Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note

Nair, Ranjit; Ito, Takayuki; Tambe, Milind; Marsella, Stacy

doi:10.1007/3-540-45603-1_129

Cited by 52 publications

(51 citation statements)

References 2 publications

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“…The police center passes the information to all the police agents, who submit bids. Combinatorial auctions, also used by [8], achieve optimal task allocation but require large computational power and message bandwidth. The paper compares auctions with a distributed mechanism using localized reasoning.…”

Section: Related Workmentioning

confidence: 99%

Decision and Coordination Strategies for RoboCup Rescue Agents

Nanjanath

Erlandson

Andrist

et al. 2010

Simulation, Modeling, and Programming for Autonomous Robots

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Abstract. We describe the decision processes of agents in the Robocup Rescue Agent Simulation. Agents have to rescue civilians trapped in buildings and extinguish fires in a city which has been struck by an earthquake. Lack of information and limited communications hamper the rescue process. We examine how effective our strategies and algorithms are and compare their performance against the baseline agents and agents which competed in last year's competition.

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Section: Related Workmentioning

confidence: 99%

Decision and Coordination Strategies for RoboCup Rescue Agents

Nanjanath

Erlandson

Andrist

et al. 2010

Simulation, Modeling, and Programming for Autonomous Robots

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“…(These individual plans get the fire engines and ambulances to move through the streets using specific search algorithms. However, these individual plans are not relevant for our discussions in this paper; interested readers should refer to the description of our RoboCupRescue team entered into the RoboCup competitions of 2001 [25].) The organizational hierarchy consists of Task Force comprising of two Engine sub-teams, one for each fire and an Ambulance Team, where the engine teams are assigned to extinguishing the fires while the ambulance team is assigned to rescuing civilians.…”

Section: Extinguishfire1mentioning

confidence: 99%

“…RescueISI: Allocations used by the our RoboCupRescue agents that were entered in the RoboCupRescue competitions of 2001 [25] (RescueISI), where they finished in third place. These agents used local reasoning for their decision making, ignoring transitional as well and observational uncertainty.…”

Section: Allocation In Robocuprescuementioning

confidence: 99%

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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“…Key teamwork algorithms have evolved from an extensive body of work on both the theory and practice of teamwork [23,8,3]. Teams of heterogeneous actors have potential applications in a wide variety of fields, ranging from supporting human collaboration [1,22] to disaster response [16] to manufacturing [9] to training [23] to games [10]. To date we have demonstrated teams of 500 software agents [21], in both a UAV simulation [19] and a disaster response simulation, but teams of as many as 200,000 agents are envisioned.…”

Section: Teamwork and Machinettamentioning

confidence: 99%

Comparing Three Approaches to Large-Scale Coordination

Scerri

Vincent

Mailler

Coordination of Large-Scale Multiagent Systems

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Summary. Coordination of large groups of agents or robots is starting to reach a level of maturity where prototype systems can be built and tested in realistic environments. These more realistic systems require that both algorithmic and practical issues are addressed in an integrated solution. In this chapter, we look at three implementations of large-scale coordination examining common issues, approaches, and open problems. The key result of the comparison is that there is a surprising degree of commonality between the independently developed approaches, in particular the use of partial, dynamic centralization. Conversely, open issues and problems encountered varied greatly with the notable exception that debugging was a major issue for each approach.

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Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note

Cited by 52 publications

References 2 publications

Decision and Coordination Strategies for RoboCup Rescue Agents

Decision and Coordination Strategies for RoboCup Rescue Agents

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

Comparing Three Approaches to Large-Scale Coordination

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