This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends. Modern technology provides us with access to an increasingly overwhelming array of choices ranging from dating options to software capabilities to movies. However, as a society, we prefer not to turn the power of choice over to an automated system, thereby creating demand for AIbased technologies such as recommenders.

In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especially those dealing with the analysis of the wild animal cases discussed in Berman and Hafner's 1993 ICAIL article. We review the basic ideas about dimensions, as used in HYPO, and point out differences with factors, as used in subsequent systems like CATO. Our goal is to correct certain misconceptions that have arisen over the years.

Donald Michie Volumes 1 --7 are published by Edinburgh University Press and in the United States by Halsted Press (a subsidiary of John Wiley & Sons, Inc.) Volumes 8 -- 9 are published by Ellis Horwood Ltd., Publishers, Chichester and in the United States by Halsted Press (a subsidiary of John Wiley & Sons, Inc.) MACHINE INTELLIGENCE 9 New York - Chichester - Brisbane - Toronto First published in 1979 by ELLIS HORWOOD LIMITED Market Cross House, Cooper Street, Chichester, West Sussex, P019 lEB, England The publisher's colophon is reproduced from James Gillison's drawing of the ancient Market Cross, Chichester No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form of by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission. One intelligent approach to prefaces -- is to have the empty preface. The well prepared reader will form a good idea of the technical programme just from looking at the table of contents; together with the names of the authors, this gives him a good idea of what happened at the symposium. I could try to assess the tallcs and direct the reader's attention to the more interesting communications. But I fear this would be too subjective and unfair to the remaining authors -- all of them equally represented in this book. However, recalling that Spring week in Repino, a resort 20 kilometres from Leningrad on the Bay of Finland and unpopulated at that time of year, I have come to the definite conclusion that the scientific meeting was in its own way unique.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

Segmentation needs to be done Aloimonos (1988), a theory is developed for determining somewhere along the way. If one is working with the twoview the motion of an observer given the flow field over a full case described in Solution Using Point Correspondences 360 degree image sphere. The method is based on the fact (above) and if the motions of the rigid bodies are that the foci of expansion and contraction for an observer small from t1 to t2, the following approach can be tried.

We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.

We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine.

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial provides an overview of artificial intelligence for computational sustainability, and introduces this special issue of AI Magazine.