T. Sandholm, V. Lesser, Coalitions among computationally bounded agents, Artificial Intelligence 94 (1) (1997) 99--137 (Special issue on Economic Principles of Multi-Agent Systems).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....dependent on which other loads are won, and at what cost. s For this reason, and for the sake of computational feasibility, we allow each agent to only place a bid for at most one load in each round of auctions. Our agents can thus be seen as computationally and rationally bounded (in the sense of [20, 30, 23]) although they repair (some of) their non optimal local decisions through decommitment. Each piece of cargo is sold in a separate Vickrey auction. In this auction type, the highest bidder wins the contract but pays the second highest price In our model, neither the number of participants nor ....

T. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1-2):99-137, 1997.

....the agents. Recent work on coalition formation analyzes coalitions among self interested agents. By forming a coalition and acting jointly agents can save costs in contrast to acting individually. 20] presents a mechanism for coalition formation in task oriented domains. The work presented in [16] investigates coalitions among self interested agents which need to solve combinatorial optimization problems. 7 Conclusion In this paper we have present a domain independent framework how inter agent cooperations can be made safe against defection. A cooperation is safe when it is in an ....

T. W. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94:99-- 137, 1997.

....on which other loads are won, and at what cost. For this reason, and for the sake of computational feasibility, we allow each agent to only place a bid for at most one load in each round of auctions. Our agents can thus be seen as computationally and rationally bounded (in the sense of [ 20, 30, 23 ] ) although they repair (some of) their non optimal local decisions through decommitment. Each piece of cargo is sold in a separate Vickrey auction. In this auction type, the highest bidder wins the contract but pays the second highest price. In our model, neither the number of participants ....

T. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Arti cial Intelligence, 94(1-2):99-137, 1997.

....the point is that agents cannot be assumed a priori to be cooperative and only interested in team goals. A more self interested approach to multiagent interaction and co ordination is the coalition. Coalitions have been discussed both within the context of economics [10] and multi agent systems [16, 9, 14, 22]. While some details vary among researchers, the standard definition of a coalition is a group of agents that have all agreed to work together to achieve a larger goal. Each agent is self interested, and by participating in the coalition, it will receive a higher utility than if it did not ....

Sandholm, T. W. and V. R. Lesser: 1997, 'Coalitions among Computationally Bounded Agents'. Artificial Intelligence 94(1), 99-137.

....agent systems operating in open environments where information is sparse and there is a lack of trust. This algorithmic approach contrasts with the cooperative (axiomatic) and non cooperative approaches of game theory that have been highly influential in the mechanism design tradition of MAS [23, 24, 43, 48, 58]. Rosenchein and Zlotkin used cooperative game theory to design negotiation mechanisms that maximize the social welfare function (the product of agent utilities, or the Nash solution) for task, state and worth oriented domains [43] Similarly, Sandholm, in addition to extending the Contract Net ....

T.W. Sandholm and V.R. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1):99--137, 1997.

....coalition structure and stable payoff division in coalition formation problems, and winner determination in combinatorial auction problems are computationally intractable. There is some research discussing the computational problems by computer scientists in each of these two fields (for example, [12, 13, 15, 19, 14] in coalition formation, and [31, 33, 26, 28, 29, 30, 32, 34] in winner determination in combinatorial auctions) but there is limited work to date on considering both behaviors simultaneously. In this paper we consider an electronic market in which both coalition formation and combinatorial ....

Tuomas W. Sandholm, Victor R. Lesser. Coalitions among Computationally Bounded Agents. Artificial Intelligence, 94(1): 99-137, 1997.

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Sandholm, T., Lesser, V. R., 1997. Coalitions among computationally bounded agents. Artificial Intelligence 94 (1), 99--137, special issue on Economic Principles of Multiagent Systems. Early version: IJCAI 1995, 662--669.

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Sandholm, T., and Lesser, V. R. 1997. Coalitions among computationally bounded agents. Artificial Intelligence 94(1):99--137. Early version appeared at the International Joint Conference on Artificial Intelligence (IJCAI), pages 662--669, 1995.

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Tuomas Sandholm and Victor Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1):99--137, 1997.

....which there is no improvement. Also, iterative improvement algorithms can switch to using different local search operators once progress has ceased using one operator (for example, once 2 swap has reached a local optimum in TSP, one can switch to 3 swap and obtain gains from deliberation again) [16] . v i = V = K. So we have found a solution to the PERFORMANCE PROFILES instance. On the other hand, suppose there is a solution to the PERFORMANCEPROFILES instance, that is, a vector (N 1 ,N 2 , N m)with K. Since spending more than c i v i deliberation steps on profile i is ....

Tuomas Sandholm and Victor Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94:99-137, 1997. Early version: IJCAI-95,662-669.

