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 article 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 have 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 done by recognizing useful analogies that can help in synthesizing parts of a design, recognizing unforeseen problems, and discovering opportunities. This article is the second part of a two-part paper that presents and discusses a variety of association and exploration methods. This part examines exploration 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 artificial intelligence, evolutionary epistemology, and the automated discovery literature.

Over the last ten years, molecular biologists and computer scientists have experimented with various artificial intelligence techniques, notably knowledge based and expert systems, qualitative simulation, natural language processing and various machine learning techniques. These techniques have been applied to problems in molecular data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the representation and recognition of genetically significant structures, the assembly of genetic maps and prediction of the structure of complex molecules such as proteins. The paper outlines the principal methods used, surveys the findings to date, and identifies promising trends and current limitations.

The execution model of Prolog, the first popular language based on Horn Clauses, was designed for efficient evaluation on von Neumann architectures. An alternative process model of execution, better suited for parallel evaluation and reactive programming, has given rise to a new class of languages based on Horn Clause logic, concurrent logic languages. There appears to be a profusion of languages which claim to fall into this class and it is difficult for an initiate to appreciate why each is the way it is. One notable member of this class, FGHC, forms the cornerstone of the Japanese 5th Generation Initiative. Fortunately, the seemingly exponential growth in these languages is only an illusion. A finite number of synchronization mechanisms arise from attempting (or sometimes not attempting) to control two principle synchronization difficulties: the premature binding problem and the binding conflict problem. Suitable combinations of these synchronization mechanisms reproduce the languages of this family. A background knowledge of Prolog is assumed and some familiarity with the difficulties encountered in concurrency would be advantageous.

Computer induction is discussed in the light of concerns recently expressed by Bloomfield about the use of such algorithms in expert systems construction.

Case-based reasoning (CBR) systems reason from experience: they solve new problems by retrieving relevant prior cases and adapting them to fit new situations. In 1988 the first case-based reasoning workshop, sponsored by DARPA, identified theoretical foundations and fundamental issues for case-based reasoning research. Since then, much investigation has examined the CBR process itself, the validity of CBR as a cognitive model, and the application of CBR technology. The results of that work include refinements in theories of the case-based reasoning process, psychological evidence for human case-based reasoning, and the fielding of over 100 CBR applications.

In recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.

Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.