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On Integrated Multi-Agent Intention Recognition Systems

Nirom Cohen-Nov. On Integrated Multi-Agent Intention Recognition Systems. Master's Thesis, Bar Ilan University,2008.

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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 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.

Additional Information

BibTeX

@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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