Case-based Learning for Knowledge-based Design Support

We present a general approach to combine methods of interactive knowledge acquisition with methods for machine learning. The approach has been developed in order to deliver knowledge required by support-systems for design-tasks. Learning rests upon a knowledge representation scheme for cases that distinguishes between knowledge needed for subgoaling and knowledge needed for design. We employ traces, i.e., protocols of the user's actions when tackling design-tasks as the initial input for incremental knowledge acquisition. This allows to learn task structures to be used for subgoaling and case-bases plus similarity relations applicable to particular case-bases.