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On Limited Non-Prioritised Belief Revision Operators with Dynamic Scope

arXiv.org Artificial Intelligence

The research on non-prioritized revision studies revision operators which do not accept all new beliefs. In this paper, we contribute to this line of research by introducing the concept of dynamic-limited revision, which are revisions expressible by a total preorder over a limited set of worlds. For a belief change operator, we consider the scope, which consists of those beliefs which yield success of revision. We show that for each set satisfying single sentence closure and disjunction completeness there exists a dynamic-limited revision having the union of this set with the beliefs set as scope. We investigate iteration postulates for belief and scope dynamics and characterise them for dynamic-limited revision. As an application, we employ dynamic-limited revision to studying belief revision in the context of so-called inherent beliefs, which are beliefs globally accepted by the agent. This leads to revision operators which we call inherence-limited. We present a representation theorem for inherence-limited revision, and we compare these operators and dynamic-limited revision with the closely related credible-limited revision operators.


Credibility-Limited Revision Operators in Propositional Logic

AAAI Conferences

In Belief Revision the new information is generally accepted, following the principle of primacy of update. In some case this behavior can be criticized and one could require that some new pieces of information can be rejected by the agent because, for instance, of insufficient plausibility. This has given rise to several approaches of non-prioritized Belief Revision. In particular (Hansson et al. 2001) defined credibility-limited revision operators, where a revision is accepted only if the new information is a formula that belongs to a set of credible formulas. They provide several representation theorems in the AGM style. In this work we study credibility-limited revision operators when the information is represented in propositional logic, like in the Katsuno and Mendelzon framework. We propose a set of postulates and a representation theorem for credibility-limited revision operators. Then we explore how to generalize these definitions to the Iterated Belief Revision case, using epistemic states in the Darwiche and Pearl style.


Booth

AAAI Conferences

In Belief Revision the new information is generally accepted, following the principle of primacy of update. In some case this behavior can be criticized and one could require that some new pieces of information can be rejected by the agent because, for instance, of insufficient plausibility. This has given rise to several approaches of non-prioritized Belief Revision. In particular (Hansson et al. 2001) defined credibility-limited revision operators, where a revision is accepted only if the new information is a formula that belongs to a set of credible formulas. They provide several representation theorems in the AGM style. In this work we study credibility-limited revision operators when the information is represented in propositional logic, like in the Katsuno and Mendelzon framework. We propose a set of postulates and a representation theorem for credibility-limited revision operators. Then we explore how to generalize these definitions to the Iterated Belief Revision case, using epistemic states in the Darwiche and Pearl style.


Trust-Sensitive Belief Revision

AAAI Conferences

Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising.  We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Ferme and Hansson, and we examine its properties. In particular, we show how trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information.  When multiple reporting agents are involved, we use a distance function over states to represent differing degrees of trust; this ensures that the most trusted reports will be believed.


Trust as a Precursor to Belief Revision

Journal of Artificial Intelligence Research

Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising. We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Fermé and Hansson, and we prove a representation result that characterizes the class of trust-sensitive revision operators in terms of a set of postulates. We also show that trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information.