ABSTRACT
This paper presents a grammar-based evolutionary model to facilitate autonomous emergence of task allocation for intelligent multi-agent systems. The approach adopts a context-free grammar to determine the behaviour rule syntax. This allows for flexibility in evolving task allocation under multiple and dynamic constraints without manual rule design and parameter tuning. Experimental evaluations conducted with a target discovery simulation illustrate that the grammar-based model performs successfully in both dynamic and non-dynamic conditions. A statistically significant performance improvement is shown compared to an algorithm developed with the broadcast of local eligibility mechanism and a genetic programming mechanism. Grammatical evolution can achieve near-optimal solutions under restrictions applied on the number of agents, targets and the time allowed. Further, analysis of the evolved rule structures shows that grammatical evolution can identify less complex rule structures for behaviours while maintaining the expected level of performance. The results infer that the proposed model is a promising alternative for dynamic task allocation with human interactions in complex real-world domains.
- E. T. Alotaibi, S. S. Alqefari, and A. Koubaa. 2019. LSAR: Multi-UAV Collaboration for Search and Rescue Missions. IEEE Access 7 (2019), 55817--55832.Google ScholarCross Ref
- C. Banks, S. Wilson, S. Coogan, and M. Egerstedt. 2020. Multi-Agent Task Allocation using Cross-Entropy Temporal Logic Optimization. In 2020 IEEE International Conference on Robotics and Automation (ICRA). 7712--7718.Google Scholar
- Y Bestaoui Sebbane. 2020. Multi-UAV Planning and Task Allocation. Chapman and Hall/CRC, New York.Google Scholar
- Davide Castelvecchi. 2016. Can we open the black box of AI? Nature News 538, 7623 (2016), 20.Google ScholarCross Ref
- Rongxin Cui, Ji Guo, and Bo Gao. 2013. Game theory-based negotiation for multiple robots task allocation. Robotica 31, 6 (2013), 923.Google ScholarCross Ref
- P. D'haeseleer. 1994. Context preserving crossover in genetic programming. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. 256-261 vol.1. https://doi.org/10.1109/ICEC.1994.350006Google ScholarCross Ref
- R. Doriya, S. Mishra, and S. Gupta. 2015. A brief survey and analysis of multi-robot communication and coordination. In International Conference on Computing, Communication Automation. 1014--1021.Google Scholar
- Agoston E Eiben and James E Smith. 2003. Introduction to evolutionary computing. Vol. 53. Springer. Google ScholarDigital Library
- Eliseo Ferrante, Edgar Duéñez Guzmán, Ali Emre Turgut, and Tom Wenseleers. 2013. GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics. In Proc. 15th Annu. Conf. Genetic and Evolutionary Computation (Amsterdam, The Netherlands) (GECCO '13). Association for Computing Machinery, New York, NY, USA, 17--24. https://doi.org/10.1145/2463372.2463385 Google ScholarDigital Library
- John Harwell. 2020. A Theoretical Framework For Self-Organized Task Allocation in Large Swarms. In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. 2191--2192. Google ScholarDigital Library
- Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. 1996. Reinforcement learning: A survey. Journal of artificial intelligence research 4 (1996), 237--285. Google ScholarCross Ref
- Nikolaos Kariotoglou, Davide M. Raimondo, Sean J. Summers, and John Lygeros. 2014. Multi-Agent Autonomous Surveillance: A Framework Based on Stochastic Reachability and Hierarchical Task Allocation. Journal of Dynamic Systems, Measurement, and Control 137, 3 (10 2014). 031008.Google Scholar
- AM Senthil Kumar and M Venkatesan. 2019. Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Computing 22, 1 (2019), 2179--2185.Google ScholarCross Ref
- E. Lakshika, M. Barlow, and A. Easton. 2013. Co-evolving semi-competitive interactions of sheepdog herding behaviors utilizing a simple rule-based multi agent framework. In Proc. 2013 IEEE Symp. Artificial Life (ALife). 82--89. https://doi.org/10.1109/ALIFE.2013.6602435Google ScholarCross Ref
- Nam Le, Anthony Brabazon, and Michael O'Neill. 2019. The Evolution of Self-taught Neural Networks in a Multi-agent Environment. In Applications of Evolutionary Computation, Paul Kaufmann and Pedro A. Castillo (Eds.). Springer International Publishing, Cham, 457--472.Google Scholar
- T. J. McCabe. 1976. A Complexity Measure. IEEE Transactions on Software Engineering SE-2, 4 (1976), 308--320. Google ScholarDigital Library
