{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:16:41Z","timestamp":1761581801384,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T00:00:00Z","timestamp":1550620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modeled as agents in a multi-agent system) that feature an \u201cextreme simplicity\u201d of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rather\u2014a direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behavior of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviors of predators with sensor offset of 20\u00b0 are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations.<\/jats:p>","DOI":"10.3390\/info10020072","type":"journal-article","created":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T11:45:39Z","timestamp":1550663139000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evolution, Robustness and Generality of a Team of Simple Agents with Asymmetric Morphology in Predator-Prey Pursuit Problem"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4857-6968","authenticated-orcid":false,"given":"Milen","family":"Georgiev","sequence":"first","affiliation":[{"name":"Department of Information System Design, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Tanev","sequence":"additional","affiliation":[{"name":"Department of Information System Design, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsunori","family":"Shimohara","sequence":"additional","affiliation":[{"name":"Department of Information System Design, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Ray","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Oklahoma, Norman, Oklahoma, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TRO.2006.886841","article-title":"Optimal paths for landmark-based navigation by differential-drive vehicles with field-of-view constraints","volume":"23","author":"Bhattacharya","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TAC.2011.2158172","article-title":"Rendezvous without coordinates","volume":"57","author":"Yu","year":"2012","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tanev, I., Georgiev, M., Shimohara, K., and Ray, T. (2018, January 12\u201314). Evolving a Team of Asymmetric Predator Agents That Do Not Compute in Predator-Prey Pursuit Problem. Proceedings of the 18th International Conference on Artificial Intelligence: Methodology, Systems, Applications, Varna, Bulgaria.","DOI":"10.1007\/978-3-319-99344-7_22"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Georgiev, M., Tanev, I., and Shimohara, K. (2018, January 15\u201319). Coevolving behavior and morphology of simple agents that model small-scale robots. Proceedings of the Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan.","DOI":"10.1145\/3205651.3208306"},{"key":"ref_5","unstructured":"Gauci, M. (2014). Swarm Robotic Systems with Minimal Information Processing. [Ph.D. Thesis, University of Sheffield]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1177\/0278364914525244","article-title":"Self-organized aggregation without computation","volume":"33","author":"Gauci","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_7","unstructured":"Gauci, M., Chen, J., Li, W., Dodd, T.J., and Gro\u00df, R. (2014, January 5\u20139). Clustering objects with robots that do not compute. Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, Paris, France."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, A., Gauci, M., and Gro\u00df, R. (2017, January 4\u20138). Shepherding with Robots That Do Not Compute. Proceedings of the 14th European Conference on Artificial Life, Lyon, France.","DOI":"10.7551\/ecal_a_056"},{"key":"ref_9","unstructured":"Brown, D.S., Turner, R., Hennigh, O., and Loscalzo, S. (2016, January 7\u20139). Discovery and exploration of novel swarm behaviors given limited robot capabilities. Proceedings of the 13th International Symposium on Distributed Autonomous Robotic Systems, London, UK."},{"key":"ref_10","unstructured":"Benda, M., Jagannathan, V., and Dodhiawala, R. (1986). An Optimal Cooperation of Knowledge Sources, Boeing AI Center, Boeing Computer Services. Technical Report BCS-G2010-28."},{"key":"ref_11","unstructured":"Haynes, T., and Sen, S. (1995, January 20\u201325). Evolving behavioral strategies in predators and prey. Proceedings of the 1995 International Joint Conference on AI, Montreal, QC, Canada."},{"key":"ref_12","unstructured":"Luke, S., and Spector, L. (1996, January 28\u201331). Evolving Teamwork and Coordination with Genetic Programming. Proceedings of the First Annual Conference on Genetic Programming, Stanford, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10710-005-2989-6","article-title":"Evolution, generality and robustness of emerged surrounding behavior in continuous predators-prey pursuit problem","volume":"6","author":"Tanev","year":"2005","journal-title":"Genet. Progr. Evolvable Mach."},{"key":"ref_14","unstructured":"Rubenstein, M., Cabrera, A., Werfel, J., Habibi, G., McLurkin, J., and Nagpal, R. (2013, January 6\u201310). Collective transport of complex objects by simple robots: Theory and experiments. Proceedings of the 2013 International Conference on Autonomous Agents and Multiagent Systems, St. Paul, MN, USA."},{"key":"ref_15","unstructured":"Lien, J.M., Rodriguez, S., Malric, J.P., and Amato, N.M. (2005, January 18\u201322). Shepherding Behaviors with Multiple Shepherds. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain."},{"key":"ref_16","first-page":"1922","article-title":"Nanorobots, NEMS, and Nanoassembly","volume":"91","author":"Requicha","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3450","DOI":"10.1021\/acs.langmuir.7b00288","article-title":"Assembly and Speed in Ion-Exchange-Based Modular Phoretic Microswimmers","volume":"33","author":"Niu","year":"2017","journal-title":"Langmuir"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1002\/anie.200804704","article-title":"Schooling Behavior of Light-Powered Autonomous Micromotors in Water","volume":"48","author":"Ibele","year":"2009","journal-title":"Angewandte Chemie Int. Ed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1603449","DOI":"10.1002\/smll.201603449","article-title":"Assembly and Transport of Microscopic Cargos via Reconfigurable Photoactivated Magnetic Microdockers","volume":"13","author":"Tierno","year":"2017","journal-title":"Small"},{"key":"ref_20","unstructured":"Holland, J. (1975). Adaptation in Natural and Artificial Systems, The University of Michigan Press."},{"key":"ref_21","unstructured":"Goldberg, E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mitchell, M. (1998). An Introduction to Genetic Algorithms, MIT Press.","DOI":"10.7551\/mitpress\/3927.001.0001"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nolfi, S., and Floreano, D. (2000). Evolutionary Robotics: The Biology, Intelligence, and Technology of Selforganizing Machines, MIT Press.","DOI":"10.7551\/mitpress\/2889.001.0001"},{"key":"ref_24","unstructured":"Angeline, P.J.E., and Kinnear, K.E. (1994). Genetic Programming and Emergent Intelligence. Advances in Genetic Programming, MIT Press."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Georgiev, M., Tanev, I., and Shimohara, K. (2018, January 11\u201314). Performance Analysis and Comparison on Heterogeneous and Homogeneous Multi-Agent Societies in Correlation to Their Average Capabilities. Proceedings of the 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, Japan.","DOI":"10.23919\/SICE.2018.8492713"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Georgiev, M., Tanev, I., and Shimohara, K. (2018, January 15\u201319). Exploration of the effect of uncertainty in homogeneous and heterogeneous multi-agent societies with regard to their average characteristics. Proceedings of the Genetic and Evolutionary Computation Conference Companion GECCO (Companion), Kyoto, Japan.","DOI":"10.1145\/3205651.3208259"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/2\/72\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:33:33Z","timestamp":1760186013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/2\/72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,20]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["info10020072"],"URL":"https:\/\/doi.org\/10.3390\/info10020072","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2019,2,20]]}}}