A combinatorial auction for collaborative planning

Hunsberger, Luke; Grosz, Barbara J.

doi:10.1109/icmas.2000.858447

Cited by 100 publications

(79 citation statements)

References 7 publications

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“…For example, Tidhar et al [40] and Tambe et al [38] performed role allocation based on matching of capabilities, while Hunsberger and Grosz [18] proposed the use of combinatorial auctions to decide on how roles should be assigned. Modi et al [24] showed how role allocation can be modeled as a distributed constraint optimization problem and applied it to the problem of tracking multiple moving targets using distributed sensors.…”

Section: Role Allocation and Reallocationmentioning

confidence: 99%

“…By enabling the RMTDP to focus its search on incomplete policies, and by providing ready-made decompositions, TOPs assist RMTDPs in quickly searching through the policy space, as illustrated in this section. We focus, in particular, on the problem of role allocation [18,24,40,11], a critical problem in teams. While the TOP provides an incomplete policy, keeping open the role allocation decision for each agent, the RMTDP policy search provides the optimal role-taking action at each of the role allocation decision points.…”

Section: Optimizing Role Allocationmentioning

confidence: 99%

“…This approach focuses on the analysis of BDI team plans, to provide feedback to human developers on how the team plans can be improved. In particular, it focuses on the critical challenge of role allocation in building teams [40,18], i.e. which agents to allocate to the various roles in the team.…”

Section: Introductionmentioning

confidence: 99%

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Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Nair

Tambe

2005

Lecture Notes in Computer Science

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Abstract. Distributed partially observable Markov decision problems (POMDPs) have emerged as a popular decision-theoretic approach for planning for multiagent teams, where it is imperative for the agents to be able to reason about the rewards (and costs) for their actions in the presence of uncertainty. However, finding the optimal distributed POMDP policy is computationally intractable (NEXPComplete). This paper is focussed on a principled way to combine the two dominant paradigms for building multiagent team plans, namely the "belief-desireintention" (BDI) approach and distributed POMDPs. In this hybrid BDI-POMDP approach, BDI team plans are exploited to improve distributed POMDP tractability and distributed POMDP-based analysis improves BDI team plan performance. Concretely, we focus on role allocation, a fundamental problem in BDI teams -which agents to allocate to the different roles in the team. The hybrid BDI-POMDP approach provides three key contributions. First, unlike prior work in multiagent role allocation, we describe a role allocation technique that takes into account future uncertainties in the domain. The second contribution is a novel decomposition technique, which exploits the structure in the BDI team plans to significantly prune the search space of combinatorially many role allocations. Our third key contribution is a significantly faster policy evaluation algorithm suited for our BDI-POMDP hybrid approach. Finally, we also present experimental results from two domains: mission rehearsal simulation and RoboCupRescue disaster rescue simulation. In the RoboCupRescue domain, we show that the role allocation technique presented in this paper is capable of performing at human expert levels by comparing with the allocations chosen by humans in the actual RoboCupRescue simulation environment.

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Section: Role Allocation and Reallocationmentioning

confidence: 99%

Section: Optimizing Role Allocationmentioning

confidence: 99%

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Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Nair

Tambe

2005

Lecture Notes in Computer Science

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“…Suppose Computer Producer Agent has received the following two tasks in the same scheduling time window 6 The agent's local scheduler 8 reasons about these two new tasks according to the above information: their earliest start times, deadline, estimated process times and the rewards. It then generates the following agenda which includes the following tasks: Agenda 2.1 [10,26] Purchase Computer A [26,46] Purchase Computer B In this agenda, task Purchase Computer A is scheduled during time range [10,26], and task Purchase Computer B is scheduled during time range [26,46].…”

Section: Detailed Example Of a Multi-linked Negotiation Problemmentioning

confidence: 99%

“…A quantity requirement may be necessary to specify how much resource is needed. 6 The agent will not schedule every time a new task arrives, but will schedule all tasks that fall into the same scheduling time window. 7 The task cannot be started until the contract has been confirmed.…”

Section: Detailed Example Of a Multi-linked Negotiation Problemmentioning

confidence: 99%

Efficient Management of Multi-Linked Negotiation Based on a Formalized Model

Zhang

Lesser²,

Abdallah³

2005

Auton Agent Multi-Agent Syst

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A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important for the next generation of advanced multi-agent systems.However, most current negotiation research looks only at a single negotiation and thus does not present techniques to manage and reason about multi-linked negotiations. In this paper, we first present a technique based on the use of a partial-order schedule and a measure of the schedule, called flexibility, which enables an agent to reason explicitly about the interactions among multiple negotiations. Next, we introduce a formalized model of the multi-linked negotiation problem. Based on this model, a heuristic search algorithm is developed for finding a near-optimal ordering of negotiation issues and their parameters. Using this algorithm, an agent can evaluate and compare different negotiation approaches and choose the best one. We show how an agent uses this technology to effectively manage interacting negotiation issues. Experimental work is presented which shows the efficiency of this approach.

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A combinatorial auction among experts and amateurs: The case of single-skilled experts

2005

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SUMMARYAuctions have recently commanded a great deal of attention in the field of multiagent systems. Correctly judging the quality of auctioned goods is often difficult for amateurs, in particular, on the Internet auctions. We have formalized such a situation so that Nature selects the quality of the auctioned good. Experts can observe Nature's selection (i.e., the quality of the good) correctly, while amateurs, including the auctioneer, cannot. In other words, the information on Nature's selection is asymmetric between experts and amateurs. In this situation, it is difficult to attain an efficient allocation, since experts have a clear advantage over amateurs, and they would not reveal their valuable information without some reward. We have succeeded in developing a single-unit auction protocol in which truthtelling is a dominant strategy for each expert. In this paper, we focus on a combinatorial auction protocol under asymmetric information on Nature's selection. Experts may have an interest in, and expert knowledge on, Nature's selection for several goods, that is, experts are versatile. However, the case of versatile experts is very complicated. Thus, as a first step, we assume experts to have an interest in, and expert knowledge on, a single good. That is, experts are singleskilled. Under these assumptions, we develop an auction protocol in which the dominant strategy for experts is truth-telling. Also, for amateurs, truth-telling is the best response when experts tell the truth. By making experts to elicit their information on the quality of the goods, the protocol can achieve a socially desirable, (i.e., Pareto efficient) allocation, if certain assumptions are satisfied.

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A combinatorial auction for collaborative planning

Abstract: When rational, utility-maximizing agents encounter an opportunity to collaborate on a group activity, they must determine whether to commit to that activity. We refer to this problem as the initial-commitment decision problem (ICDP

Cited by 100 publications

References 7 publications

Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Efficient Management of Multi-Linked Negotiation Based on a Formalized Model

A combinatorial auction among experts and amateurs: The case of single-skilled experts

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