INTEGRATION OF PROBLEM-SOLVING TECHNIQUES
Still another form of integration is the combining of two or more
problem-solving techniques such as expert systems, fuzzy logic, neural
nets, model-based reasoning, qualitative physics, simulators,
case-based reasoning, and parallel computing. Most of these
combinations are still in the prototyping and experimental stages.
Nevertheless, they are indicative of the depth to which the technology
is being explored in Japan.
Specific examples include:
- Fuzzy logic and neural networks: AdIn, a small Japanese ES company,
has developed a prototype system that uses fuzzy membership functions
within a neural network framework as a means of accelerating the
learning process and improving problem solving performance. The
application is the interpretation of images.
- Deep and shallow reasoning: a two-level expert system built by
Toshiba (Chapter 2) is intended to
diagnose unforeseen failures in a physical system. This system uses a
shallow knowledge diagnostic system to handle foreseen failures, backed
up by a deep knowledge system to tackle unforeseen problems. The deep
knowledge system employs model-based reasoning and also employs a fuzzy
- Model-based reasoning and parallel computing: NEC showed us a
prototype model-based diagnostic system operating on a parallel
- Case-based and rule-based reasoning: a system developed at Toshiba
which uses a recursive application of case-based reasoning; case-pieces
are used to repair the mismatches in the primary case.
- CAD and knowledge acquisition: Toshiba has developed a knowledge
acquisition tool in which a design process for highly stylized objects
is acquired by monitoring a CAD system.
Published: May 1993; WTEC