Automata-Based Abduction for Tractable Diagnosis.
Thomas Hubauer, Steffen Lamparter, Michael M. Pirker · Description Logics · 2010
Abductive reasoning has been recognized as a valuable com- plement to deductive inference for tasks such as diagnosis and integration of incomplete information despite its inherent computational complex- ity. This paper presents a novel, tractable abduction procedure for the lightweight description logic EL. The proposed approach extends recent research on automata-based axiom pinpointing (which is in some sense dual to our problem) by assuming information from a predefined ab- ducible part of the domain model if necessary, while the remainder of the domain is considered to be fixed. Our research is motivated by the need for efficient diagnostic reasoning for large-scale industrial systems where observations are partially incomplete and often sparse, but nevertheless the largest part of the domain such as physical structures is known. Tech- nically, we introduce a novel pattern-based definition of abducibles and show how to construct a weighted automaton that commonly encodes the definite and abducible part of the domain model. We prove that its behavior provides a compact representation of all possible hypotheses explaining an observation, and is in fact computable in PTime. Abductive reasoning is a method for generating hypotheses that explain an obser- vation based on a model of the domain, typically in the presence of incomplete data. Its non-monotonicity and explorative nature make abduction a promis- ing candidate for the interpretation of potentially incomplete information - a task which is much harder to accomplish using established monotonic inference methods such as deduction or the more elaborate axiom pinpointing. The appli- cations of abductive inference are diverse, ranging from text interpretation (1) to plan generation and analysis (2), and interpretation of sensor (3) or multimedia data (4). Our research on abductive inference is motivated by industrial applica- tions in Ambient Assisted Living and assistive diagnosis for complex technical devices. In these scenarios we found the underlying models being typically large, though not overly complex in their structure. The main consideration is therefore scalability with respect to the size of the domain model; to effectively support humans or to avoid consequential damage to machinery, information processing is subject to soft realtime constraints. Proc. 23rd Int. Workshop on Description Logics (DL2010), CEUR-WS 573, Waterloo, Canada, 2010.