SKEDSOFT

Each of the paradigms has a contribution to make to the ultimate solution of a problem, as is pointed out below:

Symbolic AI systems can contribute with:

• Rigid theory for symbol manipulation; theorem proving
• Rigorous exact reasoning mechanisms, including chain reasoning and different reasoning strategies
• Universal computational models (e.g., the production systems)

Fuzzy systems can contribute with:

•  Well-developed fuzzy logic theory
• Humanlike reasoning mechanisms; using linguistic terms
• Accommodating commonsense knowledge, ambiguous knowledge, imprecise but rational knowledge
• Universal approximation techniques
• Robustness, fault tolerance
• Low cost of development and maintenance
• Low computational cost

Neural networks can contribute with:

• Learning from data
• Modeling empirical behavior of humans
• Universal approximation techniques
• Good generalization
• Methods for extracting knowledge from data
• Methods for data analysis
• Associative memories and pattern-matching techniques
• Massive parallelism
• Robustness

These three groups of methods as complementary for three reasons:

 (1) They facilitatedealing with different kinds of knowledge representation, different inference, different accuracy andfault tolerance, etc.

 (2) Each of them may be superior to the others when solving a concrete subtask of aproblem; for example, the symbolic approach and the connectionist approach have demonstratedstrengths in solving different tasks. The former has been successful in high-levelcognitive tasks, whereas the latter is more successful in low-level perceptual and learning tasks.

 (3) One method may beapplied to improve the performance of another.