- James E. Murphy, Michael O'Neill, and Hamish Carr. 2009. Exploring Grammatical Evolution for Horse Gait Optimisation. In Genetic Programming, Leonardo Vanneschi, Steven Gustafson, Alberto Moraglio, Ivanoe De Falco, and Marc Ebner (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 183--194. Google ScholarDigital Library
- Aadesh Neupane and Michael Goodrich. 2019. Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees. In Procc. 28th Int. Joint Conf. Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 513--520. https://doi.org/10.24963/ijcai.2019/73 Google ScholarDigital Library
- M. O'Neill and C. Ryan. 2001. Grammatical evolution. IEEE Transactions on Evolutionary Computation 5, 4(Aug2001), 349--358. https://doi.org/10.1109/4235.942529 Google ScholarDigital Library
- Diego Perez, Miguel Nicolau, Michael O'Neill, and Anthony Brabazon. 2011. Evolving Behaviour Trees for the Mario AI Competition Using Grammatical Evolution. In Applications of Evolutionary Computation, Di Chio et al. (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 123--132. Google ScholarDigital Library
- Diego Perez-Liebana and Miguel Nicolau. 2018. Evolving Behaviour Tree Structures Using Grammatical Evolution. In Handbook of Grammatical Evolution, Conor Ryan, Michael O'Neill, and JJ Collins (Eds.). Springer International Publishing, Cham, 433--460. https://doi.org/10.1007/978-3-319-78717-6_18Google Scholar
- Riccardo Poli, William B. Langdon, and Nicholas Freitag McPhee. 2008. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. Google ScholarDigital Library
- Dilini Samarasinghe, Michael Barlow, Erandi Lakshika, and Kathryn Kasmarik. 2021. Exploiting abstractions for grammar-based learning of complex multi-agent behaviours. International Journal of Intelligent Systems (2021). https://doi.org/10.1002/int.22550 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22550Google Scholar
- Dilini Samarasinghe, Erandi Lakshika, Michael Barlow, and Kathryn Kasmarik. 2018. Automatic Synthesis of Swarm Behavioural Rules from Their Atomic Components. In Proceedings of the Genetic and Evolutionary Computation Conference (Kyoto, Japan) (GECCO '18). ACM, New York, NY, USA, 133--140. Google ScholarDigital Library
- Janaína Schwarzrock, Iulisloi Zacarias, Ana L.C. Bazzan, Ricardo Queiroz de Araujo Fernandes, Leonardo Henrique Moreira, and Edison Pignaton de Freitas. 2018. Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence. Engineering Applications of Artificial Intelligence 72 (2018), 10 - 20. Google ScholarDigital Library
- Onn Shehory and Sarit Kraus. 1998. Methods for task allocation via agent coalition formation. Artificial Intelligence 101, 1 (1998), 165 - 200. Google ScholarDigital Library
- Jieke Shi, Zhou Yang, and Junwu Zhu. 2020. An auction-based rescue task allocation approach for heterogeneous multi-robot system. Multimedia Tools and Applications 79, 21 (2020), 14529--14538.Google ScholarCross Ref
- V. Singhal and D. Dahiya. 2015. Distributed task allocation in dynamic multi-agent system. In International Conference on Computing, Communication Automation. 643--648.Google Scholar
- John Mark Swafford, Michael O'Neill, Miguel Nicolau, and Anthony Brabazon. 2011. Exploring Grammatical Modification with Modules in Grammatical Evolution. In Genetic Programming, Sara Silva, James A. Foster, Miguel Nicolau, Penousal Machado, and Mario Giacobini (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 310--321. Google ScholarDigital Library
- Ardi et al. Tampuu. 2017. Multiagent cooperation and competition with deep reinforcement learning. PLOS ONE 12, 4(042017), 1--15. https://doi.org/10.1371/journal.pone.0172395Google Scholar
- N. Tsiogkas, G. Papadimitriou, Z. Saigol, and D. Lane. 2014. Efficient multi-AUV cooperation using semantic knowledge representation for underwater archaeology missions. In 2014 Oceans - St. John's. 1--6.Google Scholar
- Barry Brian Werger and Maja J. Mataric. 2000. Broadcast of Local Eligibility: Behavior-Based Control for Strongly Cooperative Robot Teams. In Proceedings of the Fourth International Conference on Autonomous Agents (Barcelona, Spain) (AGENTS '00). Association for Computing Machinery, New York, NY, USA, 21--22. Google ScholarDigital Library
- Vahid Yazdanpanah, Mehdi Dastani, Shaheen Fatima, Nicholas R Jennings, Devrim M Yazan, and Henk Zijm. 2020. Task Coordination in Multiagent Systems. In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. 2056--2058. Google ScholarDigital Library
Index Terms
- Task Allocation in Multi-Agent Systems with Grammar-Based Evolution
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