Abstract
This paper presents the AFRANCI tool for the development of Multi-Strategy learning systems. Designing a Multi-Strategy system using AFRANCI is a two step process. The use interactively designs the structure of the system and then chooses the learning strategies for each module. After providing the datasets all modules as automatically trained. The system is aware and takes into consideration the inter-dependency of the modules. The tool has built-in learning algorithms but can use external programs implementing the learning algorithms. The tool has the following facilities. It allows any user to design in an interactive and easy fashion the structure of the target system. The structure of the target system is a collection of interconnected modules. The user may then choose the different learning algorithms to construct each module. The tool has several built-in Machine Learning algorithms has has interfaces that enables it to use external learning tools like WEKA and CN2. AFRANCI uses the interdependency of the modules to determine the sequence of training. For each module the system uses a wrapper to tune automatically the parameters of the learning algorithm. In the final step of the design sequence AFRANCI generates a compact and legible ready-to-use ANSI C open-source code for the final system.

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The HARVARD system is a general purpose system adequate for Knowledge Discover in Databases (KDD) running in general purpose PCs and based on distributed computing over a connected network of PCs. In this paper we discuss the extension of HARVARD to interact with a Grid Computing setting. This extension, called HARVARD-g, enable the HARVARD system to schedule task to the Grid and therefore largely increase its available computational power.

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We propose a model, called Incremental Correction. (IC) model to address the problem of reverse engineering human control skills using the Behavioural Cloning methodology. The proposed model is based on the concept of closed loop or feedback control. The controllers are induced via Machine Learning tools from traces of human expert control performance. Controllers using the IC model exhibit an increase in robustness and a reduction in encoding complexity when compared to previous models used in behavioural cloning.