Samuel Barrett's Publications

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Making Friends on the Fly: Advances in Ad Hoc Teamwork

Samuel Barrett. Making Friends on the Fly: Advances in Ad Hoc Teamwork. Ph.D. Thesis, The University of Texas at Austin, Austin, Texas, USA, 2014.

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Abstract

Given the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, it is expected that they will encounter and 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 any standards will lag behind the state-of-the-art protocols and 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. We argue that agents that effectively reason about ad hoc teamwork need to exhibit three capabilities: 1) robustness to teammate variety, 2) robustness to diverse tasks, and 3) fast adaptation. This thesis focuses on addressing all three of these challenges. In particular, this thesis introduces algorithms for quickly adapting to unknown teammates that enable agents to react to new teammates without extensive observations. The majority of existing multiagent algorithms focus on scenarios where all agents share coordination and communication protocols. While previous research on ad hoc teamwork considers some of these three challenges, this thesis introduces a new algorithm, PLASTIC, that is the first to address all three challenges in a single algorithm. PLASTIC adapts quickly to unknown teammates by reusing knowledge it learns about previous teammates and exploiting any expert knowledge available. Given this knowledge, PLASTIC selects which previous teammates are most similar to the current ones online and uses this information to adapt to their behaviors. This thesis introduces two instantiations of PLASTIC. The first is a model-based approach, PLASTIC-Model, that builds models of previous teammates' behaviors and plans online to determine the best course of action. The second uses a policy-based approach, PLASTIC-Policy, in which it learns policies for cooperating with past teammates and selects from among these policies online. Furthermore, we introduce a new transfer learning algorithm, TwoStageTransfer, that allows transferring knowledge from many past teammates while considering how similar each teammate is to the current ones. We theoretically analyze the computational tractability of PLASTIC-Model in a number of scenarios with unknown teammates. Additionally, we empirically evaluate PLASTIC in three domains that cover a spread of possible settings. Our evaluations show that PLASTIC can learn to communicate with unknown teammates using a limited set of messages, coordinate with externally-created teammates that do not reason about ad hoc teams, and act intelligently in domains with continuous states and actions. Furthermore, these evaluations show that TwoStageTransfer outperforms existing transfer learning algorithms and enables PLASTIC to adapt even better to new teammates. We also identify three dimensions that we argue best describe ad hoc teamwork scenarios. We hypothesize that these dimensions are useful for analyzing similarities among domains and determining which can be tackled by similar algorithms in addition to identifying avenues for future research. The work presented in this thesis represents an important step towards enabling agents to adapt to unknown teammates in the real world. PLASTIC significantly broadens the robustness of robots to their teammates and allows them to quickly adapt to new teammates by reusing previously learned knowledge.

BibTeX

@PhdThesis{THESIS14-Barrett,
  author = {Samuel Barrett},
  title  = {Making Friends on the Fly: Advances in Ad Hoc Teamwork},
  school = {The University of Texas at Austin},
  year = {2014},
  address = {Austin, Texas, USA},
  month = {December},
  abstract={
    Given the continuing improvements in design and manufacturing processes in
    addition to improvements in artificial intelligence, robots are being
    deployed in an increasing variety of environments for longer periods of time.
    As the number of robots grows, it is expected that they will encounter and
    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 any standards will lag behind the
    state-of-the-art protocols and 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.  We
    argue that agents that effectively reason about ad hoc teamwork need to
    exhibit three capabilities: 1) robustness to teammate variety, 2) robustness
    to diverse tasks, and 3) fast adaptation.  This thesis focuses on addressing
    all three of these challenges.  In particular, this thesis introduces
    algorithms for quickly adapting to unknown teammates that enable agents to
    react to new teammates without extensive observations.
    The majority of existing multiagent algorithms focus on scenarios where all
    agents share coordination and communication protocols.  While previous research
    on ad hoc teamwork considers some of these three challenges, this thesis
    introduces a new algorithm, PLASTIC, that is the first to address all three
    challenges in a single algorithm.  PLASTIC adapts quickly to unknown teammates
    by reusing knowledge it learns about previous teammates and exploiting any
    expert knowledge available.  Given this knowledge, PLASTIC selects which
    previous teammates are most similar to the current ones online and uses this
    information to adapt to their behaviors.  This thesis introduces two
    instantiations of PLASTIC.  The first is a model-based approach, PLASTIC-Model,
    that builds models of previous teammates' behaviors and plans online to
    determine the best course of action.  The second uses a policy-based
    approach, PLASTIC-Policy, in which it learns policies for cooperating with
    past teammates and selects from among these policies online.  Furthermore, we
    introduce a new transfer learning algorithm, TwoStageTransfer, that allows
    transferring knowledge from many past teammates while considering how similar
    each teammate is to the current ones.
    We theoretically analyze the computational tractability of PLASTIC-Model in a
    number of scenarios with unknown teammates.  Additionally, we empirically
    evaluate PLASTIC in three domains that cover a spread of possible settings.
    Our evaluations show that PLASTIC can learn to communicate with unknown
    teammates using a limited set of messages, coordinate with externally-created
    teammates that do not reason about ad hoc teams, and act intelligently in
    domains with continuous states and actions.  Furthermore, these evaluations
    show that TwoStageTransfer outperforms existing transfer learning algorithms
    and enables PLASTIC to adapt even better to new teammates.  We also identify
    three dimensions that we argue best describe ad hoc teamwork scenarios.  We
    hypothesize that these dimensions are useful for analyzing similarities among
    domains and determining which can be tackled by similar algorithms in addition
    to identifying avenues for future research.  The work presented in this thesis
    represents an important step towards enabling agents to adapt to unknown
    teammates in the real world.  PLASTIC significantly broadens the robustness of
    robots to their teammates and allows them to quickly adapt to new teammates by
    reusing previously learned knowledge.
  }
}

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