ABSTRACT
This chapter focuses on the impact this technology can have on intelligent tutoring systems (ITSs) and also the teaching of knowledge-intensive domains, such as biology, history, and weather forecasting. It has become a truism in artificial intelligence (AI) that knowledge is crucial. It is well understood in many complex domains that the best teaching method is case-based. Law and business schools teach cases rather than rules. Recent research in AI suggests that there is a valid psychological reason for this, and we are now designing AI teaching methods that use cases to teach. Case-based reasoning has a number of potential advantages for solving AI problems. Natural language understanding remains one of the hardest problems in AI. Language understanding is always memory search. The Direct Memory Access Parser (DMAP) is an implementation of an understander based on memory search. DMAP determines which memory structures account for the text as a whole. CBR model of what learning a knowledge-rich domain involves.
