Automated Abduction

Inoue, Katsumi

doi:10.1007/3-540-45632-5_13

Cited by 9 publications

(4 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We here use Poole's framework for default reasoning [21] (without constraints), which is a subset of Reiter's default logic [23] for clausal theories. Poole's framework is very simple yet powerful and practically useful as it can also give the logical account of abduction for which many resolution-based procedures exist [22,10].…”

Section: Framework For Default Reasoningmentioning

confidence: 99%

“…The use of characteristic clauses enables us to characterize various reasoning problems of interest to AI, such as query answering [3,12], knowledge compilation [2], nonmonotonic reasoning and abduction [8,10], inductive logic programming [9], and distributed knowledge bases [19]. In the propositional case, each characteristic clause of Σ is a prime implicate of Σ.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consequence Finding in Default Theories

Inoue

Iwanuma

Nabeshima

2004

Flexible Query Answering Systems

Self Cite

View full text Add to dashboard Cite

Abstract. Consequence finding has been recognized as an important technique in many intelligent systems involving inference. In previous work, propositional or first-order clausal theories have been considered for consequence finding. In this paper, we consider consequence finding within a default theory, which consists of a first-order clausal theory and a set of normal defaults. In each extension of a default theory, consequence finding can be performed with the "generating defaults" for the extension. Alternatively, we consider all extensions in one theory with the "conditional consequence" format, which explicitly represents how a conclusion depends on which defaults. We also propose a procedure to compute consequences from a default theory based on a first-order consequence-finding procedure SOL. The SOL calculus is then further refined using skip-preference and complement checking, which have a great ability of preventing irrational derivations. The proposed system can be well applied to a multi-agent system with speculative computation in an incomplete communication environment.

show abstract

Section: Framework For Default Reasoningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consequence Finding in Default Theories

Inoue

Iwanuma

Nabeshima

2004

Flexible Query Answering Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various procedures for generation of abducibles have been proposed so far; some are designed for Classical Propositional Calculus (CPC for short) [1], others for more sophisticated propositional logics [29,26] or for first-order logic [16,25,28]. Those procedures, which are defined in a strictly logical setting, use, for example, the proof methods of analytic tableaux [1,26,25], of sequent calculi [26,25], or the dynamic proof method of adaptive logics [29,28].…”

Section: Introductionmentioning

confidence: 99%

Identifying Efficient Abductive Hypotheses Using Multicriteria Dominance Relation

Komosiński

Kups

Leszczyńska-Jasion

et al. 2014

ACM Trans. Comput. Logic

View full text Add to dashboard Cite

In this article, results of the automation of an abductive procedure are reported. This work is a continuation of our earlier research [21], where a general scheme of the procedure has been proposed. Here, a more advanced system developed to generate and evaluate abductive hypotheses is introduced. Abductive hypotheses have been generated by the implementation of the Synthetic Tableau Method. Before the evaluation, the set of hypotheses has undergone several reduction phases. To assess usefulness of abductive hypotheses in the reduced set, several criteria have been employed. The evaluation of efficiency of the hypotheses has been provided by the multi-criteria dominance relation. To comprehend the abductive procedure and the evaluation process more extensively, analyses have been conducted on a number of artificially generated abductive problems.

show abstract

“…• Minimal rules: The minimal rule generation in rough set theory enables us to obtain minimal association rules using attribute reduction. Minimal causes provide better explanations of effects, as employed in abductive reasoning [15].…”

Section: Introductionmentioning

confidence: 99%

A rough set approach to multiple dataset analysis

Kaneiwa

2011

Applied Soft Computing

View full text Add to dashboard Cite

In the area of data mining, the discovery of valuable changes and connections (e.g., causality) from multiple data sets has been recognized as an important issue. This issue essentially differs from finding statistical associations in a single data set because it is complicated by the different data behaviors and relationships across multiple data sets. Using rough set theory, this paper proposes a change and connection mining algorithm for discovering a time delay between the quantitative changes in the data of two temporal information systems and for generating the association rules of changes from their connected decision table. We establish evaluation criteria for the connectedness of two temporal information systems with varying time delays by calculating weight-based accuracy and coverage of the association rules of changes, adjusted by a fuzzy membership function.

show abstract

Automated Abduction

Cited by 9 publications

References 66 publications

Consequence Finding in Default Theories

Consequence Finding in Default Theories

Identifying Efficient Abductive Hypotheses Using Multicriteria Dominance Relation

A rough set approach to multiple dataset analysis

Contact Info

Product

Resources

About