Learning of Behavior Trees for Autonomous Agents

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Misc{oai:arXiv.org:1504.05811,

• author = "Michele Colledanchise and Ramviyas Parasuraman and Petter Oegren",
• title = "Learning of Behavior Trees for Autonomous Agents",
• year = "2015",
• month = apr # "~22",
• abstract = "Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behaviour Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.",
• bibsource = "OAI-PMH server at export.arxiv.org",
• oai = "oai:arXiv.org:1504.05811",
• keywords = "genetic algorithms, genetic programming, computer science - robotics, computer science - artificial intelligence, computer science - learning",
• URL = "http://arxiv.org/abs/1504.05811",

}

Genetic Programming entries for Michele Colledanchise Ramviyas Nattanmai Parasuraman Petter Oegren

Citations