caa a22 4500 606159819 CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1761-3 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1761-3 Combining NEAT and PSO for learning tactical human behavior [Elektronische Daten] [Gary Stein, Avelino Gonzalez, Clayton Barham] This article presents and discusses a machine learning algorithm called PIGEON used to build agents capable of displaying tactical behavior in various domains. Such tactical behavior can be relevant in military simulations and video games, as well as in everyday tasks in the physical world, such as driving an automobile. Furthermore, PIGEON displays good performance across two different approaches to learning (observational and experiential) and across multiple domains. PIGEON is a hybrid algorithm, combining NEAT and PSO in two different manners. The investigation described in this paper compares the performance of the two versions of PIGEON to each other as well as to NEAT and to PSO individually. These four machine learning algorithms are applied in two different approaches to learning—through observation of human performance and through experience, as well as in three distinct domain testbeds. The criteria used to compare them were high proficiency in task completion and rapid learning. Results indicate that overall, PIGEON worked best when NEAT and PSO are applied in an alternating manner. This combination was called PIGEON-Alternate, or simply Alternate. The two versions of the PIGEON algorithm, the tests conducted, the results obtained and the conclusions are described in detail. The Natural Computing Applications Forum, 2014 Neuroevolution nationallicence Particle Swarm Optimization nationallicence Machine learning nationallicence Tactical reasoning nationallicence Stein Gary Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut Gonzalez Avelino Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut Barham Clayton Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut Neural Computing and Applications Springer London 26/4(2015-05-01), 747-764 0941-0643 26:4<747 2015 26 521 https://doi.org/10.1007/s00521-014-1761-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1761-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Stein Gary Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut NATIONALLICENCE 700 1- Gonzalez Avelino Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut NATIONALLICENCE 700 1- Barham Clayton Intelligent Systems Laboratory, Computer Science Division, University of Central Florida, Orlando, FL, USA aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 747-764 0941-0643 26:4<747 2015 26 521