Detecting and repairing anomalous evolutions in noisy environments. Logic programming formalization and complexity results
type: Item
instance of: scholarly article
title: Detecting and repairing anomalous evolutions in noisy environments. Logic programming formalization and complexity results
zbMATH Open document ID: 1237.68201
author: Fabrizio Angiulli
author: Gianluigi Greco
author: Luigi Palopoli
published in: Annals of Mathematics and Artificial Intelligence
publication date: 2011-11-08T00:00:00Z
review text: This paper studies the diagnosis and repair of anomalous observations in a noisy system. In particular, it studies the problem of reasoning by an agent about its environment with the help of potentially faulty sensors, such as looking for a parking spot in a garage equipped with potentially faulty cameras, and ways to identify and repair faulty cameras. Thus, the focus of this work is not on agent planning based on observation made through the sensors, but agent reasoning about the collective sensor behavior, identifying and repairing faulty sensors based on its knowledge of the system when the agent carries out such a plan, referred to as \textit{agent revolution} in the paper. Technically speaking, the authors adopt a framework \(\langle {\mathcal F, S, D, K}\rangle\) to represent an agent, where \({\mathcal F}\) refers to a set of propositional letters, each taking a value among true, false, and unknown, used to specify the world for the agent; \({\mathcal S}\) denotes the set of available sensors that the agent may observe; \({\mathcal D}\) is a collection of state transition rules associated with a set of given actions that the agent may take; and \({\mathcal K}\) refers to some internal knowledge that the agent may use to reason about the current state of its world. Under this setting, an anomaly refers to a discrepancy between observations made with sensors that occur in \({\mathcal D}\) and trusted knowledge as stored in \({\mathcal K},\) while a repair refers to replacing faulty sensor observations with alternative values that eliminate all the aforementioned discrepancy, which does not need to reverse actions that already took place. The above transition rules in \({\mathcal D}\) are represented in terms of the action language \({\mathcal A}_k,\) associated with the \(0\)-approximation semantics; while those knowledge in \({\mathcal K}\) represented as propositional logic programs, augmented with the negation-by-default extension, associated with the answe…
reviewed by: Zhizhang Shen
Mathematics Subject Classification ID: 68T27
Mathematics Subject Classification ID: 68T37
Mathematics Subject Classification ID: 68T42
Mathematics Subject Classification ID: 68N17
Mathematics Subject Classification ID: 68Q15
zbMATH DE Number: 5969034
zbMATH Keywords: reasoning under uncertainty
zbMATH Keywords: agent technology
zbMATH Keywords: complexity results
describes a project that uses: Smodels
describes a project that uses: CCalc
MaRDI profile type: Publication
full work available at URL: s10472-010-9220-z
OpenAlex ID: W1607350599
cites work: On the logic of theory change: Partial meet contraction and revision functions
cites work: Q3039305
cites work: On the logic of theory change: safe contraction
cites work: Answer sets for consistent query answering in inconsistent databases
cites work: Reasoning about noisy sensors and effectors in the situation calculus
cites work: Diagnostic reasoning with A-Prolog
cites work: Computational complexity of planning and approximate planning in the presence of incompleteness
cites work: Q4940940
cites work: Database Programming Languages
cites work: Q4256521
cites work: The complexity of selecting maximal solutions
cites work: Q4460681
cites work: Heterogeneous temporal probabilistic agents
cites work: Probabilistic agent programs
cites work: A logic programming approach to knowledge-state planning. II: The DLV\(^\mathcal K\) system
cites work: On the computational cost of disjunctive logic programming: Propositional case
cites work: The complexity of logic-based abduction
cites work: Q4707752
cites work: Magic Sets and their application to data integration
cites work: Q4460684
cites work: Census Data Repair: A Challenging Application of Disjunctive Logic Programming
cites work: Q3983043
cites work: Representing action and change by logic programs
cites work: Nonmonotonic causal theories
cites work: Knowledge, probability, and adversaries
cites work: A survey of non-prioritized belief revision
cites work: Reasoning about actions with sensing under qualitative and probabilistic uncertainty
cites work: Unfolding partiality and disjunctions in stable model semantics
cites work: Q4385525
cites work: Diagnosing multiple faults
cites work: Q5750448
cites work: The complexity of optimization problems
cites work: The DLV system for knowledge representation and reasoning
cites work: Abduction in logic programming: A new definition and an abductive procedure based on rewriting
cites work: Q4215779
cites work: Q4298260
cites work: Q5750451
cites work: A theory of diagnosis from first principles
cites work: Q4252522
cites work: Knowledge, action, and the frame problem
cites work: A taxonomy of complexity classes of functions
cites work: Logic Programming and Nonmonotonic Reasoning
cites work: Formalizing sensing actions -- a transition function based approach
cites work: Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming
cites work: Q5750452
DOI: 10.1007/S10472-010-9220-Z
description: scientific article; zbMATH DE number 5969034