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Learning of Behavior Trees for Autonomous Agents
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-7714-928X
2018 (English)In: IEEE Transactions on Games, ISSN 2475-1502Article in journal (Refereed) Published
Abstract [en]

In this paper, we study the problem of automatically synthesizing a successful Behavior Tree (BT) in an a-priori unknown dynamic environment. Starting with a given set of behaviors, a reward function, and sensing in terms of a set of binary conditions, the proposed algorithm incrementally learns a switching structure in terms of a BT, that is able to handle the situations encountered. Exploiting the fact that BTs generalize And-Or-Trees and also provide very natural chromosome mappings for genetic pro- gramming, we combine the long term performance of Genetic Programming with a greedy element and use the And-Or analogy to limit the size of the resulting structure. Finally, earlier results on BTs enable us to provide certain safety guarantees for the resulting system. Using the testing environment Mario AI we compare our approach to alternative methods for learning BTs and Finite State Machines. The evaluation shows that the proposed approach generated solutions with better performance, and often fewer nodes than the other two methods.

Place, publisher, year, edition, pages
IEEE Press, 2018.
National Category
Robotics
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-224667DOI: 10.1109/TG.2018.2816806OAI: oai:DiVA.org:kth-224667DiVA, id: diva2:1192221
Note

QC 20180326

Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-26Bibliographically approved

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