AISB convention 2017

  In the run up to AISB2017 convention, I've asked Joanna Bryson, from the organising team, to answer few questions about the convention and what comes with it. Mohammad Majid al-Rifaie ( Tu...


Harold Cohen

Harold Cohen, tireless computer art pioneer dies at 87   Harold Cohen at the Tate (1983) Aaron image in background   Harold Cohen died at 87 in his studio on 27th April 2016 in Encintias California, USA.The first time I hear...


Dancing with Pixies?...

At TEDx Tottenham, London Mark Bishop (the former chair of the Society) demonstrates that if the ongoing EU flagship science project - the 1.6 billion dollar "Human Brain Project” - ultimately succeeds in understanding all as...


Computerised Minds. ...

A video sponsored by the society discusses Searle's Chinese Room Argument (CRA) and the heated debates surrounding it. In this video, which is accessible to the general public and those with interest in AI, Olly's Philosophy Tube ...


Connection Science

All individual members of The Society for the Study of Artificial Intelligence and Simulation of Behaviour have a personal subscription to the Taylor Francis journal Connection Science as part of their membership. How to Acce...



AISB opportunities Bulletin Item

CALL FOR PAPERS: Special issue in Preference Learning and Ranking

PREFERENCE LEARNING AND RANKING Special Issue in Machine Learning


Methods for learning and predicting preference models from explicit or implicit preference 
information and feedback are among the very recent research trends in machine learning and 
knowledge discovery. Approaches relevant to this area range from learning special types of 
preference models such as lexicographic orders over collaborative filtering techniques for 
recommender systems and ranking techniques for information retrieval, to generalizations of 
classification problems such as label ranking. Like many complex learning tasks that have 
recently entered the stage in the field of machine learning, preference learning deviates 
strongly from the standard machine learning problems of classification and regression. It 
is particularly challenging as it involves the prediction of complex structures, such as 
weak or partial order relations, rather than single values. Moreover, training input will 
not, as it is usually the case, be offered in the form of complete examples but may comprise 
more general types of information, such as relative preferences or different kinds of indirect 
feedback. Authors are invited to submit full papers presenting original results on any aspect 
of machine learning and games. An ideal contribution to this special issue would be strongly 
motivated by applications to commercial or classical games and focused on research issues 
relevant to the topics described below. Papers specific to game theory should not be submitted 
to this special issue (there will be forthcoming special issue on this topic).


Topics of interest to the special issue include, but are not limited to

  * quantitative and qualitative approaches to modeling preferences and
    different forms of feedback and training data;
  * learning utility functions and related regression problems;
  * preference mining, preference elicitation, and active learning;
  * learning relational preference models;
  * generalizations or special forms of classification problems, such as
    label ranking, ordinal classification, and hierarchical classification;
  * comparison of different preference learning paradigms (e.g.,
    learning of single models vs. modular approaches that decompose the
    problem into subproblems);
  * ranking problems, such as learning to rank objects or to aggregate
  * methods for special application fields, such as web search,
    information retrieval, electronic commerce, games, personalization,
    or recommender systems.


Titles and Short Abstracts: 	/December 31, 2011/
Submission Deadline: 	        /January 10, 2012/

If you intend to submit a paper to the special issue, please send a short abstract per E-mail to 
both editors before December 31, 2011.

Submissions to the special issue must be submitted like regular submissions to the journal. 
Instructions can be found at .

Each submission will be reviewed according to the standards of the Machine Learning Journal. 
All inquiries regarding this special issue should also be directed to the guest editors.

We aim for a publication of the special issue in late 2012/early 2013.


Eyke Hllermeier   (Philipps-Universitt Marburg)
Johannes Frnkranz (TU Darmstadt)