Answers that Have Integrity

[EN] Answers to queries in possibly inconsistent databases may not have integrity. We formalize ‘has integrity’ on the basis of a definition of ‘causes’. A cause of an answer is a minimal excerpt of the database that explains why the answer has been given. An answer has integrity if one of its causes does not overlap with any cause of integrity violation.Supported by FEDER and the Spanish grants TIN2009-14460-C03, TIN2010-17139.Decker, H. (2011). Answers that Have Integrity. Lecture Notes in Computer Science. 6834:54-72. https://doi.org/10.1007/978-3-642-23441-5S5472683

AGM 25 years: twenty-five years of research in belief change

The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases. In this review, the first twenty five years of this development are summarized. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, computatibility of AGM operations, and criticism of the model.info:eu-repo/semantics/publishedVersio

The cost of consistency: information economy in Paraconsistent Belief Revision

By Belief Revision it is understood a system that logically explains the rational process of changing beliefs by taking into account a new piece of information. The most influential approach in this field of study, the AGM system, proposed by Alchourrón, Gärdenfors, and Makinson, postulates rationality criteria for different types of belief change. In this paper I shall assess the relationship between those criteria and argue for an opposition between the principles of Information Economy and Consistency. Furthermore, I shall argue that Paraconsistent Belief Revision manages to minimise this friction in the best possible way

A conditional perspective of belief revision

Belief Revision is a subarea of Knowledge Representation and Reasoning (KRR) that investigates how to rationally revise an intelligent agent's beliefs in response to new information. There are several approaches to belief revision, but one well-known approach is the AGM model, which is rooted in work by Alchourrón, Gärdenfors, and Makinson. This model provides a set of axioms defining desirable properties of belief revision operators, which manipulate the agent's belief set represented as a set of propositional formulas. A famous extension to the classical AGM framework of Belief Revision is Darwiche and Pearl's approach to iterated belief revision. They uncovered that the key to rational behavior under iteration is adequate preservation of conditional beliefs, i.e., beliefs the agent is willing to accept in light of (hypothetical) new information. Therefore, they introduced belief revision operators modifying the agent's belief state, built from conditional beliefs. Kern-Isberner fully axiomatized a principle of conditional preservation for belief revision, which captures the core of adequate treatment of conditional beliefs during the revision. This powerful axiom provides the necessary conceptual framework for revising belief states with sets of conditionals as input, and it shows that conditional beliefs are subtle but essential for studying the process of belief revision. This thesis provides a conditional perspective of Belief Revision for different belief revision scenarios. In the first part, we introduce and investigate a notion of locality for belief revision operators on the semantic level. Hence, we exploit the unique features of conditionals, which allow us to set up local cases and revise according to these cases, s.t., the complexity of the revision task is reduced significantly. In the second part, we consider the general setting of belief revision with respect to additional meta-information accompanying the input information. We demonstrate the versatility and flexibility of conditionals as input for belief revision operators by reducing the parameterized input to a conditional one for two well-known parameterized belief revision operators who are similarly motivated but very different in their technical execution. Our results show that considering conditional beliefs as input for belief revision operators provides a gateway to new insights into the dynamics of belief revision