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Nirom Cohen-Nov. On Integrated Multi-Agent Intention Recognition Systems. Master's Thesis, Bar Ilan University,2008.
Intention recognition is the ability to reason and infer information about others, based on observations of their behavior. Unfortunately, to date there have been only a handful of investigations into the integration of intention recognition into agent architectures. In particular, there are open questions as to the effect of intention recognition on the computational resources available to the agent. The issue is of particular importance in modern virtual environments, where the agent may be interacting with multiple other agents. This work tackles this question analytically and empirically. First, we examine run-time considerations, and offer a novel view of plan-recognition as a sampling process. Under this view, an existing work can be viewed as if trying to reduce the computational load on the agent by reducing the number of hypotheses it considers. In contrast, we reduce the Frequency by which the recognition process is sampled. We provide an analytical model allowing selection of a fixed-frequency recognition, and examine a number of heuristics for dynamically-changing plans. In the second part of the thesis, we consider a method of integrating plan recognition processes, called Mirroring, in which the executable knowledge of the agent is re-used, as is, for recognition.
@MastersThesis{nirom-msc,
author = {Nirom Cohen-Nov},
title = {On Integrated Multi-Agent Intention Recognition Systems},
school = {{B}ar {I}lan {U}niversity},
year = {2008},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {},
OPTannote = {},
wwwnote = {},
abstract = { Intention recognition is the ability to reason and infer information
about others, based on observations of their behavior.
Unfortunately, to date there have been only a handful of
investigations into the integration of intention recognition into
agent architectures. In particular, there are open questions as to
the effect of intention recognition on the computational resources
available to the agent. The issue is of particular importance in
modern virtual environments, where the agent may be interacting with
multiple other agents. This work tackles this question analytically
and empirically. First, we examine run-time considerations, and
offer a novel view of plan-recognition as a sampling process. Under
this view, an existing work can be viewed as if trying to reduce the
computational load on the agent by reducing the number of hypotheses
it considers. In contrast, we reduce the \emph{Frequency}
by which the recognition process is sampled. We provide an
analytical model allowing selection of a fixed-frequency
recognition, and examine a number of heuristics for
dynamically-changing plans. In the second part of the thesis, we
consider a method of integrating plan recognition processes, called
\emph{Mirroring}, in which the executable knowledge of the agent is re-used, as is, for recognition.
}
}
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