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Shashank Suhas
Seminar-HFO
Commits
a953144b
Commit
a953144b
authored
Jun 11, 2015
by
Matthew Hausknecht
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a953144b
@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.
}
}
\ No newline at end of file
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