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

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

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

Urban wastewater reuse for crop production in the water-short Guanajuato River Basin, Mexico

Water quality / Waste waters / Water reuse / Water resource management / River basins / Irrigation water / Crop production / Water use / Data collection / Case studies / Mexico / Guanajuato River Basin / Tula Irrigation District

Non-equilibrium mechanics and dynamics of motor activated gels

The mechanics of cells is strongly affected by molecular motors that generate forces in the cellular cytoskeleton. We develop a model for cytoskeletal networks driven out of equilibrium by molecular motors exerting transient contractile stresses. Using this model we show how motor activity can dramatically increase the network's bulk elastic moduli. We also show how motor binding kinetics naturally leads to enhanced low-frequency stress fluctuations that result in non-equilibrium diffusive motion within an elastic network, as seen in recent \emph{in vitro} and \emph{in vivo} experiments.Comment: 21 pages, 8 figure