# Publications Freek Stulp

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 • Sorted by Date • Classified by Publication Type • Classified by Research Category • Tailoring Action Parameterizations to Their Task Contexts Freek Stulp and Michael Beetz. Tailoring Action Parameterizations to Their Task Contexts. In IJCAI Workshop Agents in Real-Time and Dynamic Environments'', 2005. Non-archival. Download [PDF]507.1kB 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
 @InProceedings{stulp05tailoring, title = {Tailoring Action Parameterizations to Their Task Contexts}, author = {Freek Stulp and Michael Beetz}, booktitle = {IJCAI Workshop Agents in Real-Time and Dynamic Environments''}, year = {2005}, note = {Non-archival.}, 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.}, bib2html_pubtype = {Refereed Workshop Paper}, bib2html_rescat = {Optimizing the Execution of Symbolic Robot Plans}, file = {IJCAI-WS-2005-Stulp-Beetz.pdf:http\://www9.in.tum.de/publications/2005/IJCAI-WS-2005-Stulp-Beetz.pdf:PDF}, url = {http://www.tzi.de/\~visser/ijcai05/} } 

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