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Tailoring Action Parameterizations to Their Task Contexts (bibtex)
  title = {Tailoring Action Parameterizations to Their Task Contexts},
  year = {2005},
  author = {Stulp, Freek and Beetz, Michael},
  note = {{IJCAI} Workshop {“Agents} in Real-Time and Dynamic Environments”},
  abstract = {Solving complex tasks successfully and efficiently not only depends
	on {\textbackslash}em what you do, but also {\textbackslash}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.},
  url = {ṽisser/ijcai05/}
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Last edited 29.01.2013 17:37 by Quirin Lohr