Decentralized Dynamic Task Allocation Using Overlapping Potential Games

This paper reports on a novel decentralized technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralized method for solving the approximating games that uses the distributed stochastic algorithm. Finally, we discuss an implementation of our approach to a task allocation problem in the RoboCup Rescue disaster management simulator. The results show that our technique performs comparably to a centralized task scheduler (within 6% on average), and also, unlike its centralized counterpart, it is robust to restrictions on the agents’ communication and observation ranges.

[1]  Jeff G. Schneider,et al.  Approximate solutions for partially observable stochastic games with common payoffs , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[2]  Archie C. Chapman.  Control of large distributed systems using games with pure strategy Nash equilibria , 2009 .

[3]  Nicholas R. Jennings,et al.  Sequential decision making with untrustworthy service providers , 2008, AAMAS.

[4]  Nicholas R. Jennings,et al.  Agent-based control systems , 2003 .

[5]  Marco Spuri,et al.  Deadline Scheduling for Real-Time Systems , 2011 .

[6]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[7]  Zoubir Mammeri,et al.  Scheduling in Real-Time Systems , 2002 .

[8]  Alex Rogers,et al.  A multi-agent simulation system for prediction and scheduling of aero engine overhaul , 2008, AAMAS.

[9]  Archie C. Chapman,et al.  A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems1 , 2011, The Knowledge Engineering Review.

[10]  Tiina Heikkinen,et al.  A potential game approach to distributed power control and scheduling , 2006, Comput. Networks.

[11]  Claudia V. Goldman,et al.  Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis , 2004, J. Artif. Intell. Res..

[12]  Nicholas R. Jennings,et al.  Games with Congestion-Averse Utilities , 2009, SAGT.

[13]  Nicholas R. Jennings,et al.  Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm , 2009, IJCAI.

[14]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[15]  Bo An,et al.  An Application of Automated Negotiation to Distributed Task Allocation , 2007 .

[16]  Sven Koenig,et al.  Sequential Bundle-Bid Single-Sale Auction Algorithms for Decentralized Control , 2007, IJCAI.

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

[18]  Nicholas R. Jennings,et al.  The price of democracy in coalition formation , 2009, AAMAS.

[19]  Koen V. Hindriks,et al.  Agent programming with temporally extended goals , 2009, AAMAS.

[20]  Bart Selman,et al.  Optimal Multi-Agent Scheduling with Constraint Programming , 2007, AAAI.

[21]  Boi Faltings,et al.  Coordinating Agent Plans Through Distributed Constraint Optimization , 2008 .

[22]  Sarvapali D. Ramchurn,et al.  An Anytime Algorithm for Optimal Coalition Structure Generation , 2014, J. Artif. Intell. Res..

[23]  C. Q. Lee,et al.  The Computer Journal , 1958, Nature.

[24]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[25]  Stephen J. Roberts,et al.  Decentralized predictive sensor allocation , 2008, 2008 47th IEEE Conference on Decision and Control.

[26]  L. Shapley,et al.  Potential Games , 1994 .

[27]  NICHOLAS R. JENNINGS,et al.  An agent-based approach for building complex software systems , 2001, CACM.

[28]  Hiroaki Kitano,et al.  RoboCup Rescue: search and rescue in large-scale disasters as a domain for autonomous agents research , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[29]  Amy Greenwald,et al.  Bid Determination in Simultaneous Auctions: A Case Study , 2001 .

[30]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[31]  Michael P. Wellman,et al.  Auction Protocols for Decentralized Scheduling , 2001, Games Econ. Behav..

[32]  Weixiong Zhang,et al.  Distributed Breakout vs . Distributed Stochastic : A Comparative Evaluation on Scan Scheduling , 2002 .

[33]  R. Rosenthal.  A class of games possessing pure-strategy Nash equilibria , 1973 .

[34]  T. Payne,et al.  Flexible service provisioning with advance agreements , 2008, AAMAS.

[35]  Nicholas R. Jennings,et al.  Bidding strategies for realistic multi-unit sealed-bid auctions , 2008, Autonomous Agents and Multi-Agent Systems.

[36]  Erol Gelenbe,et al.  A distributed decision support system for Building Evacuation , 2009, 2009 2nd Conference on Human System Interactions.

[37]  Sarvapali D. Ramchurn,et al.  2008 International Conference on Information Processing in Sensor Networks Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes , 2022 .

[38]  Archie C. Chapman,et al.  Benchmarking hybrid algorithms for distributed constraint optimisation games , 2010, Autonomous Agents and Multi-Agent Systems.

[39]  Nicholas R. Jennings,et al.  Bidding optimally in concurrent second-price auctions of perfectly substitutable goods , 2007, AAMAS '07.

[40]  Jason R. Marden,et al.  Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation , 2007 .

[41]  P. Dasgupta.  Trust as a commodity , 1988 .