BOSS: A Bi-directional Search Technique for Optimal Coalition Structure Generation with Minimal Overlapping (Student Abstract) (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.
ODSS: Efficient Hybridization for Optimal Coalition Structure Generation
Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Coalition Structure Generation (CSG) is an NP-complete problem that remains difficult to solve on account of its complexity. In this paper, we propose an efficient hybrid algorithm for optimal coalition structure generation called ODSS. ODSS is a hybrid version of two previously established algorithms IDP (Rahwan and Jennings 2008) and IP (Rahwan et al. 2009). ODSS minimizes the overlapping between IDP and IP by dividing the whole search space of CSG into two disjoint sets of subspaces and proposes a novel subspace shrinking technique to reduce the size of the subspace searched by IP with the help of IDP. When compared to the state-of-the-art against a wide variety of value distributions, ODSS is shown to perform better by up to 54.15% on benchmark inputs.
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,
Improving coalition structure search with an imperfect algorithm: analysis and evaluation results
Artificial Intelligence Review, 2020
Optimal Coalition Structure Generation (CSG) is a significant research problem in multiagent systems that remains difficult to solve. This problem has many important applications in transportation, eCommerce, distributed sensor networks and others. The CSG problem is NPcomplete and finding the optimal result for n agents needs to check O(n n) possible partitions. The ODP-IP algorithm (Michalak et al. in Artif Intell 230:14-50, 2016) achieves the current lowest worst-case time complexity of O(3 n). In the light of its high computational time complexity, we devise an Imperfect Dynamic Programming (ImDP) algorithm for the CSG problem with runtime O(n2 n) given n agents. Imperfect algorithm means that there are some contrived inputs for which the algorithm fails to give the optimal result. We benchmarked ImDP against ODP-IP and proved its efficiency. Experimental results confirmed that ImDP algorithm performance is better for several data distributions, and for some it improves dramatically ODP-IP. For example, given 27 agents, with ImDP for agent-based uniform distribution time gain is 91% (i.e. 49 min).
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...
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.
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.
A distributed algorithm for anytime coalition structure generation
2010
A major research challenge in multi-agent systems is the problem of partitioning a set of agents into mutually disjoint coalitions, such that the overall performance of the system is optimized. This problem is difficult because the search space is very large: the number of possible coalition structures increases exponentially with the number of agents. Although several algorithms have been proposed to tackle this Coalition Structure Generation (CSG) problem, all of them suffer from being inherently centralized, which leads to the existence of a performance bottleneck and a single point of failure. In this paper, we develop the first decentralized algorithm for solving the CSG problem optimally. In our algorithm, the necessary calculations are distributed among the agents, instead of being carried out centrally by a single agent (as is the case in all the available algorithms in the literature). In this way, the search can be carried out in a much faster and more robust way, and the agents can share the burden of the calculations. The algorithm combines, and improves upon, techniques from two existing algorithms in the literature, namely DCVC [5] and IP [9], and applies novel techniques for filtering the input and reducing the inter-agent communication load.
Coalition structure generation with worst case guarantees
Artificial Intelligence, 1999
Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition structure is NP-complete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum?
A Best-First Anytime Algorithm for Computing Optimal Coalition Structures (Short Paper)
2008
This work presents a best-rst anytime algorithm for com- puting optimal coalition structures. The approach is novel in that it generates coalition structures based on coalition values, while existing algorithms base their generation on the structure (members and congurations) of coalitions. With our algorithm, coalition structures are generated by repeatedly choosing the best coalition, as determined using a novel metric