The basic categories of research in knowledge-based systems include: knowledge representation, knowledge use (or problem-solving), and knowledge acquisition (i.e., machine learning and discovery).
In knowledge representation, the key topics are concepts, languages, and standards for knowledge representation. There are many issues involved in scaling up expert systems: defining the problems encountered in the pursuit of large knowledge bases; developing the infrastructure for building and sharing large knowledge bases; and actually accumulating a large body of knowledge, for example, common sense knowledge or engineering and technical knowledge.
Knowledge use, or problem-solving, research efforts involve the development of new methods for different kinds of reasoning, such as analogical reasoning, reasoning based on probability theory and decision theory, and reasoning from case examples.
The first generation of expert systems was characterized by knowledge bases that were narrow and, hence, performance that was brittle. When the boundary of a system's knowledge was traversed, the system's behavior went from extremely competent to incompetent very quickly. To overcome such brittleness, researchers are now focusing on reasoning from models, principles and causes. Thus, the knowledge-based system will not have to know everything about an area, as it were, but can reason with a broader base of knowledge by using the models, the principles, and the causation.
The quest for a large knowledge base boils down to the problem of access to distributed knowledge bases involving multiple expert systems and developers. The effort to develop the infrastructure needed to obtain access is a research area called knowledge sharing. The goal of the knowledge sharing research is to overcome the isolation of first-generation expert systems, which rarely interchanged any knowledge. Hence, the knowledge bases that were built for expert systems in the 1980s did not accumulate.
A major issue of expert systems research involves methods for reasoning with uncertain data and uncertain knowledge. One of the most widely adopted methods is called "fuzzy logic" or "fuzzy reasoning," especially in Japan, where fuzzy reasoning is the object of much research attention and much scrutiny on the part of American researchers.
Very lately, there has come on the scene the research topic of neural networks -- networks of distributed components operating in parallel to make classification decisions. The links between neural networks technology and expert system technology are being explored.
Finally, research is underway to explore the use of new parallel computing methods in the implementation of expert systems and advanced knowledge-based systems. The new wave of computing is multi-processor technology. The question is, what will be the impact of such high-performance parallel computing activities on expert system techniques?