----------------------------------------------------------------------------- 

      Machine Learning Journal Special Issue on Fusion of
      Domain Knowledge with Data for Decision Support
      ----------------------------------------------------------------------------- 


                      >>> Second Call for Papers <<<


Statistics and machine learning are data-oriented tasks in which domain 
models are induced from data. The bulk of research in these fields 
concentrates on inducing models from data archived in computer databases. 
However, for many problem domains, human expertise forms an essential part 
of the corpus of knowledge needed to construct models of the domain. The 
discipline of knowledge engineering has focused on encoding the knowledge 
of experts in a form that can be encoded into computational models of a 
domain. At present, knowledge engineering and machine learning remain 
largely separate disciplines. Yet in many fields of endeavor, substantial 
human expertise exists alongside data archives. When both data and domain 
knowledge are available, how can these two resources effectively be 
combined to construct decision support systems?

The aim of this special issue of the Machine Learning journal is to allow 
researchers to communicate their work on integrating domain knowledge with 
data (knowledge-data fusion; theory revision; theory refinement) to a 
general machine learning audience. Emphasis is on sound theoretical 
frameworks rather than ad hoc approaches. Of particular interest are papers 
that combine clear theoretical discussion with practical examples, and 
papers that compare different approaches.

Possible frameworks for knowledge-data fusion include probabilistic 
(Bayesian/belief) networks, possibilistic logics and networks, hybrid 
neuro-fuzzy networks, and inductive logic programming.

Topics of interest include (but are not limited to):
* Practical applications of knowledge-data fusion. What lessons have been 
learnt from attempts to apply knowledge-data fusion to real-world decision 
problems?
* How are the various knowledge representation and inference frameworks 
that permit induction theoretically related to each other?
* What frameworks enable an existing induced model, such as a neural 
network, to be incorporated into a proposed knowledge-based system?
* How can knowledge-data fusion be applied to temporal data?

Submitted papers must not exceed 30 pages and must conform to the Machine 
Learning journal style. Please see the associated Web site for further 
submission details: http://www.btinternet.com/~rdybowski/mlkdf/

This Call for Papers is *not* restricted to those who presented at the UAI 
2000 Workshop on Knowledge-Data Fusion: it is open to everyone who has an 
interest in this topic.

Please direct any enquiries to Richard Dybowski: rdybowski@btinternet.com


Schedule
--------------
Paper submission deadline: June 1, 2001
Authors' notification of decisions: September 1, 2001
Final revised papers due: December 15, 2001


Guest Editors
--------------------
Richard Dybowski
Kathryn Blackmond Laskey (George Mason University)
James Myers (Ballistic Missile Defense Organization)
Simon Parsons (Liverpool University)
-------------------
MLnet community list
http://www.mlnet.org/mlnet2/services/mlnet-community.html