Efficient MUS Enumeration of Horn Formulae with Applications to Axiom Pinpointing

Arif, M. Fareed; Mencía, Carlos; Marques-Silva, João

doi:10.1007/978-3-319-24318-4_24

Cited by 20 publications

(35 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In logic-based abduction there are two formulations of the problem directly connected to the relation between diagnoses, observations, and the background knowledge: consequence finding [40] and proof-tree completion [42]. (1) In consequence finding explanations are derived deductively by re-formulating abduction as the search for logical consequences. Two well known consequence finding techniques are the Assumptionbased Truth Maintenance System (ATMS) [6], deriving consequences from propositional Horn theories, and Skipping Ordered Linear (SOL) resolution allowing to extract logical consequences of interest for full clausal first-order theories [23].…”

Section: Related Researchmentioning

confidence: 99%

“…Only a portion of attributes, namely those associated with the set of observations, has to be derived online. 1 Figure 2 lists all features extracted from the models, with the Instance 1 In the case of general abductive reasoning it might not be the case that partial information of the theory is already available, hence, all attributes have to computed online specific/Observation dependent constituting the attributes computed online.…”

Section: Meta-approachmentioning

confidence: 99%

“…The traditional consistency-based method exploits a formalization of the The work presented in this paper has been supported by the FFG project Applied Model Based Reasoning (AMOR) under grant 842407 and Steirische Wirtschaftsförderung project EXPERT. Roxane Koitz-Hristov rkoitz@ist.tugraz.at Franz Wotawa wotawa@ist.tugraz.at 1 Insitute for Software Technology, Graz University of Technology, Inffeldgasse 16b/II, Graz 8010, Austria correct behavior that in conjunction with a symptom leads to inconsistencies. These inconsistencies are then used to derive explanations.…”

Section: Introductionmentioning

confidence: 99%

“…Besides being expressive enough to formalize the artifact's characteristics, the model should allow for efficient diagnostic reasoning to be suitable in a practical application. Ideally, the system description allows additional support of the decision making process by (1) including probabilities to allow a prioritization, (2) by enabling probe selection recommendations to refine the diagnoses, and/or (3) by taking into account costs and other factors for suggesting repair or replacement activities [4].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Applying algorithm selection to abductive diagnostic reasoning

Koitz-Hristov

Wotawa

2018

Appl Intell

View full text Add to dashboard Cite

The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the "best" performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the metaapproach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.

show abstract

Section: Related Researchmentioning

confidence: 99%

Section: Meta-approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applying algorithm selection to abductive diagnostic reasoning

Koitz-Hristov

Wotawa

2018

Appl Intell

View full text Add to dashboard Cite

show abstract

“…For the concrete case of EL + , the problem encoding is well-known to be Horn [34], with the soft clauses being unit positive. The use of LTUR-like algorithms has been investigated in [3].…”

Section: Sample Problemsmentioning

confidence: 99%

Horn Maximum Satisfiability: Reductions, Algorithms and Applications

Marques-Silva

Ignatiev

Morgado

2017

Progress in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Recent years have witness remarkable performance improvements in maximum satisfiability (MaxSAT) solvers. In practice, MaxSAT algorithms often target the most generic MaxSAT formulation, whereas dedicated solvers, which address specific subclasses of MaxSAT, have not been investigated. This paper shows that a wide range of optimization and decision problems are either naturally formulated as MaxSAT over Horn formulas, or permit simple encodings using Horn MaxSAT. Furthermore, the paper also shows how linear time decision procedures for Horn formulas can be used for developing novel algorithms for the Horn MaxSAT problem.

show abstract

Computing Shortest Resolution Proofs

Mencía

Marques-Silva

2019

Progress in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Propositional resolution is a powerful proof system for unsatisfiable propositional formulas in conjunctive normal form. Resolution proofs represent useful explanations of infeasibility, with important applications. This motivates the challenge of computing shortest resolution proofs, i.e. those with the smallest number of inference steps. This paper proposes a SAT-based approach for this problem. Concretely, the paper investigates new propositional encodings for computing shortest resolution proofs and devises a number of optimizations, including symmetry breaking, additional constraints on the structure of proofs, as well as exploiting related concepts in infeasibility analysis, such as minimal correction subsets. Experimental results show the suitability of the proposed approach.

show abstract

Efficient MUS Enumeration of Horn Formulae with Applications to Axiom Pinpointing

Cited by 20 publications

References 36 publications

Applying algorithm selection to abductive diagnostic reasoning

Applying algorithm selection to abductive diagnostic reasoning

Horn Maximum Satisfiability: Reductions, Algorithms and Applications

Computing Shortest Resolution Proofs

Contact Info

Product

Resources

About