Automata-Based Abduction for Tractable Diagnosis.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".