Abstract
Dynamic planning is a hot topic in autonomous computing. This work presents a novel approach of simulating swarm computing behaviour in a sandbox environment where swarms of robots are challenged to fight against each other with a goal of “conquering” any environment bases. Swarm strategies are being used which are decided, modified and applied at run time. Autonomous swarm agents seem surprisingly applicable to several problems where combined artificial intelligence agents are challenged to generate innovative solutions and evaluate them prior to proposing or adopting the best possible one. This work is applicable in areas where AI agents should make selections close to real time within a range of available options under a multi-constraint, multi-objective mission environment. Relevance to Business Process workflows is also presented and documented.
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References
Leake, D.B.: Case-based reasoning. In: Ralston, A., Reilly, E.D., Hemmendinger, D. (eds.) Encyclopedia of computer science, 4th edn, pp. 196–197. Wiley, Chichester (2003)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
Lorenzi, F., Santos, D.S., de Oliveira, D., Bazzan, A.L.C.: Task allocation in case-based recommender systems: a swarm intelligence approach. Information Science Reference, pp. 268–279 (2007)
Nouaouria, N., Boukadoum, M.: A particle swarm optimization approach for the case retrieval stage in CBR. In: Bramer, M., Petridis, M., Hopgood, A. (eds.) Research and Development in Intelligent Systems XXVII: Incorporating Applications and Innovations in Intelligent Systems XVIII Proceedings of AI-2010, pp. 209–222. Springer, London (2011). https://doi.org/10.1007/978-0-85729-130-1_15
Ben Yahia, N., Bellamine, N., Ben Ghezala, H.: Integrating fuzzy case-based reasoning and particle swarm optimization to support decision making. Int. J. Comput. Sci. Issues 9(3), 117–124 (2012)
O’ Connor, D., Kapetanakis, S., Floyd, M., Ontanon, S., Petridis, M.: RoboWars: autonomous swarm robotics using case-based reasoning. In: Petridis, M. (ed.) Proceedings of the 22nd UK CBR Workshop, Peterhouse, December 2017, pp. 5–10. Brighton Press (2017)
Sebestyénová, J.: CBR in agent-based decision support system. In: Acta Polytechnica Hungarica, vol. 4, no. 1, pp. 127–138. Andras Bako, Budapest Tech (2007)
De la Prieta, F., Bajo, J., Corchado, J.M.: A CBR approach to allocate computational resources within a cloud platform. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds.) Intelligent Distributed Computing IX. SCI, vol. 616, pp. 75–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25017-5_7
Manousakis-Kokorakis, V., Petridis, M., Kapetanakis, S.: Collaborative reasoning in workflow monitoring using a multi-agent architecture. J. Expert Update 15(1), 37–47 (2015)
Fu, J., Fu, Y.: Case-based reasoning and multi-agents for cost collaborative management in supply chain. Int. Workshop Inf. Electron. Procedia Eng. 29, 1088–1098 (2012)
Agorgianitis, I., Petridis, M., Kapetanakis, S., Fish, A.: Evaluating distributed methods for CBR systems for monitoring business process workflows. In: Proceeding of ICCBR 2016, Workshop on Reasoning About Time in CBR, Atlanta, GA, October 28–November 2, 2016, pp. 122–131 (2016)
Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: Case-based planning and execution for real-time strategy games. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 164–178. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74141-1_12
Ontañón, S: Case acquisition strategies for case-based reasoning in real-time strategy games. In: FLAIRS 2012. AAAI Press (2012)
Mishra, K., Ontañón, S., Ram, A.: Situation assessment for plan retrieval in real-time strategy games. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 355–369. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85502-6_24
Wender, S., Watson, I.: Combining case-based reasoning and reinforcement learning for unit navigation in real-time strategy game AI. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS (LNAI), vol. 8765, pp. 511–525. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11209-1_36
Bulitko, V., Bjornsson, Y., Lawrence, R.: Case-based subgoaling in real-time heuristic search for video game pathfinding. J. Artif. Intell. Res. 39, 269–300 (2010)
Floyd, M.W., Esfandiari, B.: Building learning by observation agents using jLOAF. In: Proceedings of Workshop on Case-Based Reasoning for Computer, ICCBR 2011, pp. 37–41, 12–15 September 2011
Acosta, E., Esfandiari, B., Floyd, M.W.: Feature selection for CBR in imitation of RoboCup agents: a comparative study. In: Proceedings of the Workshop on Case-Based Reasoning for Computer Games (Held at the 18th International Conference on Case-Based Reasoning), Alessandria, Italy, July 19–22, pp. 25–34 (2010)
Floyd, M.W., Esfandiari, B.: An active approach to automatic case generation. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS (LNAI), vol. 5650, pp. 150–164. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02998-1_12
Kapetanakis, S., Petridis, M., Knight, B., Ma, J., Bacon, L.: A case based reasoning approach for the monitoring of business workflows. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS (LNAI), vol. 6176, pp. 390–405. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14274-1_29
Kapetanakis, S., Petridis, M., Knight, B., Ma, J., Bacon, L.: Providing explanations for the intelligent monitoring of business workflows using case-based reasoning. In: Workshop Proceedings of ExACT-10 at ECAI 2010, Lisbon, Portugal (2010)
Kapetanakis, S., Petridis, M.: Evaluating a case-based reasoning architecture for the intelligent monitoring of business workflows. In: Montani, S., Jain, L.C. (eds.) Successful Case-based Reasoning Applications-2, pp. 43–54. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-38736-4_4
Niu, L., Zhuo, G.: An improved real algorithm for difficult path finding situation. In: Proceeding of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2008, Beijing, China, vol. 37 (2008)
Teodorovic, D., Šelmic, M., Mijatovic-Teodorovic, L.: Combining case-based reasoning with bee colony optimisation for dose planning in well differentiated thyroid cancer treatment. Expert Syst. Appl. 40(5), 2147–2155 (2013)
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We would like to thank UK Nuffield Research and the European Defence Agency (EDA) for providing support to the initial stages of this work.
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O’Connor, D., Kapetanakis, S., Samakovitis, G., Floyd, M., Ontañon, S., Petridis, M. (2018). Autonomous Swarm Agents Using Case-Based Reasoning. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_20
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