Probabilistic Causal Modelling in Intelligent Systems
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (1 August 2018) | Viewed by 8535
Special Issue Editors
Interests: philosophy of science; computer simulation; Bayesian reasoning; evaluation theory; argument analysis
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; Bayesian networks; data mining; evolutionary ethics; intelligent agents; knowledge engineering; plan recognition; probabilistic reasoning; user modelling
Special Issue Information
Dear Colleagues,
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
Dr. Erik Nyberg
Guest Editors
Manuscript Submission Information
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Keywords
- causal Bayesian networks
- probabilistic graphical models
- probabilistic causality
- information theory
- causal power
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