Designing object-oriented representations for reasoning from first-principles

Modeling expert knowledge using `situation-action´ rules is not always feasible in knowledge intensive involving volatile knowledge. In such domains, the size and the dynamic nature of the search space make it extremely difficult to setup a rule base and keep it accurate. An alternative approach suggests that in some domain many of the rules that experts use can be derived by reasoning from `first-principles´. That approach entails modeling experts´ deep knowledge, and emulating the reasoning processes with deep knowledge that allow experts to derive many rules and their justification. The author discusses the design and implementation of an object-oriented representation for the deep knowledge traders utilize in a business domain called hedging, which is knowledge intensive and involves volatile knowledge. He illustrates how deep knowledge modeled using that representation is used to support reasoning from first-principles. The author also analyzes features of that representation that were found to be beneficial in the development of a knowledge-based system called INTELLIGENT-HEDGER