Among the most important challenges for contemporary AI research are the development of methods for improved robustness, adaptability, and overall interactiveness of systems. Interactiveness, the ability to perform and react in tight co-operation with a user and/or other parts of the environment, can be viewed as subsuming the other two. There are various approaches to addressing these problems, spanning from minor improvements of existing methods and theories, through new and different methodologies, up to completely different paradigms. As an example of the latter, the very foundation of knowledge-based systems, based on a designer's explicit representation of real world knowledge in computer software structures, has recently been questioned by prominent members of the KBS community. In the present paper, some foundational issues of the knowledge-based paradigm are reviewed, and the main arguments of the critiquing position are discussed. Some of the deficiencies of current appr...