Designing is a skill central to many human tasks. Designers are constantly producing newer and better artifacts, generating innovative solutions to problems in our world. This paper looks at innovation, and research that is aimed at developing theories and methodologies for innovative design. We view design as a process of association and exploration. These two approaches are fundamental to innovation. The aim of exploration is to generate a large variety of design alternatives by breaking away from the norms by looking in unlikely places, and by relaxing binding constraints. Exploration exposes possibilities that would not normally hâve been considered, possibilities that may serendipitously lead to innovative solutions. Association, on the other hand, attempts to exploit previous design experiences in a new design context. This is donc by recognizing useful analogies that can help in synthesizing parts of a design, recognizing unforeseen problems, and discovering opportunities. This paper is the first part of a two-part paper that presents and discusses a variety of association and exploration methods. This part examines association-based techniques, some of which have been used in actual design Systems, and others that point to the solution of some open questions in design research. We develop these ideas by examining connections between design research and other disciplines such as cognitive psychology, artificial intelligence, the history of science, and the creativity literature.

The financial domain has seen considerable knowledge Systems activity. Some of the efforts have resulted in deployable Systems, but many others have met with much less success. Commentators have attempted to discern broad, universal indicators which explain or predict success. More recently, however, it has become clearer that a principal cause of the difficultes encountered is the incongruence between the real world task and the System architecture employed by developers. Proceeding from the latter perspective, this paper concerns itself with knowledge Systems for the provision of investment related financial advice. The various tasks involved are differentiated and correlated to the System architectures employed by developers. It is seen that while simple rulebased Systems suffice for some tasks, others indicate the need for techniques such as Case-Based Design.

A unifying framework for concept-learning, derived from Mitchell's Generalization as Search-paradigm, is presented. Central to the framework is the generic algorithm Gencol. Gencol forms a synthesis of existing concept-learning algorithms as it identifies the key issues in concept-learning: the representation of concepts and examples, the search strategy and heuristics, and the operators that transform one concept-description into another one when searching the concept description space. Gencol is relevant for practical purposes as it offers a solid basis for the design and implementation of concept-learning algorithms. The presented framework is quite general as seemingly disparate algorithms such as TDIDT, AQ, MIS and version spaces fit into Gencol.