{"type":"ScholarlyArticle","title":"Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees","authors":[{"name":"Aadesh Neupane","slug":"aadesh-neupane","given":"Aadesh","family":"Neupane","affiliation":"Brigham Young University , Provo , UT"},{"name":"Michael Goodrich","slug":"michael-goodrich","given":"Michael","family":"Goodrich","affiliation":"Brigham Young University , Provo , UT"}],"abstract":"Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behaviors but may not scale to large problems. This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types, which overcomes some of the limitations in using GEs to evolve swarm behavior. Given human-provided, problem-specific fitness-functions, the learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We empirically verify the algorithm's effectiveness on three different problems: single-source foraging, collective transport, and nest maintenance. Agent diversity is key for the evolved behaviors to outperform hand-coded solutions in each task.","doi":"10.24963/ijcai.2019/73","url":"https://ijcai.org/proceedings/2019/73","pdf":"https://ijcai.org/proceedings/2019/0073.pdf","markdown":"https://ijcai.org/proceedings/2019/73.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/73","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":73,"pages":{"first":513,"last":520},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Learning","Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence"],"videos":[],"references":[{"position":1,"raw":"[Agogino and Tumer, 2004] A. 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