....to act rationally even if determining rational strategies is complex [ Rosenschein Zlotkin, 1994 ] Also, computational agents do not su#er from emotional irrationality. Finally, the bounded rationality of computational agents can be more systematically characterized than that of humans [ Sandholm Lesser, 1997 ] Whether a given communication protocol for self interested agents will lead to e#cient outcomes typically depends on the underlying strategic structure of these interactions. The traditional economic approach of mechanism design which studies first the set of equilibria is therefore of ....

....increasingly e#cient negotiation. In settings where the bargaining set, i.e. set of individually rational Pareto e#cient deals, is di#cult to construct for example due to a combinatorial number of possible deals [ Sandholm, 1993 ] or the computational complexity of evaluating any given deal [ Sandholm Lesser, 1997 ] the computational speed of automated agents can significantly enhance negotiation. Additional e#ciency can stem from the fact that computational agents can negotiate with large numbers of other agents quickly and virtually with no negotiation overhead. However, this paper showed that in ....

Sandholm, T., and Lesser, V. R. 1997. Coalitions among computationally bounded agents. Artificial Intelligence 94(1):99-- 137. Special issue on Economic Principles of Multiagent Systems.

....to minimize transportation costs (driven mileage) while still making all of its deliveries and maintaining constraints involving maximum route length, maximum load weight and volume, delivery of all parcels and insisting that vehicles return to a depot of its center. This problem is NP complete [17]. Figure 1: Small example problem instance of the multiagent vehicle routing problem. This instance has two dispatch centers represented in the figure by computer operators. They receive the delivery orders and route the vehicles. The light dispatch center has light tasks and trucks while the ....

T. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1):99--137, 1997.

....in our model, agents have to use their limited resources in order to determine exactly what they are bargaining over. 2. An example application To make the presentation more concrete, we now discuss an example domain where our methods are needed. Consider a distributed vehicle routing problem [33] with two geographically dispersed dispatch centers that are self interested companies (Fig. 1) Each center is responsible for certain tasks (deliveries) and has a certain set of resources (vehicles) to take care of them. So each agent representing a dispatch center has its own vehicles and ....

....(vehicles) to take care of them. So each agent representing a dispatch center has its own vehicles and delivery tasks. Each agent s individual problem is to minimize transportation costs (driven mileage) while still making all of its deliveries while honoring the following constraints [33]: The same source of complexity has been addressed [33] but that paper only studied outcomes, not the process or the agents strategies. It was also assumed that the algorithm s performance is deterministically known in advance. Finally, the agents had costly but unlimited computation, while ....

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T. Sandholm, V.R. Lesser, Coalitions among computationally bounded agents, Artificial Intelligence 94 (1) (1997.

....which there is no improvement. Also, iterative improvement algorithms can switch to using di#erent local search operators once progress has ceased using one operator (for example, once 2 swap has reached a local optimum in TSP, one can switch to 3 swap and obtain gains from deliberation again) [16]. that N i c i v i for all i. We now claim that I = i : N i c i is a solution to the KNAPSACK instance. First, using the fact that f j (N j ) 0 for all j #I, we have f i (N i )#K=V. Also, c i = N i c i ) N K=C V V=C. So we have found a ....

Tuomas Sandholm and Victor R Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1):99--137, 1997.

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T. Sandholm, V. Lesser, Coalitions among computationally bounded agents, Artificial Intelligence 94 (1) (1997) 99--137 (Special issue on Economic Principles of Multi-Agent Systems).

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Sandholm, T., and Lesser, V. 1997. Coalitions among computationally bounded agents. Artif. Intell. 94(1):99--137.

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T. Sandholm and V. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, Special issue on Economic Principles of Multiagent Systems(94(1)):99--137, 1997.

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T. Sandholm and V. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, Special issue on Economic Principles of Multiagent Systems(94(1)):99--137, 1997.

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T. Sandholm and V. Lesser. Coalitions Among Computationally Bounded Agents. Artificial Intelligence, 94(1-2), 1997.

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T. Sandholm and V. Lesser. Coalitions Among Computationally Bounded Agents. Artificial Intelligence, 94(1-2), 1997.

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T. Sandholm and V. Lesser. Coalitions Among Computationally Bounded Agents. Artificial Intelligence, 94(1-2), 1997.

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Tuomas W Sandholm and Victor R Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94(1--2):99--137, 1997.

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T. Sandholm and Lesser V. Coalitions among Computationally Bounded Agents. In Arti cial Intelligence, volume 94, pages 99-137, 1997. Special issue on Economic Principles of Multiagent Systems.

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T. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Artificial Intelligence, 94:99--134, 1997.

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Sandholm, T. W., and Lesser, V. R. 1997. Coalitions among computationally bounded agents. Arti#cial Intelligence 94#1#2#:99#137.

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