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Freek Stulp and Michael Beetz. Tailoring Action Parameterizations to Their Task Contexts. 2005. IJCAI Workshop ``Agents in Real-Time and Dynamic Environments''
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Abstract
Solving complex tasks successfully and efficiently not only depends on \em what you do, but also \em how you do it. Different task contexts have different performance measures, and thus require different ways of executing an action to optimize performance. Simply adding new actions that are tailored to perform well within a specific task context makes planning or action selection programming more difficult, as generality and adaptivity is lost. Rather, existing actions should be parametrized such that they optimize the task-specific performance measure. In this paper we propose a novel computation model for the execution of abstract action chains. In this computation model, a robot first learns situation-specific performance models of abstract actions. It then uses these models to automatically specialize the abstract actions for their execution in a given action chain. This specialization results in refined chains that are optimized for performance. As a side effect this behavior optimization also appears to produce action chains with seamless transitions between actions.
BibTeX Entry
@Misc{stulp05tailoring,
author = {Freek Stulp and Michael Beetz},
title = {Tailoring Action Parameterizations to Their Task Contexts},
note = {IJCAI Workshop ``Agents in Real-Time and Dynamic Environments''},
year = {2005},
url = {http://www.tzi.de/\~visser/ijcai05/},
bib2html_pubtype = {Refereed Workshop Paper},
bib2html_rescat = {Plan-based Robot Control, Robot Learning},
bib2html_groups = {IAS, AGILO},
bib2html_keywords = {},
abstract = {Solving complex tasks successfully and efficiently not
only depends on {\em what} you do, but also {\em how} you do it. Different
task contexts have different performance measures, and thus require different
ways of executing an action to optimize performance. Simply adding new actions
that are tailored to perform well within a specific task context makes
planning or action selection programming more difficult, as generality and
adaptivity is lost. Rather, existing actions should be parametrized such that
they optimize the task-specific performance measure. In this paper we propose
a novel computation model for the execution of abstract action chains. In this
computation model, a robot first learns situation-specific performance models
of abstract actions. It then uses these models to automatically specialize the
abstract actions for their execution in a given action chain. This
specialization results in refined chains that are optimized for performance.
As a side effect this behavior optimization also appears to produce action
chains with seamless transitions between actions.}
}
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