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136
Coalition Structure Generation with Worst Case Guarantees
, 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 ..."
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Cited by 252 (10 self)
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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 NPcomplete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half. As desired, our algorithm lowers the bound rapidly early on, and exhibits diminishing returns to computation. It also significantly outperforms its obvious contenders. Finally, we show how to distribute the desired
Coalitions Among Computationally Bounded Agents
 Artificial Intelligence
, 1997
"... This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individua ..."
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Cited by 190 (25 self)
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This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decisiontheoretically traded o against computation cost. A normative, application and protocolindependent theory of coalitions among boundedrational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents ' algorithms ' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and boundedrational agents is introduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NPcomplete and the instances are so large thatwith current technologyany agent's rationality is bounded by computational complexity. 1
Distributed Rational Decision Making
, 1999
"... Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etco ..."
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Cited by 185 (0 self)
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Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etcover which separately designed agents belonging to different organizations can interact in an open environment in realtime and safely carry out transactions. The second reason is strong application pull for computer support for negotiation at the operative decision making level. For example, we are witnessing the advent of small transaction electronic commerce on the Internet for purchasing goods, information, and communication bandwidth [29]. There is also an industrial trend toward virtual enterprises: dynamic alliances of small, agile enterprises which together can take advantage of economies of scale when available (e.g., respond to mor
Electoral Competition and Special Interest Politics
 Review of Economic Studies
, 1996
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 140 (3 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Negotiation Among Selfinterested Computationally Limited Agents
, 1996
"... A Dissertation Presented by TUOMAS W. SANDHOLM ..."
Computing Shapley values, manipulating value division schemes, and checking core membership in multiissue domains
 IN PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI
, 2004
"... Coalition formation is a key problem in automated negotiation among selfinterested agents. In order for coalition formation to be successful, a key question that must be answered is how the gains from cooperation are to be distributed. Various solution concepts have been proposed, but the computati ..."
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Cited by 67 (8 self)
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Coalition formation is a key problem in automated negotiation among selfinterested agents. In order for coalition formation to be successful, a key question that must be answered is how the gains from cooperation are to be distributed. Various solution concepts have been proposed, but the computational questions around these solution concepts have received little attention. We study a concise representation of characteristic functions which allows for the agents to be concerned with a number of independent issues that each coalition of agents can address. For example, there may be a set of tasks that the capacityunconstrained agents could undertake, where accomplishing a task generates a certain amount of value (possibly depending on how well the task is accomplished). Given this representation, we show how to quickly compute the Shapley value—a seminal value division scheme that distributes the gains from cooperation fairly in a certain sense. We then show that in (distributed) marginalcontribution based value division schemes, which are known to be vulnerable to manipulation of the order in which the agents are added to the coalition, this manipulation is NPcomplete. Thus, computational complexity serves as a barrier to manipulating the joining order. Finally, we show that given a value division, determining whether some subcoalition has an incentive to break away (in which case we say the division is not in the core) is NPcomplete. So, computational complexity serves to increase the stability of the coalition.
Mixedinteger programming methods for finding Nash equilibria
 IN PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI
, 2005
"... We present, to our knowledge, the first mixed integer program (MIP) formulations for finding Nash equilibria in games (specifically, twoplayer normal form games). We study different design dimensions of search algorithms that are based on those formulations. Our MIP Nash algorithm outperforms Lemke ..."
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Cited by 65 (22 self)
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We present, to our knowledge, the first mixed integer program (MIP) formulations for finding Nash equilibria in games (specifically, twoplayer normal form games). We study different design dimensions of search algorithms that are based on those formulations. Our MIP Nash algorithm outperforms LemkeHowson but not PorterNudelmanShoham (PNS) on GAMUT data. We argue why experiments should also be conducted on games with equilibria with mediumsized supports only, and present a methodology for generating such games. On such games MIP Nash drastically outperforms PNS but not LemkeHowson. Certain MIP Nash formulations also yield anytime algorithms for ɛequilibrium, with provable bounds. Another advantage of MIP Nash is that it can be used to find an optimal equilibrium (according to various objectives). The prior algorithms can be extended to that setting, but they are orders of magnitude slower.
Complexity of Determining Nonemptiness of the Core
, 2002
"... Coalition formation is a key problem in automated negotiation among selfinterested agents, and other multiagent applications. A coalition of agents can sometimes accomplish things that the individual agents cannot, or can do things more efficiently. However, ..."
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Cited by 46 (5 self)
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Coalition formation is a key problem in automated negotiation among selfinterested agents, and other multiagent applications. A coalition of agents can sometimes accomplish things that the individual agents cannot, or can do things more efficiently. However,
Anytime Coalition Structure Generation: An Average Case Study
 Journal of Experimental and Theoretical AI
, 2000
"... Abstract. Coalition formation is a key topic in multiagent systems. One would 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 for exhaustive search for the optimal one. We present experimental res ..."
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Cited by 42 (4 self)
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Abstract. Coalition formation is a key topic in multiagent systems. One would 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 for exhaustive search for the optimal one. We present experimental results for three anytime algorithms that search the space of coalition structures. We show that, in the average case, all three algorithms do much better than the recently established theoretical worst case results in Sandholm et al. (1999a). We also show that no one algorithm is dominant. Each algorithm’s performance is in¯uenced by the particular instance distribution, with each algorithm outperforming the others for diŒerent instances. We present a possible explanation for the behaviour of the algorithms and support our hypothesis with data collected from a controlled experimental run. K eywords: coalition structure, algorithm, multiagent systems 1.
When Are Nash Equilibria SelfEnforcing? An Experimental Analysis
 International Journal of Game Theory
, 2000
"... Abstract: We investigate the effect of nonbinding preplay communication in experiments with simple twoplayer coordination games. We reproduce the results of other studies in which play converges to a Paretodominated equilibrium in the absence of communication, and communication moves outcomes in ..."
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Cited by 35 (2 self)
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Abstract: We investigate the effect of nonbinding preplay communication in experiments with simple twoplayer coordination games. We reproduce the results of other studies in which play converges to a Paretodominated equilibrium in the absence of communication, and communication moves outcomes in the direction of the Paretodominant equilibrium. However, we provide new results which show that the effectiveness of communication is sensitive to the structure of payoffs. Our results support an argument put forward by Aumann: agreements to play a Nash equilibrium are fragile when players have a strict preference over their opponent's strategy choice. We also find that informative communication does not necessarily lead to the Paretodominant equilibrium.