Association Systems — Task Structure Level Associative Hybrid Architecture

Abstract

The hybrid architectures described in chapters 3, 4, and 5 are based on fusion, transformation, and combination of intelligent methodologies like expert systems, fuzzy logic, neural networks, and genetic algorithms. The concepts of fusion, transformation, and combination have been used in different situations or tasks, and by applying top-down and/or bottom-up knowledge engineering strategy. All these hybrid architectures have a number of advantages in that the hybrid arrangement is able to successfully accomplish tasks in various situations. However, these hybrid architectures also suffer from some drawbacks. These drawbacks can be explained in terms of the quality of solution and range of tasks covered (see Figure 6.1). Fusion, and transformation architectures on their own do not capture all aspects of human cognition related to problem solving. For example, fusion architectures result in conversion of explicit knowledge into implicit knowledge, and thus loose on the declarative aspects of problem solving. Thus, they are restricted in terms of the range of tasks covered by them. The transformation architectures with bottom up strategy get into problems with increasing task complexity. Therefore the quality of solution suffers when there is heavy overlap between variables, where the rules are very complicated, the quality of data is poor, or data is noisy. Also, because they lack explicit reasoning, the range of tasks covered by them becomes restricted. The combination architectures cover a range of tasks because of their inherent flexibility in terms of selection of two or more intelligent methodologies.