Credibility-Limited Revision for Epistemic Spaces

• URL: http://arxiv.org/abs/2409.07119v1
• Date: Wed, 11 Sep 2024 09:15:43 GMT
• Title: Credibility-Limited Revision for Epistemic Spaces
• Authors: Kai Sauerwald,
• Abstract summary: We extend the class of credibility-limited revision operators in a way that all AGM revision operators are included. A semantic characterization of extended credibility-limited revision operators that employ total preorders on possible worlds is presented.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider credibility-limited revision in the framework of belief change for epistemic spaces, permitting inconsistent belief sets and inconsistent beliefs. In this unrestricted setting, the class of credibility-limited revision operators does not include any AGM revision operators. We extend the class of credibility-limited revision operators in a way that all AGM revision operators are included while keeping the original spirit of credibility-limited revision. Extended credibility-limited revision operators are defined axiomatically. A semantic characterization of extended credibility-limited revision operators that employ total preorders on possible worlds is presented.

Related papers

• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• The Realizability of Revision and Contraction Operators in Epistemic Spaces [8.010966370223985]
  AGM revision and AGM contraction operators are only realizable in precisely determined spaces. We define the class of linear change operators, a special kind of maxichoice operator. When AGM revision, respectively, AGM contraction, is realizable, linear change operators are a canonical realization.
  arXiv  Detail & Related papers  (2024-07-30T15:55:01Z)
• Certifiably Byzantine-Robust Federated Conformal Prediction [49.23374238798428]
  We introduce a novel framework Rob-FCP, which executes robust federated conformal prediction effectively countering malicious clients. We empirically demonstrate the robustness of Rob-FCP against diverse proportions of malicious clients under a variety of Byzantine attacks.
  arXiv  Detail & Related papers  (2024-06-04T04:43:30Z)
• Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach [51.012396632595554]
  Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments. Recent theoretical results verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. We develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects.
  arXiv  Detail & Related papers  (2023-12-15T12:58:05Z)
• System of Spheres-based Two Level Credibility-limited Revisions [0.0]
  When revising by a two level credibility-limited revision, two levels of credibility and one level of incredibility are considered. We propose a construction for two level credibility-limited revision operators based on Grove's systems of spheres.
  arXiv  Detail & Related papers  (2023-07-11T07:10:39Z)
• Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization [56.94741578760294]
  We propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summary. Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact.
  arXiv  Detail & Related papers  (2023-05-23T22:11:47Z)
• Hallucinated Adversarial Control for Conservative Offline Policy Evaluation [64.94009515033984]
  We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, we seek to obtain a (tight) lower bound on a policy's performance. We introduce HAMBO, which builds on an uncertainty-aware learned model of the transition dynamics. We prove that the resulting COPE estimates are valid lower bounds, and, under regularity conditions, show their convergence to the true expected return.
  arXiv  Detail & Related papers  (2023-03-02T08:57:35Z)
• On Limited Non-Prioritised Belief Revision Operators with Dynamic Scope [2.7071541526963805]
  We introduce 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.
  arXiv  Detail & Related papers  (2021-08-17T17:22:29Z)
• On the use of evidence theory in belief base revision [0.0]
  We propose the idea of credible belief base revision yielding to define two new formula-based revision operators. These operators stem from consistent subbases maximal with respect to credibility instead of set inclusion and cardinality.
  arXiv  Detail & Related papers  (2020-09-24T12:45:32Z)
• Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments [96.114824979298]
  Three important challenges in conference peer review are maliciously attempting to get assigned to certain papers and "torpedo reviewing" We present a framework that brings all these challenges under a common umbrella and present a (randomized) algorithm for reviewer assignment. Our algorithms can limit the chance that any malicious reviewer gets assigned to their desired paper to 50% while producing assignments with over 90% of the total optimal similarity.
  arXiv  Detail & Related papers  (2020-06-29T23:55:53Z)
• Belief Base Revision for Further Improvement of Unified Answer Set Programming [0.0]
  The base revision operator is developed using Removed Set Revision strategy. The operator is characterized by respect to the postulates for base revisions operator satisfies.
  arXiv  Detail & Related papers  (2020-02-27T08:31:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.