Learning of Behavior Trees for Autonomous Agents

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Colledanchise:2018:ieeeTOG,

• author = "Michele Colledanchise and Ramviyas Nattanmai Parasuraman and Petter Ogren",
• journal = "IEEE Transactions on Games",
• title = "Learning of Behavior Trees for Autonomous Agents",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Games, Genetics, Heuristic algorithms, Planning, Safety, Stochastic processes",
• ISSN = "2475-1502",
• DOI = "doi:10.1109/TG.2018.2816806",
• abstract = "we study the problem of automatically synthesizing a successful Behaviour Tree (BT) in an a-priori unknown dynamic environment. Starting with a given set of behaviours, 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 generalise And-Or-Trees and also provide very natural chromosome mappings for genetic programming, 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.",
• notes = "Also known as \cite{8319483}",

}

Genetic Programming entries for Michele Colledanchise Ramviyas Nattanmai Parasuraman Petter Ogren

Citations