A Relational Representation for Procedural Task Knowledge
S. Hart, R. Grupen, and D. Jensen. A Relational Representation for Procedural Task Knowledge, Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-2005)
- Abstract
- This paper proposes a methodology for learning joint probability
estimates regarding the effect of sensorimotor features
on the predicated quality of desired behavior. These relationships
can then be used to choose actions that will most likely
produce success. relational dependency networks are used to
learn statistical models of procedural task knowledge. An example
task expert for picking up objects is learned through
actual experience with a humanoid robot. We believe that
this approach is widely applicable and has great potential to
allow a robot to autonomously determine which features in
the world are salient and should be used to recommend policy
for action.
- Text
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