Using soft computing to build real world intelligent decision support systems in uncertain domains
Whilst the builders of traditional decision support systems have regularly used game theory and operations research, they have rarely used statistical techniques to build intelligent support systems for fields that have weak domain models. Further, the principle tools in the artificial intelligence arsenal were centred on symbol manipulation and predicate logic, while the use of numerical techniques were looked upon with disfavour. We claim that soft computing techniques (such as fuzzy reasoning and neural networks) can be integrated with symbolic techniques to provide for efficient decision making in knowledge-based systems. We illustrate our claim through the discussion of two decision support systems that have been constructed using soft computing techniques. Split-Up uses rules and neural networks to advise on property distribution following divorce in Australia, whilst IFDSSEA uses fuzzy reasoning to assists teachers in New York State to grade essays. We focus on how both systems reason and how they have been evaluated.
soft computing fuzzy reasoning neural networks knowledge discovery evaluation of decision support systems