A distributed algorithm for anytime coalition structure generation (original) (raw)
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An anytime algorithm for optimal coalition structure generation
2010 Sixth International Conference on Natural Computation, 2010
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.
Near-optimal anytime coalition structure generation
Proceedings of the 20th …, 2007
Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best set of agents that should participate in a given team. To this end, in this paper, we present a novel, anytime algorithm for coalition structure generation that is faster than previous anytime algorithms designed for this purpose. Our algorithm can generate solutions that either have a tight bound from the optimal or are optimal (depending on the objective) and works by partitioning the space in terms of a small set of elements that represent structures which contain coalitions of particular sizes. It then performs an online heuristic search that prunes the space and only considers valid and non-redundant coalition structures. We empirically show that we are able to find solutions that are, in the worst case, 99% efficient in 0.0043% of the time to find the optimal value by the state of the art dynamic programming (DP) algorithm (for 20 agents), using 66% less memory.
Anytime Optimal Coalition Structure Generation
2007
Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best groups of agents to select to achieve some goal. To this end, in this paper, we present a novel, optimal anytime algorithm for this coalition structure generation problem that is significantly faster than previous algorithms designed for this purpose. Specifically, our algorithm can generate solutions by partitioning the space of all potential coalitions into sub-spaces that contain coalition structures that are similar, according to some criterion, such that these sub-spaces can be pruned by identifying their bounds. Using this representation, the algorithm then searches through only valid and unique coalition structures and selects the best among them using a branch-and-bound technique. We empirically show that we are able to find solutions that are optimal in 0.082% of the time taken by the state of the art dynamic programming algorithm (for 27 agents) using much less memory (O(2 |A| ) instead of O(3 |A| ) for the set of agents A). Moreover, our algorithm is the first to be able to solve the coalition structure generation problem for numbers of agents bigger than 27 in reasonable time (less than 90 minutes for 27 agents as opposed to around 2 months for the best previous solution). n 2 ) [8]. Moreover,
Distributed Coalition Formation for Collaborative Multi-Agent Systems
2006
We investigate collaborative multi-agent systems (MAS) and simple, scalable negotiation protocols for such MAS that enable the agents to coordinate and self-organize in a peer-to-peer, fully decentralized and dynamic manner in order to better accomplish various tasks. We specifically focus on the problem of multi-agent distributed coalition formation. We describe our coalition formation algorithm, MCDCF, as a scalable and fully decentralized subroutine that can find its use in a variety of multi-agent coordination and collaboration scenarios. We summarize our recent simulation results of running an optimized version of MCDCF on random (Erdös-Renyi) graphs. We performed statistical analysis of MCDCF performance on random graphs in two scenarios. In one scenario, we studied performance when the graph density is held fixed and the total number of nodes is increased up to 100. In the second scenario, we fixed the total number of nodes but varied the graph density, that is, the average number of neighbors per node. The experiments on random graphs of fixed densities generally validate our theoretical predictions about the scalability and convergence properties of our algorithm. Finally, we briefly discuss what we have learned from our experiments and outline several lines of future work under consideration.
Frontiers in artificial intelligence and applications, 2023
Organizing agents into disjoint groups is a crucial challenge in artificial intelligence, with many applications where quick runtime is essential. The Simultaneous Coalition Structure Generation and Assignment (SCSGA) problem involves partitioning a set of agents into coalitions and assigning each coalition to a task, with the goal of maximizing social welfare. However, this is an NP-complete problem, and only a few algorithms have been proposed to address it for both small and large-scale problems. In this paper, we address this challenge by presenting a novel algorithm that can efficiently solve both small and large instances of this problem. Our method is based on a new search space representation, where each coalition is codified by an index. We have developed an algorithm that can explore this solution space effectively by generating index vectors that represent coalition structures. The resulting algorithm is anytime and can scale to large problems with hundreds or thousands of agents. We evaluated our algorithm on a range of value distributions and compared its performance against state-of-the-art algorithms. Our experimental results demonstrate that our algorithm outperforms existing methods in solving the SCSGA problem, providing high-quality solutions for a wide range of problem instances.
Novel Heuristics for Coalition Structure Generation in Multi-agent Systems
2010
A coalition is a set of self-interested agents that agree to cooperate for achieving a set of goals. Coalition formation is an active area of research in multi-agent systems nowadays. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal, which is called coalition structure generation. 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. Generally, agents would enumerate all possible coalitions, store them in memory, and then try to construct the coalition structure that maximizes the sum of the values of the coalitions. However, this is not feasible when we have a large number of agents, and other constraints on execution time, and memory. Hence, there is a need to develop an algorithm that can generate solutions rapidly for large number of agents while providing bound...
We present herein a fully distributed algorithm for group or coalition formation among autonomous agents. The algorithm is based on a distributed computation of maximal cliques (of up to pre-specified size) in the underlying graph that captures the interconnection topology of the agents. Hence, given the current configuration of the agents, the groups that are formed are characterized by a high degree of connectivity, and therefore high fault tolerance with respect to node and link failures. We also briefly discuss how our basic algorithm can be adapted in various ways so that the formed groups satisfy the requirements ("goodness" criteria) other than mere strong inter-group communication connectivity. We envision various variants of our basic algorithm to prove themselves useful subroutines in many multi-agent system and ad hoc network applications where the agents may repeatedly need to form temporary groups or coalitions in an efficient, fully distributed and online manner.
Coalition structure generation: dynamic programming meets anytime optimisation
2008
Coalition structure generation involves partitioning a set of agents into exhaustive and disjoint coalitions so as to maximize the social welfare. What makes this such a challenging problem is that the number of possible solutions grows exponentially as the number of agents increases. To date, two main approaches have been developed to solve this problem, each with its own strengths and weaknesses. The state of the art in the first approach is the Improved Dynamic Programming (IDP) algorithm, due to Rahwan and Jennings, that is guaranteed to find an optimal solution in O(3 n ), but which cannot generate a solution until it has completed its entire execution. The state of the art in the second approach is an anytime algorithm called IP, due to Rahwan et al., that provides worst-case guarantees on the quality of the best solution found so far, but which is O(n n ). In this paper, we develop a novel algorithm that combines both IDP and IP, resulting in a hybrid performance that exploits the strength of both algorithms and, at the same, avoids their main weaknesses. Our approach is also significantly faster (e.g. given 25 agents, it takes only 28% of the time required by IP, and 0.3% of the time required by IDP).
An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment
2018
A fundamental problem in artificial intelligence is how to organize and coordinate agents to improve their performance and skills. In this paper, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent tasks, and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This optimization problem has many real-world applications, including forming goal-oriented teams of agents. To evaluate the algorithm’s performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm against CPLEX using randomized data sets of varying distribution and complexity. We also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that the algorithm can be utilized in game-playing to coordinate smaller sets of agents in real-time.