This paper shows how automated abduction and induction can be used to infer logical process models from temporal observations of states and actions. The proposed method employs a non-monotonic learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL) to learn domain axioms in a temporal logic programming formalism known as the Event Calculus (EC). The key benefit of this logical learning method is its ability to utilise background knowledge and to return human understandable hypotheses. The approach is illustrated on a simplified biological process modelling task.
|Translated title of the contribution||Inferring process models from temporal data with abduction and induction|
|Title of host publication||1st International Workshop on the Induction of Process Models|
|Publication status||Published - 2007|
Bibliographical noteOther page information: -
Conference Proceedings/Title of Journal: 1st International Workshop on the Induction of Process Models
Other identifier: 2000827