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GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining (bibtex)
@INPROCEEDINGS{hoyningen-huene_gram:_2007,
  author = {Hoyningen-Huene, Nicolai v and Kirchlechner, Bernhard and Beetz,
	Michael},
  title = {{GrAM:} Reasoning with Grounded Action Models by Combining Knowledge
	Representation and Data Mining},
  booktitle = {Towards Affordance-based Robot Control},
  year = {2007},
  abstract = {This paper proposes {GrAM} (Grounded Action Models), a novel integration
	of actions and action models into the knowledge representation and
	inference mechanisms of agents. In {GrAM} action models accord to
	agent behavior and can be specified explicitly and implicitly. The
	explicit representation is an action class specific set of Markov
	logic rules that predict action properties. Stated implicitly an
	action model defines a data mining problem that, when executed, computes
	the model's explicit representation. When inferred from an implicit
	representation the prediction rules predict typical behavior and
	are learned from a set of training examples, or, in other words,
	grounded in the respective experience of the agents. Therefore, {GrAM}
	allows for the functional and thus adaptive specification of concepts
	such as the class of situations in which a special action is typically
	executed successfully or the concept of agents that tend to execute
	certain kinds of actions. {GrAM} represents actions and their models
	using an upgrading of the representation language {OWL} and equips
	the Java Theorem Prover ({JTP)}, a hybrid reasoner for {OWL}, with
	additional mechanisms that allow for the automatic acquisition of
	action models and solving a variety of inference tasks for actions,
	action models and functional descriptions.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr