UT Austin Villa Publications

Sorted by DateClassified by Publication TypeClassified by TopicSorted by First Author Last Name

Making Friends on the Fly: Cooperating with New Teammates

Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone. Making Friends on the Fly: Cooperating with New Teammates. Artificial Intelligence, October 2016.
Official version from journal website.

Download

[PDF]917.9kB  [HTML] 

Abstract

Robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, they will increasingly need to interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. This article focuses on a limited version of the ad hoc teamwork problem in which an agent knows the environmental dynamics and has had past experiences with other teammates, though these experiences may not be representative of the current teammates. To tackle this problem, this article introduces a new general-purpose algorithm, PLASTIC, that reuses knowledge learned from previous teammates or provided by experts to quickly adapt to new teammates. This algorithm is instantiated in two forms: 1) PLASTIC-Model -- which builds models of previous teammates' behaviors and plans behaviors online using these models and 2) PLASTIC-Policy -- which learns policies for cooperating with previous teammates and selects among these policies online. We evaluate PLASTIC on two benchmark tasks: the pursuit domain and robot soccer in the RoboCup 2D simulation domain. Recognizing that a key requirement of ad hoc teamwork is adaptability to previously unseen agents, the tests use more than 40 previously unknown teams on the first task and 7 previously unknown teams on the second. While PLASTIC assumes that there is some degree of similarity between the current and past teammates' behaviors, no steps are taken in the experimental setup to make sure this assumption holds. The teammates were created by a variety of independent developers and were not designed to share any similarities. Nonetheless, the results show that PLASTIC was able to identify and exploit similarities between its current and past teammates' behaviors, allowing it to quickly adapt to new teammates.

BibTeX

@article{AIJ16-Barrett,
AUTHOR={Samuel Barrett and Avi Rosenfeld and Sarit Kraus and Peter Stone},     
TITLE={Making Friends on the Fly: Cooperating with New Teammates},
JOURNAL={Artificial Intelligence},
YEAR={2016},
month={October},
URL={http://www.sciencedirect.com/science/article/pii/S0004370216301266},
DOI={10.1016/j.artint.2016.10.005},
ISSN={},
ABSTRACT={
  Robots are being deployed in an increasing variety of environments for longer
  periods of time.  As the number of robots grows, they will increasingly need
  to interact with other robots.  Additionally, the number of companies and
  research laboratories producing these robots is increasing, leading to the
  situation where these robots may not share a common communication or
  coordination protocol.  While standards for coordination and communication
  may be created, we expect that robots will need to additionally reason
  intelligently about their teammates with limited information.  This problem
  motivates the area of ad hoc teamwork in which an agent may potentially
  cooperate with a variety of teammates in order to achieve a shared goal.
  This article focuses on a limited version of the ad hoc teamwork problem in
  which an agent knows the environmental dynamics and has had past experiences
  with other teammates, though these experiences may not be representative of
  the current teammates.  To tackle this problem, this article introduces a new
  general-purpose algorithm, PLASTIC, that reuses knowledge learned from
  previous teammates or provided by experts to quickly adapt to new teammates.
  This algorithm is instantiated in two forms:
  1) PLASTIC-Model -- which builds models of previous teammates' behaviors and
    plans behaviors online using these models
  and
  2) PLASTIC-Policy -- which learns policies for cooperating with previous
    teammates and selects among these policies online.
  We evaluate PLASTIC on two benchmark tasks: the pursuit domain and robot soccer
  in the RoboCup 2D simulation domain.  Recognizing that a key requirement of ad
  hoc teamwork is adaptability to previously unseen agents, the tests use more
  than 40 previously unknown teams on the first task and 7 previously unknown
  teams on the second.  While PLASTIC assumes that there is some degree of
  similarity between the current and past teammates' behaviors, no steps are
  taken in the experimental setup to make sure this assumption holds.  The
  teammates were created by a variety of independent developers and were not
  designed to share any similarities.  Nonetheless, the results show that PLASTIC
  was able to identify and exploit similarities between its current and past
  teammates' behaviors, allowing it to quickly adapt to new teammates.
},
wwwnote={<a href="https://authors.elsevier.com/a/1T~PH-c5CBBI">Official version</a> from journal website.},
}

Generated by bib2html.pl (written by Patrick Riley ) on Thu Oct 12, 2017 13:11:26