i++ School Newsletter

Week commencing 26 July 2010

Previous Newsletters

Competition Launched

The Broadband Wireless Association, in association with the Digital Communications Knowledge Transfer Network, are sponsoring a competition for small companies and Research Groups to disseminate their latest research outputs and solutions in the New Technology Campus at IBC, a multimedia conference and exhibition in Amsterdam in September. More information is available here or from David Crawford in the REO (crawford@essex.ac.uk).

Essex Researchers Develop Games for I-Phone

The work of Dr Alexei Vernitski and his colleagues in the Department of Mathematics is highlighted in Business Weekly. Dr Vernitski and his team have now formatted their range of electronic puzzles I-Phone or I-Pod Touch apps. Read the full article here.

staff News

Emeritus Professor James Doran

On July 7 2010 Emeritus Professor Jim Doran presented a paper entitled "The MIAP cognitive agent architecture and its potential use in agent-based economic models" at an international workshop on Advances in Agent-Based Computational Economics (ADACE 2010) held at the ZiF Centre for Interdisciplinary Research at the University of Bielefeld in Germany.

Papers Accepted

Mario Graff and Riccardo Poli, Practical Performance Models of Algorithms in Evolutionary Program Induction and other Domains, Artificial Intelligence Journal (impact factor: 3.0), forthcoming.

Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters.

In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems - symbolic regression and Boolean function induction - and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our  technique by also accurately modelling  the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance  point of view, which are not detected by ordinary experimentation.

 Luca Citi, Riccardo Poli, and Caterina Cinel, Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin’s speller, Journal of Neural Engineering (impact factor: 3.7), forthcoming.

The P300 is an endogenous event-related potential which is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because users of P300-based BCIs need no special   training.  However, P300 waves are hard to detect and, therefore,  multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling  this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller.

The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.