Abstract:Defeasible inheritance networks are a non-monotonic framework that deals with hierarchical knowledge. On the other hand, rational closure is acknowledged as a landmark of the preferential approach to non-monotonic reasoning. We will combine these two approaches and define a new non-monotonic closure operation for propositional knowledge bases that combines the advantages of both. Then we redefine such a procedure for Description Logics (DLs), a family of logics well-suited to model structured information. In b… Show more
“…On the contrary, once penguins are recognized as non-typical birds, no inheritance of typical properties is possible. In order to solve this problem, a strengthening of a rational closure-like algorithm with defeasible inheritance networks has been studied by [9].…”
Section: Conclusion and Related Workmentioning
confidence: 99%
“…The authors show that, for each order s, the consequence relation s is rational and can be computed in PSpace. In a subsequent work [9,11], the authors introduce an approach based on the combination of rational closure and Defeasible Inheritance Networks (INs). The authors first develop their approach at a propositional level, then they extend it to DLs, addressing both TBox and ABox reasoning.…”
“…On the contrary, once penguins are recognized as non-typical birds, no inheritance of typical properties is possible. In order to solve this problem, a strengthening of a rational closure-like algorithm with defeasible inheritance networks has been studied by [9].…”
Section: Conclusion and Related Workmentioning
confidence: 99%
“…The authors show that, for each order s, the consequence relation s is rational and can be computed in PSpace. In a subsequent work [9,11], the authors introduce an approach based on the combination of rational closure and Defeasible Inheritance Networks (INs). The authors first develop their approach at a propositional level, then they extend it to DLs, addressing both TBox and ABox reasoning.…”
“…Therefore, currently conflict detection between policies cannot be done using OWL-POLAR when it requires non-monotonic reasoning. However, this limitation would be relieved when nonmonotonic reasoning mechanisms becomes available in standard OWL-DL reasoners [6].…”
Section: Related Work and Discussionmentioning
confidence: 99%
“…Then, it checks if P j is also active in this canonical state by finding all substitutions satisfying activation and role constraints of P j (line 3). Lastly, it checks if any of these substitutions satisfies the requirements listed above (lines [4][5][6][7][8]. The algorithm returns true if such a substitution exists (lines 5-7); otherwise it returns false (line 9).…”
In a distributed system, the actions of one component may lead to severe failures in the system as a whole. To govern such systems, constraints are placed on the behaviour of components to avoid such undesirable actions. Policies or norms are declarations of soft constraints regulating what is prohibited, permitted or obliged within a distributed system. These constraints provide systems-level means to mitigate against failures. A few machine-processable representations for policies have been proposed, but they tend to be either limited in the types of policies that can be expressed or are limited by the complexity of associated reasoning mechanisms. In this paper, we present a language that sufficiently expresses the types of policies essential in practical systems, and which enables both policy-governed decision-making and policy analysis within the bounds of decidability. We then propose an OWL-based representation of policies that meets these criteria and reasoning mechanisms that use a novel combination of ontology consistency checking and query answering. The proposed policy representation and reasoning mechanisms allow development of distributed agent-based systems that operate flexibly and effectively in policy-constrainted environments.
“…In Section 6, we assume the reader to be familiar with the standard nonmonotonic semantics for DLs (Circumscription, Default logic, MKNF, Rational closure, etc.). Their syntax and semantics can be found in [3,4,21,10,18,29,19,40].…”
Section: Example 1 Consider the Famous Nixon's Diamondmentioning
Nonmonotonic inferences are not yet supported by Description Logic technology, although their potential usefulness is widely recognized. Lack of support to nonmonotonic reasoning is due to a number of issues related to expressiveness, computational complexity, and optimizations. This work contributes to the practical support of nonmono-tonic reasoning in description logics by introducing a new semantics designed to address knowledge engineering needs. The formalism is validated through extensive comparison with the other non-monotonic DLs, and systematic scalability tests.
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