There are a number of soft computing technologies that have been shown to be effective when operating on problems involving uncertainty. One of many guiding factors in determining what technology to apply is the nature of the information we have available on which to act. Sometimes we have data that contains a buried wealth of information, other times we have knowledge (rules). Each of these characteristics leads us towards a different technology that is best suited to acting on that kind of information.
Another factor relates to dealing with a tradeoff between interpretability and precision. It is a characteristic of some soft reasoning technologies that you can either get a simplified but easily understood answer, or you can have a more precise answer for which the means that it was arrived at are not readily available. Sometimes simplification is necessary or useful, but it comes with a cost. Understanding issues like these and finding the correct balance is one of the barriers of entry for providing high quality intelligent solution. The table below shows some of the differences between a few of the more commonly used soft technologies and illustrates one possible way in which they might be utilized in the three layer reasoning model.
|
Automated Knowledge Acquisition |
Coping With Brittleness |
High-Level Reasoning |
Low-Level Reasoning |
Explanation |
Knowledge based systems |
● |
● |
● ● ● ● ● |
● |
● ● ● ● ● |
Rule Induction |
● ● ● ● |
● ● |
● ● ● |
● ● |
● ● ● |
Fuzzy Systems |
● |
● ● ● ● ● |
● ● ● |
● ● ● ● ● |
● ● ● ● ● |
Neural Networks |
● ● ● ● ● |
● ● ● ● |
● |
● ● ● ● ● |
● |
Genetic-Algorithms |
● ● ● ● ● |
● ● ● |
● ● ● |
● ● ● |
● ● ● |
Property Assessment of different reasoning techniques.
Example Reasoning Layers
A point that is sufficiently important to bear repeating is that no single soft technology is right for all tasks and there is a strong synergistic effect when several complimentary technologies are used together. The problem has been that with the exception of a few binary hybrid combinations and cooperating agent technology, there has been little progress made regarding techniques to integrate multiple types reasoning technologies into a single semantic system in a reusable way. Typically when more than one soft technology is employed, the solution takes the form of a binary hybrid. The problem with the binary hybrids is twofold. First, the use of only two reasoning technologies artificially limits the coverage of diverse problem spaces. Second, the coupling of the technologies is typically tight and targeted at a very specific problem, thereby making the combination difficult to reuse.
Suran Goonatilake and Sukhdev Khebbal, Intelligent Hybrid Systems , (New York: Wiley, 1995)