NDLI: Computational Quantification of Trust Updates

Please wait, while we are loading the content...

2006 International Workshop on Integrating AI and Data Mining

International Workshop on Integrating AI and Data Mining [Cover]

International Workshop on Integrating AI and Data Mining-Title

International Workshop on Integrating AI and Data Mining-Copyright

Patterns of Behaviour Mediated by Cognitive Scripts and Emotional Attitudes - Context-Aware Engineering of Data Mining Systems

Visualization of Non-vectorial Data Using Twin Kernel Embedding

Dynamic Algorithm Selection Using Reinforcement Learning

Mining Better Technical Trading Strategies with Genetic Algorithms

Finding the Right Features for Instrument Classification of Classical Music

Quantification of Intermarket Influence Based on the Global Optimization and Its Application for Stock Market Prediction

Efficient Fuzzy Rules For Classification

Using Coupled Hidden Markov Models to Model Suspect Interactions in Digital Forensic Analysis

Using Ontology to Map Categories in Blog

Computational Quantification of Trust Updates

Author index

Computational Quantification of Trust Updates

Content Provider	IEEE Xplore Digital Library
Author	Ramer, A.
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci. & Eng., UNSW, Sydney, NSW (Ramer, A.)
Abstract	A computational model for expressions of trust values is outlined. It is based on the proposal by Jonker and Treur to base trust updates on reported experiences. The model handles arbitrary sequences of experience inputs; its such updates are fully commutative and associative. It satisfies all the axiomatic properties suggested for the trust values. Trust is interpreted as family of probabilistic beliefs on the space of possible experience reports. Expansion of the space of reports gives rise to inverse conditioning of probability distributions and thus of trust values. Belief and trust changes follow the AGM structure. Inverse conditioning is put into effect through a suitable application of maximum entropy principles
Starting Page	73
Ending Page	78
File Size	200191
Page Count	6
File Format	PDF
ISBN	0769527302
DOI	10.1109/AIDM.2006.3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-04
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling probability revision Probability distribution Entropy Proposals Data mining AGM belief revision Computer science trust experience Ethics Insurance Trust updating inverse conditioning. Australia Artificial intelligence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The implementation of a first-order logic AGM belief revision system

An agents establishes trust with equitable information revelation

Bayesian updating rules and AGM belief revision

AGM Belief Revision, Semantically

Data compression using deterministic experts

The Implementation of a First-Order Logic AGM Belief Revision System (1993)

Probabilistic Belief Expansion and Conditioning (1996)

Equivalent Experience-based Trust Quantification Model

Belief Revision and Trust

Computational Quantification of Trust Updates

Similar Documents

The implementation of a first-order logic AGM belief revision system

An agents establishes trust with equitable information revelation

Bayesian updating rules and AGM belief revision

AGM Belief Revision, Semantically

Data compression using deterministic experts

The Implementation of a First-Order Logic AGM Belief Revision System (1993)

Probabilistic Belief Expansion and Conditioning (1996)

Equivalent Experience-based Trust Quantification Model

Belief Revision and Trust

Computational Quantification of Trust Updates