New Revision Algorithms

Goldsmith, Judy; Sloan, Robert H.; Szörényi, Balázs; Turán, György

doi:10.1007/978-3-540-30215-5_30

Cited by 6 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given a query which is close to the user's intended query, our goal is to determine the intended query through few membership questions -polynomial in the distance between the given query and the intended query. Efficient revision algorithms exist for (non-quantified) rolepreserving Horn formula [9]. The Boolean-lattice provides us with a natural way to measure how close two queries are: the distance between the distinguishing tuples of the given and intended queries.…”

Section: Discussionmentioning

confidence: 99%

Learning and verifying quantified boolean queries by example

Abouzied

Angluin

Papadimitriou

et al. 2013

Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

View full text Add to dashboard Cite

To help a user specify and verify quantified queries -a class of database queries known to be very challenging for all but the most expert users -one can question the user on whether certain data objects are answers or non-answers to her intended query. In this paper, we analyze the number of questions needed to learn or verify qhorn queries, a special class of Boolean quantified queries whose underlying form is conjunctions of quantified Horn expressions. We provide optimal polynomial-question and polynomial-time learning and verification algorithms for two subclasses of the class qhorn with upper constant limits on a query's causal density.

show abstract

Section: Discussionmentioning

confidence: 99%

Learning and verifying quantified boolean queries by example

Abouzied

Angluin

Papadimitriou

et al. 2013

Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

View full text Add to dashboard Cite

show abstract

“…Acyclic Horn theories are an important and well-studied class of Horn theories that have been considered in abduction, for example, in Console et al [1991] and Selman and Levesque [1996], but also, in a more general context, in logic programming [Apt and Bezem 1991], in knowledge bases and expert systems [Boros et al 1998;Ginsberg 1988;Hammer and Kogan 1994;Hammer and Kogan 1995;Nguyen et al 1987] and in machine learning [Angluin 1987;Arimura 1997;Goldsmith et al 2004;Goldsmith and Sloan 2005;Reddy and Tadepalli 1998]. While the algorithm is easy to implement, the proof of its correctness is nontrivial and requires some insight into the interaction of clauses in the theory for explanation forming.…”

Section: Resultsmentioning

confidence: 99%

On computing all abductive explanations from a propositional Horn theory

Eiter

Makino

2007

J. ACM

View full text Add to dashboard Cite

Abduction is a fundamental mode of reasoning with applications in many areas of AI and Computer Science. The computation of abductive explanations is an important computational problem, which is at the core of early systems such as the ATMS and Clause Management Systems and is intimately related to prime implicate generation in propositional logic. Many algorithms have been devised for computing some abductive explanation, and the complexity of the problem has been well studied. However, little attention has been paid to the problem of computing multiple explanations, and in particular all explanations for an abductive query. We fill this gap and consider the computation of all explanations of an abductive query from a propositional Horn theory, or of a polynomial subset of them. Our study pays particular attention to the form of the query, ranging from a literal to a compound formula, to whether explanations are based on a set of abducible literals and to the representation of the Horn theory, either by a Horn conjunctive normal form (CNF) or model-based in terms of its characteristic models. For these combinations, we present either tractability results in terms of polynomial total-time algorithms, intractability results in terms of Dedicated to the memory of Marco Cadoli. Most of the results in this article appeared in preliminary form in nonexistence of such algorithms (unless P = NP), or semi-tractability results in terms of solvability in quasi-polynomial time, established by polynomial-time equivalence to the problem of dualizing a monotone CNF expression. Our results complement previous results in the literature, and refute a longstanding conjecture by Selman and Levesque. They elucidate the complexity of generating all abductive explanations and shed light on related problems such as generating sets of restricted prime implicates of a Horn theory. The algorithms for tractable cases can be readily applied for generating a polynomial subset of explanations in polynomial time.

show abstract

“…(Fermé, 2011)-nor built upon a model of dynamic theory revision of knowledge accounting for both facts and rules, as in machine learning, see e.g. (Omlin and Giles, 1996), (Goldsmith and Sloan, 2005). Similarly to Horty (Horty, 2011), our specific scope is on rules, as components of a rule base, already defined at symbolic level.…”

Section: Resultsmentioning

confidence: 99%

A Constructivist Approach to Rule Bases

Sileno

Boer

Engers

2015

Proceedings of the International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

Abstract:The paper presents a set of algorithms for the conversion of rule bases between priority-based and constraintbased representations. Inspired by research in precedential reasoning in law, such algorithms can be used for the analysis of a rule base, and for the study of the impact of the introduction of new rules. In addition, the paper explores an optimization mechanism, built upon assumptions about the world in which the rule-based system operates, providing a model of environmental adaptation. The investigation is relevant to practical reasoning, agent modeling and agent programming.

show abstract

New Revision Algorithms

Cited by 6 publications

References 17 publications

Learning and verifying quantified boolean queries by example

Learning and verifying quantified boolean queries by example

On computing all abductive explanations from a propositional Horn theory

A Constructivist Approach to Rule Bases

Contact Info

Product

Resources

About