition or to create a new one containing, at this moment, only her. The decision is irrevocable. The objective is partitioning agents into coalitions so as to maximize the resulting social welfare that is the sum of all coalition values. We consider two cases for the value of a coalition: (1) the sum of the weights of its edges, and (2) the sum of the weights of its edges divided by its size. Coalition structures appear in a variety of application in AI, multi-agent systems, networks, as well as in social networks, data analysis, computational biology, game theory, and scheduling. For each of the coalition value functions we consider the bounded and unbounded cases depending on whether or not the size of a coalition can exceed a given value α. Furthermore, we consider the case of a limited number of coalitions and various weight functions for the edges, i.e., unrestricted, positive and constant weights. We show tight or nearly tight bounds for the competitive ratio in each case.

scholarly journals A New Genetic Algorithm Encoding for Coalition Structure Generation Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Pablo Contreras ◽  
Paul Bosch ◽  
Mauricio Varas ◽  
Franco Basso
Keyword(s):  
Genetic Algorithm ◽  
Coalition Structure ◽  
Combinatorial Problems ◽  
Relevant Issue ◽  
Structure Generation ◽  
Genetic Encoding ◽  
Computational Implementation ◽  
Coalition Structure Generation ◽  
Generation Problem ◽  
Reasonable Computing

Genetic algorithms have proved to be a useful improvement heuristic for tackling several combinatorial problems, including the coalition structure generation problem. In this case, the focus lies on selecting the best partition from a discrete set. A relevant issue when designing a Genetic algorithm for coalition structure generation problems is to choose a proper genetic encoding that enables an efficient computational implementation. In this paper, we present a novel hybrid encoding, and we compare its performance against several genetic encoding proposed in the literature. We show that even in difficult instances of the coalition structure generation problem, the proposed approach is a competitive alternative to obtaining good quality solutions in reasonable computing times. Furthermore, we also show that the encoding relevance increases as the number of players increases.

scholarly journals MaxSAT Encoding for MC-Net-Based Coalition Structure Generation Problem with Externalities

2014 ◽  
Vol E97.D (7) ◽  
pp. 1781-1789 ◽  
Author(s):  
Xiaojuan LIAO ◽  
Miyuki KOSHIMURA ◽  
Hiroshi FUJITA ◽  
Ryuzo HASEGAWA
Keyword(s):  
Coalition Structure ◽  
Structure Generation ◽  
Coalition Structure Generation ◽  
Generation Problem

scholarly journals An Anytime Algorithm for Optimal Coalition Structure Generation

2009 ◽  
Vol 34 ◽  
pp. 521-567 ◽  
Author(s):  
T. Rahwan ◽  
S. D. Ramchurn ◽  
N. R. Jennings ◽  
A. Giovannucci
Keyword(s):  
Optimal Solution ◽  
Coalition Structure ◽  
Upper And Lower Bounds ◽  
Multi Agent Systems ◽  
Worst Case ◽  
Structure Generation ◽  
Anytime Algorithm ◽  
Coalition Structures ◽  
Coalition Structure Generation ◽  
Generation Problem

Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques ranging from dynamic programming, to integer programming, to stochastic search all of which suffer from major limitations relating to execution time, solution quality, and memory requirements. With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.

Robust Coalition Structure Generation

2018 ◽  
pp. 140-157
Author(s):  
Tenda Okimoto ◽  
Nicolas Schwind ◽  
Emir Demirović ◽  
Katsumi Inoue ◽  
Pierre Marquis
Keyword(s):  
Coalition Structure ◽  
Structure Generation ◽  
Coalition Structure Generation
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