Special Issue "Probabilistic Causal Modelling in Intelligent Systems"
Deadline for manuscript submissions: 1 August 2018
Prof. Ann E Nicholson
Clayton School of Information Technology, Monash University, Clayton, Victoria 3800, Australia
Website | E-Mail
Interests: artificial intelligence; Bayesian networks; data mining; evolutionary ethics; intelligent agents; knowledge engineering; plan recognition; probabilistic reasoning; user modelling
Probabilistic Causality—the idea that causality is stochastic and that probabilistic dependencies reveal their causal foundations—has come a long way since its origins with the work of Hans Reichenbach in the 1950s. After losing its reductionist pretensions in the 1970s and 1980s in debates within Philosophy of Science, it crashed headlong into the Bayesian network technology emerging from Statistics and Artificial Intelligence (AI). Out of that collision, in the late 1980s, grew some remarkable innovations, including the Causal Discovery programs of Clark Glymour and collaborators in Philosophy and Judea Pearl and others in AI. The technology and new ideas have continued to flow, and at a great pace. This Special Issue on “Probabilistic Causal Modelling” will provide a view of where we have come from, a snapshot of where we are, and a probabilistic prediction of where we are headed.
Dr. Kevin B Korb
Prof. Ann E Nicholson
Mr. Erik Nyberg
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- causal Bayesian networks
- probabilistic graphical models
- probabilistic causality
- information theory
- causal power
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Luke Fenton-Glynn
Affiliation: Department of Philosophy, University College London, Gower Street, London, WC1E 6BT, U.K.
Abstract: Actual (token) causes – e.g. Suzy’s being exposed to asbestos –often bring about their effects – e.g. Suzy’s suffering mesothelioma – probabilistically. I use probabilistic causal models to tackle one of the thornier
difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.