Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption

In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is available, revoking resource allocations to tasks in progress if new opportunities arise that those resources are better suited to handle. Our multiagent model assigns a task agent to each task that must be completed and a proxy agent to each resource that is available. Preemption occurs when a task agent approaches a proxy agent with a sufficiently compelling need that the proxy agent determines the newcomer derives more benefit from the proxy agent’s resource than the task agent currently using that resource. Task agents reason about which resources to request based on a learning of churn and congestion. We compare to a well-established multiagent resource allocation framework that permits preemption under more conservative assumptions and show through simulation that our model allows for improved allocations through more permissive preemption. In all, we offer a novel approach for multiagent resource allocation that is able to cope well with dynamic task arrivals.

[1]  John A. Doucette,et al.  An Ex-Ante Rational Distributed Resource Allocation System using Transfer of Control Strategies for Preemption with Applications to Emergency Medicine , 2012 .

[2]  Franz Rothlauf,et al.  Agent-Based Patient Scheduling in Hospitals , 2006, Multiagent Engineering.

[3]  Robin Cohen,et al.  Why bother about bother : Is it worth it to ask the user ? , 2005 .

[4]  Torsten O. Paulussen,et al.  Dynamic Patient Scheduling in Hospitals , 2004 .

[5]  Michael Sonnenschein,et al.  On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty , 2012, 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems.

[6]  Sarvapali D. Ramchurn,et al.  A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems , 2011, AAAI.

[7]  Gerald Tesauro,et al.  Online Resource Allocation Using Decompositional Reinforcement Learning , 2005, AAAI.

[8]  Ana L. C. Bazzan,et al.  Distributed clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach , 2012, Appl. Soft Comput..

[9]  Hai Yang,et al.  ACM Transactions on Intelligent Systems and Technology - Special Section on Urban Computing , 2014 .

[10]  Robin Cohen,et al.  Distributed multiagent resource allocation with adaptive preemption for dynamic tasks , 2014, AAMAS.

[11]  Maria L. Gini,et al.  Tasks with cost growing over time and agent reallocation delays , 2014, AAMAS.

[12]  Michael Wooldridge,et al.  Adaptive task resources allocation in multi-agent systems , 2001, AGENTS '01.

[13]  Richard Alterman,et al.  An Adaptive Planner , 1986, AAAI.

[14]  Robin Cohen,et al.  A hybrid transfer of control model for adjustable autonomy multiagent systems , 2005, AAMAS '05.

[15]  Milind Tambe,et al.  Why the elf acted autonomously: towards a theory of adjustable autonomy , 2002, AAMAS '02.

[16]  Wolfgang Reif,et al.  A Trust- and Cooperation-Based Solution of a Dynamic Resource Allocation Problem , 2013, 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems.

[18]  Yingqian Zhang,et al.  Distributed task allocation in social networks , 2007, AAMAS '07.

[19]  Yann Chevaleyre,et al.  Issues in Multiagent Resource Allocation , 2006, Informatica.

[20]  G. Monahan.  State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 1982 .

[21]  Archie C. Chapman,et al.  Decentralised dynamic task allocation: a practical game: theoretic approach , 2009, AAMAS.