Belief Revision System Research Articles

A study of possible-worlds semantics of relevance-sensitive belief revision

Abstract Parikh’s relevance-sensitive axiom (P) for belief revision is open to two different interpretations, i.e. the weak and the strong version of (P), both of which are plausible depending on the context. Given that strong (P) has not received the attention it deserves, in this article, an extended examination of it is conducted. In particular, we point out interesting properties of the semantic characterization of the strong version of (P), as well as a vital feature of it that, potentially, results in a significant drop on the resources required for an implementation of a belief-revision system. Lastly, we shed light on the natural connection between global and local revision functions, via their corresponding semantic characterization, hence, a means for constructing global revision functions from local ones, and vice versa, is provided.

T-spline local refinement as a belief revision system: a rule-based implementation

As originally conceived, T-splines generalize both NURBS and Subdivision surfaces. Central to T-splines is the knot refinement algorithm, which seems to successfully import the local characteristic...

A Fuzzy Dynamic Belief Logic System

In this paper, we develop a fuzzy dynamic belief revision logic system. In our system, propositions take truth values in a set of multiple fuzzy linguistic terms, which people use in everyday life. And we use a uninorm operator to aggregate the linguistic truth values of the same proposition but drawn from two different rules because uninorms can reflect well that the aggregated result of two somehow negative truth values of the same proposition should be more negative, the aggregated result of two somehow positive ones should be more positive, and the result of a negative one and a positive one is a compromise. In this system, the belief on a proposition is the linguistic truth of the proposition in the most possible world according to the current preference over all possible worlds. In the light of new information, the preference degrees of possible worlds will be updated. Accordingly, the most possible world will be changed to another and thus an old belief on a propositional formula will be changed to the linguistic truth of the proposition in the new most possible world. Moreover, we prove the soundness and completeness of our fuzzy dynamic belief revision system. In addition, we also prove that our belief revision method in fuzzy environment satisfies some relevant ones of standard AGM postulates named after the names of their proponents, Alchourron, Gardenfors, and Makinson.

A model-based belief revision system

Numerous belief revision and update semantics have been proposed in the literature in the past few years, but until recently, no work in the belief revision literature has focussed on the problem of implementing these semantics, and little attention has been paid to algorithmic questions. In this paper, we present and analyze our update algorithms built in Immortal, a model-based belief revision system. These algorithms can work for a variety of model-based belief revision semantics proposed to date. We also extend previously proposed semantics to handle updates involving the equality predicate and function symbols and incorporate these extensions in our algorithms. As an example, we discuss the use of belief revision semantics to model the action-augmented envisioning problem in qualitative simulation, and we show the experimental results of running an example simulation in Immortal.

A model for belief revision in a multi-agent environment (abstract)

In modeling the knowledge processing structure of an Agent in a Multi-Agent world it becomes necessary to enlarge the traditional concept of Belief Revision. For detecting contradictions and identifying their sources it is sufficient to maintain informations about what has been told; but to "solve" a contradiction it is necessary to keep informations about who said it or, in general, about the source where that knowledge came from. We can take as certain the fact that an agent gave an information, but we can take the given information only as a revisable assumption. The Belief Revision system can't leave the sources of the informations out of consideration because of their relevance in giving the additional notion of "strength of belief" [Galliers 89]. In fact, the reliability of the source affects the credibility of the information and vice-versa. It is necessary to develop systems that deal with couples <assumption, source of the assumption> . In [Dragoni 91] we've proposed a system that moves in this direction. Here we give a short description of that system. In part one we describe the agent' s knowledge processing structure using a particular characterization of the "Assumption Based Belief Revision" concept; in part two we outline the project of an embedded device that enables the overall system to deal with couples <assumption, source of the assumption> in an rather anthropomorphic manner.

Partial monotonicity and a new version of the Ramsey test

We introduce two new belief revision axioms: partial monotonicity and consequence correctness. We show that partial monotonicity is consistent with but independent of the full set of axioms for a Gardenfors belief revision sytem. In contrast to the Gardenfors inconsistency results for certain monotonicity principles, we use partial monotonicity to inform a consistent formalization of the Ramsey test within a belief revision system extended by a conditional operator. We take this to be a technical dissolution of the well-known Gardenfors dilemma.

The implementation of a model-based belief revision system

In this paper, we briefly describe Immortal, an implementation of a model-based belief revision system. We present our motivation for building Immortal, describe its basic components and properties, and discuss our method of evaluating Immortal.

Automatic belief revision in a plausibility-driven design environment

Truth maintenance systems (TMSs) offer the most elaborate approach to the belief revision problem and are thus ideal candidates for automating belief revision during the course of plausibility-driven design evolution. However, the theory of plausible design (TPD) necessitates multivalued belief propagation while conventional TMSs are two-valued (IN/OUT) systems. Due to this mismatch, the encoding of constraint dependency graphs (CDGs) in conventional TMSs requires an exponential number of justifications. A belief revision system for TPD called the theory of plausible design belief revision system (TPD-BRS) is proposed. TPD-BRS uses a modified version of the assumption-based TMS label propagation algorithm in conjunction with a label interpreter to support automated belief revision in TPD efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A model for belief revision

It is generally recognized that the possibility of detecting contradictions and identifying their sources is an important feature of an intelligent system. Systems that are able to detect contradictions, identify their causes, or readjust their knowledge bases to remove the contradiction, called Belief Revision Systems, Truth Maintenance Systems, or Reason Maintenance Systems, have been studied by several researchers in Artificial Intelligence (AI). In this paper, we present a logic suitable for supporting belief revision systems, discuss the properties that a belief revision system based on this logic will exhibit, and present a particular implementation of our model of a belief revision system. The system we present differs from most of the systems developed so far in three respects: First, it is based on a logic that was developed to support belief revision systems. Second, it uses the rules of inference of the logic to automatically compute the dependencies among propositions rather than having to force the user to do this, as in many existing systems. Third, it was the first belief revision system whose implementation relies on the manipulation of sets of assumptions, not justifications.