Knowledge acquisition for discrete event systems
using machine learning
Knowledge acquisition for an understanding of discrete event simulation
systems is a difficult task. Machine Learning has been investigated to help
in the knowledge acquisition process. Our approach involves consultation
with a domain expert, and the use of discrete event simulation models and
machine learning as tools for the intelligent analysis of simulated systems.
Current methods for the analysis and interpretation of such systems are
restricted to statistical techniques that say much about the reliability of
an output, but little about the output inter connectivity.
The objective of our work is to improve the ability to interpret the model
to the level of explanation that might loosely be described as
``How the simulated system works''. The new interpretation techniques
are based on knowledge acquisition concerning the system using machine
learning tools. ``How the simulated system works'' is important
for a decision maker's understanding of the system in terms of relationships
between the various parameters, the utilisation of resources
and the location of (potential) bottlenecks. This
sort of understanding is important when managing queuing problems in
production systems, or when planning a new system. The outputs from three
discrete event simulators were processed as machine learning problems. In
these experiments, the simulation outputs were interpreted by two
attribute-based learning programs, namely RETIS and ASSISTANT,
and by an Inductive Logic Programming system, namely MARKUS.
The knowledge gained from the machine learning tools was evaluated
by the domain expert, who considered that this knowledge contributed
to an understanding of the system, and how it might profitably be modified.