Learning of Behavior Trees for Autonomous Agents

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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 Parasuraman Petter Oegren

Citations