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GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining (bibtex) [pdf]
  author =       {Nicolai v. Hoyningen-Huene and Bernhard Kirchlechner and Michael Beetz},
  title =        {{GrAM}: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining},
  booktitle =    {Towards Affordance-based Robot Control},
  year =         {2007},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Game analysis},
  bib2html_groups   = {IAS,FIPM,Aspogamo},
  bib2html_funding  = {FIPM},
  bib2html_domain   = {Soccer Analysis},
  bib2html_keywords = {},
  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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