1,721,809 research outputs found
Coalition Structure Generation: Dynamic Programming Meets Anytime Optimisation
No full textCoalition 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(3n), 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(nn). 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)
A hybrid algorithm for coalition structure generation
No full textThe current state-of-the-art algorithm for optimal coalition structure generation is IDP-IP—an algorithm that combines IDP (a dynamic programming algorithm due to Rahwan and Jennings, 2008b) with IP (a tree-search algorithm due to Rahwan et al., 2009). In this paper we analyse IDP-IP, highlight its limitations, and then develop a new approach for combining IDP with IP that overcomes these limitations
Stratum: A methodology for designing heuristic agent negotiation strategies.
Full text linkAutomated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behaviour. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this paper, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analysing the negotiation environment and designing strategies in light of agent capabilities, and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterising some strategies for the Trading Agent Competition and for argumentation-based negotiation
Algorithms for coalition formation in multi-agent systems
Full text linkCoalition formation is a fundamental form of interaction that allows 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 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. Now since the number of possible coalitions grows exponentially with the number of agents involved, then, instead of having a single agent calculate all these values, it would be more efficient to distribute this calculation among all agents, thus, exploiting all computational resources that are available to the system, and preventing the existence of a single point of failure.Against this background, we develop a novel algorithm for distributing the value calculation among the cooperative agents. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, has minimal memory requirements, and can reflect variations in the computational speeds of the agents. To evaluate the effectiveness of our algorithm we compare it with the only other algorithm available in the literature for distributing the coalitional value calculations (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took less than 0.02% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 383229848 to 0, and the total number of bytes sent between the agents dropped from 1146989648 to 0. Note that for larger numbers of agents, these improvements become exponentially better.Once the coalitional 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. This problem, which is widely known as the coalition structure generation problem, is extremely challenging due to the number of possible combinations which grows very quickly as the number of agents increases, making it impossible to go through the entire search space, even for small numbers of agents. Given this, 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 a novel, anytime algorithm for solving the coalition structure generation problem. Specifically, the algorithm can generate solutions by partitioning the space of all potential coalition structures into sub-spaces containing 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 can then search through the selected sub-space(s) very efficiently 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 required by the fastest available algorithm in the literature (for 27 agents), and that is using only 33% of the memory required by that algorithm. 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 30 agents as opposed to around 2 months for the current state of the art). 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, given 21 agents, and after only 0.0000002% of the search space has been searched, our algorithm usually guarantees that the solution quality is no worse than 91% of optimal value, while previous algorithms only guarantees 9.52%. Moreover, our guarantee usually reaches 100% after 0.0000019% of the space has been searched, while the guarantee provided by other algorithms can never go beyond 50% until the whole space has been searched. Again note that these improvements become exponentially better given larger numbers of agents
Anytime coalition structure generation in multi-agent systems with positive or negative externalities
No full textIn contrast, very little attention has been given to the more general class of Partition Function Games, where the emphasis is on how the formation of one coalition could influence the performance of other co-existing coalitions in the system. However, these inter-coalitional dependencies, called externalities from coalition formation, play a crucial role in many real-world multi-agent applications where agents have either conflicting or overlapping goals.Against this background, this paper is the first computational study of coalitional games with externalities in the multi-agent system context. We focus on the Coalition Structure Generation (CSG) problem which involves finding an exhaustive and disjoint division of the agents into coalitions such that the performance of the entire system is optimized. While this problem is already very challenging in the absence of externalities, due to the exponential size of the search space, taking externalities into consideration makes it even more challenging as the size of the input, given n agents, grows from O(2n) to O(nn).Our main contribution is the development of the first CSG algorithm for coalitional games with either positive or negative externalities. Specifically, we prove that it is possible to compute upper and lower bounds on the values of any set of disjoint coalitions. Building upon this, we prove that in order to establish a worst-case guarantee on solution quality it is necessary to search a certain set of coalition structures (which we define). We also show how to progressively improve this guarantee with further search.Since there are no previous CSG algorithms for games with externalities, we benchmark our algorithm against other state-of-the-art approaches in games where no externalities are present. Surprisingly, we find that, as far as worst-case guarantees are concerned, our algorithm outperforms the others by orders of magnitude. For instance, to reach a bound of 3 given 24 agents, the number of coalition structures that need to be searched by our algorithm is only 0.0007% of that needed by Sandholm et al. (1999) [1], and 0.5% of that needed by Dang and Jennings (2004) [2]. This is despite the fact that the other algorithms take advantage of the special properties of games with no externalities, while ours does not
An anytime algorithm for finding the ε-core in nontransferable utility coalitional games
No full textWe provide the first anytime algorithm for finding the ε-core in a nontransferable utility coalitional game. For a given set of possible joint actions, our algorithm calculates ε, the maximum utility any agent could gain by deviating from this set of actions. If ε is too high, our algorithm searches for a subset of the joint actions which leads to a smaller ε. Simulations show our algorithm is more efficient than an exhaustive search by up to 2 orders of magnitude
An Algorithm for Distributing Coalitional Value Calculations among Cooperating Agents
No full textThe process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, instead of having a single agent calculate all these values (as is typically the case), it is more efficient to distribute this calculation among the agents, thus using all the computational resources available to the system and avoiding the existence of a single point of failure. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents’ shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, has minimal memory requirements, and can reflect variations in the computational speeds of the agents. To evaluate the effectiveness of our algorithm, we compare it with the only other algorithm available in the literature for distributing the coalitional value calculations (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took less than 0.02% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 383229848 to 0, and the total number of bytes sent between the agents dropped from 1146989648 to 0 (note that for larger numbers of agents, these improvements become exponentially better)
An anytime algorithm for optimal coalition structure generation
Full text linkCoalition 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
Minimum search to establish worst-case guarantees in coalition structure generation
No full textCoalition formation is a fundamental research topic in multi-agent systems. In this context, while it is desirable to generate a coalition structure that maximizes the sum of the values of the coalitions, the space of possible solutions is often too large to allow exhaustive search. Thus, a fundamental open question in this area is the following: Can we search through only a subset of coalition structures, and be guaranteed to find a solution that is within a desirable bound (beta) from optimum? If so, what is the minimum such subset? To date, the above question has only been partially answered by Sandholm et al. in their seminal work on anytime coalition structure generation [Sandholm et al., 1999]. More specifically, they identified minimum subsets to be searched for two particular bounds: β = n and β = [n/2]. Nevertheless, the question remained open for other values of β. In this paper, we provide the complete answer to this question
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