Automatically detecting neighbourhood constraint interactions using Comet

Local Search has been shown to be capable of producing high quality solutions in a variety of hard constraint and optimisation problems. Typically implementing a Local Search algorithm is done in a problem specic manner. In the last few years a variety of approaches have emerged focussed on easing the implementation and creating a clean separation between the algorithm and problem. We present a system which can deduce information about the interactions between problem constraints and the search neighbourhoods whilst maintaining a loose coupling between these components. We apply this technique to the International Timetabling Competition instances and show an implementation expressed in Comet

The NASA/Baltimore Applications Project (BAP). Computer aided dispatch and communications system for the Baltimore Fire Department: A case study of urban technology application

An engineer and a computer expert from Goddard Space Flight Center were assigned to provide technical assistance in the design and installation of a computer assisted system for dispatching and communicating with fire department personnel and equipment in Baltimore City. Primary contributions were in decision making and management processes. The project is analyzed from four perspectives: (1) fire service; (2) technology transfer; (3) public administration; and (5) innovation. The city benefitted substantially from the approach and competence of the NASA personnel. Given the proper conditions, there are distinct advantages in having a nearby Federal laboratory provide assistance to a city on a continuing basis, as is done in the Baltimore Applications Project

Comment: On Random Scan Gibbs Samplers

Comment on ``On Random Scan Gibbs Samplers'' [arXiv:0808.3852]Comment: Published in at http://dx.doi.org/10.1214/08-STS252B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evolving macro-actions for planning

Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research into learning for planning. However, most existing work learns macros that are reusable plan fragments and so observable from planner behaviours online or plan characteristics offline. Also, there are learning methods that learn macros from domain analysis. Nevertheless, most of these methods explore restricted macro spaces and exploit specific features of planners or domains. But, the learning examples, especially that are used to acquire previous experiences, might not cover many aspects of the system, or might not always reflect that better choices have been made during the search. Moreover, any specific properties are not likely to be common with many planners or domains. This paper presents an offline evolutionary method that learns macros for arbitrary planners and domains. Our method explores a wider macro space and learns macros that are somehow not observable from the examples. Our method also represents a generalised macro learning framework as it does not discover or utilise any specific structural properties of planners or domains