Learning Behavior Trees by Evolution-Inspired Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.7686

@InProceedings{deng:2023:GECCOcomp,

• author = "Chuanshuai Deng and Chenjing Zhao and Zhenghui Liu and Jiexin Zhang and Yunlong Wu and Yanzhen Wang and Hong Cheng and Xiaodong Yi",
• title = "Learning Behavior Trees by {Evolution-Inspired} Approaches",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "275--278",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, behavior tree: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590642",
• size = "4 pages",
• abstract = "As a reactive and modular policy control architecture, Behavior Tree (BT) has been used in computer games and robotics for autonomous agents' task switching. However, constructing BTs manually for complex tasks requires expert domain-knowledge and is error-prone. As a solution, researchers have proposed to auto-construct BTs using evolutionary algorithms such as Genetic Programming (GP) and Grammatical Evolution (GE). Nevertheless, their effectiveness in practical situations is in doubt and there are different drawbacks in the application.In this paper, we present a novel BT evolutionary system that integrates both GE and GP as modules and auto-checks the complexity of a given task to select which module to use. In addition, our system collects BTs that are either previously generated or manually designed by the user, which are utilized to further improve the convergence speed and the quality of generated trees for new tasks.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Chuanshuai Deng Chenjing Zhao Zhenghui Liu Jiexin Zhang Yunlong Wu Yanzhen Wang Hong Cheng Xiaodong Yi

Citations