An Environmental Impact Assessment Learning Apprentice System

In engineering problems with a weak domain theory, such as environmental evaluation, machine learning techniques should bring to bear any existing background knowledge so as to guide the knowledge acquisition process. This paper proposes an Interactive Inductive Learning System (IILS) which can use background knowledge provided by the experts to avoid many incorrect or incomplete induced heuristics. Through the specification of guidance relations, the expert can force the rule induction system to focus on a subset of relevant attributes and training instances. A guidance relation consists of a set of attribute constraints, where each constraint instructs IILS on the role of an attribute for the induction of a concept. IILS was linked to the database of an Environmental Evaluation Support System (EESS) for the induction of impact estimation and comparison heuristics. The induced heuristics are generalizations of past assessments stored in the database and permit the prediction of impact levels for cases not previously encountered by EESS. The integration of IILS transforms the EESS into an environmental impact assessment learning apprentice system capable of acquiring and improving evaluation heuristics through its normal use.