Many different approaches to game playing have been suggested including alpha-beta search, temporal difference learning, genetic algorithms, and coevolution. Here, a powerful new algorithm for neuroevolution, Neuro-Evolution for Augmenting Topologies (NEAT), is adapted to the game playing domain. Evolution and coevolution were used to try and develop neural networks capable of defeating an alph...

It is essential that the reasoning of an expert system can be ex-­ plained and justified. In the paper simple ideas for providing com-­ prehensive explanations of deductions are presented. The method is to transform the proof tree to a more comprehensive proof tree from which a neat explanation directly can be extracted. The transformation of proof trees is a way to view the problem of explaini...

Successful pathogenic organisms have developed mechanisms to thrive under extreme levels of iron restriction. Haem-iron represents the largest iron reservoir in the human body and is a significant source of iron for some bacterial pathogens. NEAT (NEAr Transporter) domains are found exclusively in a family of cell surface proteins in Gram-positive bacteria. Many NEAT domain-containing proteins,...

There are a number of efforts towards building applications that leverage temporal information in documents. The demonstration of our NEAT (News Exploration Along Time) prototype system that we propose here, is an attempt towards building an intuitive and exploratory interface for search results over large news archives using timelines. The demonstration uses the New York Times Annotated Corpus...

Complexification is an essential concept in evolutionary robotics that develops dynamic neural networks over generations with the aim of increasing their capabilities. Artificial networks networks are complexified by altering, adding and removing nodes and connections from the neural network structure. The dimensionality of the input layer in a neural network also plays a significant role in th...

The larger the size of the data, structured or unstructured, the harder to understand and make use of it. One of the fundamentals to machine learning is feature selection. Feature selection, by reducing the number of irrelevant/redundant features, dramatically reduces the run time of a learning algorithm and leads to a more general concept. In this paper, realization of feature selection throug...