Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
COLLAGEN: when agents collaborate with people
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Artificial Intelligence - Special issue on Robocop: the first step
Dynamic reorganization of decision-making groups
Proceedings of the fifth international conference on Autonomous agents
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Adaptive task resources allocation in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem
Proceedings of the 2002 ACM symposium on Applied computing
Multi-agent policies: from centralized ones to decentralized ones
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Building Dynamic Agent Organizations in Cyberspace
IEEE Internet Computing
Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note
RoboCup 2001: Robot Soccer World Cup V
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans
Autonomous Agents and Multi-Agent Systems
Transition-independent decentralized markov decision processes
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A prototype infrastructure for distributed robot-agent-person teams
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Role allocation and reallocation in multiagent teams: towards a practical analysis
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Combinatorial Auction for Collaborative Planning
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Communication for Improving Policy Computation in Distributed POMDPs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
CAST: collaborative agents for simulating teamwork
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Methods for task allocation via agent coalition formation
Artificial Intelligence
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Quantitative modeling of complex computational task environments
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Learning to cooperate via policy search
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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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 (NEXP-Complete). This paper is focussed on a principled way to combine the two dominant paradigms for building multiagent team plans, namely the “belief-desire-intention” (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.