Abstract (EN):
This paper presents the AFRANCI tool for the development
of Multi-Strategy learning systems. AFRANCI allows users to build, in
an interactive and easy way, complex systems. Systems are built using a
two step methodology: design of the structure of the system; and fill in
the modules. The structure of the target system is a collection of interconnected
modules. The user may then choose among a variety of learning
algorithms to construct each module. The tool has several built-in Machine
Learning algorithms and interfaces that enable it to use external
learning tools like WEKA or CN2. AFRANCI uses the interdependency
of the modules to determine the sequence of their training. To improve
usability, the tool uses a wrapper that hides from the user the parameter
tuning procedure for each algorithm. In a final step of the design
sequence AFRANCI generates a compact and legible ready-to-use ANSI
C++ open-source code for the final system.
To illustrate the concept we have empirically evaluated the tool in the
context of the RoboCup Rescue domain. We have developed a small
system that uses both neural networks and rules in the same system.
The experiment have shown that a very significant speed up is attained
in the development of systems when using this tool.
Language:
English
Type (Professor's evaluation):
Scientific
Contact:
Rui Camacho
License type: