Modeling multi-robot task allocation with limited information as global game
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Continuous response threshold functions to coordinate collaborative tasks in multi-agent systems are commonly employed models in a number of fields including ethology, economics, and swarm robotics. Although empirical evidence exists for the response threshold model in predicting and matching swarm behavior for social insects, there has been no formal argument as to why natural swarms use this approach and why it should be used for engineering artificial ones. In this paper, we show, by formulating task allocation as a global game, that continuous response threshold functions used for communication-free task assignment result in system level Bayesian Nash equilibria. Building up on these results, we show that individual agents not only do not need to communicate with each other, but also do not need to model each other’s behavior, which makes this coordination mechanism accessible to very simple agents, suggesting a reason for their prevalence in nature and motivating their use in an engineering context.
KeywordsThreshold-based task allocation Swarm robotics Social insects Game theory Global games
A. Kanakia and N. Correll have been supported by NSF CAREER Grant #1150223. We are grateful for this support.
- Bonabeau, E., Sobkowski, A., Theraulaz, G., & Deneubourg, J.-L. (1997). Adaptive task allocation inspired by a model of division of labor in social insects. In Biocomputing and emergent computation: Proceedings of BCEC97 (pp. 36–45). World Scientific Press.Google Scholar
- Correll, N. (2007). Coordination schemes for distributed boundary coverage with a swarm of miniature robots: Synthesis, analysis and experimental validation. PhD thesis, Ecole Polytechnique Fédérale, Lausanne, CH.Google Scholar
- Correll, N. (2008). Parameter estimation and optimal control of swarm-robotic systems: A case study in distributed task allocation. In IEEE international conference on robotics and automation (ICRA) (pp. 3302–3307). IEEE.Google Scholar
- Dantu, K., Berman, S., Kate, B., & Nagpal, R. (2012). A comparison of deterministic and stochastic approaches for allocating spatially dependent tasks in micro-aerial vehicle collectives. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 793–800). IEEE.Google Scholar
- Gerkey, B. P., & Mataric, M. J. (2003). Multi-robot task allocation: Analyzing the complexity and optimality of key architectures. In IEEE international conference on robotics and automation (Vol. 3, pp. 3862–3868). IEEE.Google Scholar
- Grenager, T., Powers, R., & Shoham, Y. (2002). Dispersion games: General definitions and some specific learning results. In AAAI innovative applications of artificial intelligence conference (IAAI) (pp. 398–403). AAAI.Google Scholar
- Kalra, N., & Martinoli, A. (2006). Comparative study of market-based and threshold-based task allocation. In Distributed autonomous robotic systems 7 (pp. 91–101). Springer.Google Scholar
- Mather, T. W., Hsieh, M. A., & Frazzoli, E. (2010). Towards dynamic team formation for robot ensembles. In IEEE international conference on robotics and automation (ICRA) (pp. 4970–4975). IEEE.Google Scholar
- Morris, S. E., & Shin, H. S. (2000). Global games: Theory and applications. New Haven, CT: Cowles Foundation for Research in Economics.Google Scholar
- Pynadath, D. V., & Tambe, M. (2002). Multiagent teamwork: Analyzing the optimality and complexity of key theories and models. In Proceedings of the first international joint conference on autonomous agents and multiagent systems (AAMAS): Part 2 (pp. 873–880). ACM.Google Scholar
- Tumer, K., & Wolpert, D. (2004). A survey of collectives. In Collectives and the design of complex systems (pp. 1–42). Springer.Google Scholar