This paper provides a historical summary of the motivations which have led several research communities to contemplate qualitative techniques. Qualitative reasoning satisfies various problem solving needs in high level decision tasks, embodied in a set of tools which allow deep knowledge to be put in compatible form with software requirements while still remaining realistic. An overview of these mathematical formalisms is presented; qualitative simulation is introduced as one of the most significant outcomes. Finally, some current research issues concerning temporal aspects of qualitative reasoning are discussed.

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.

Some weaknesses of current decision support technologies are discussed. Numerical methods have strong theoretical foundations but are representationally weak, and only deal with a small part of the decision process. Knowledge-based systems offer greater flexibility, but have not been accompanied by a clear decision theory. Theoretical development of symbolic decision procedures is advocated, an approach to the design of decision support systems based on first-order logic is presented, and work on this approach is reviewed. A central proposal is an extended form of inference called argumentation; reasoning qualitatively for and against decision options from generalized domain theories. Argumentation captures a natural and familiar form of reasoning, and contributes to the robustness, flexibility and intelligibility of problem solving, while having a clear theoretical basis. Argumentation was developed initially for medical applications though it may have much wider applicability.

An important aspect of any interactive computer system is its ease of use. On-line assistance is a major contributor to software usability. Conventional on-line assistance includes on-line documentation, or keyword access to textual help descriptions. More sophisticated on-line assistance includes intelligent help which provides immediate assistance for a specific problem, and intelligent training whose main goal is to support the user in learning about the system. These contemporary forms of intelligent assistance represent knowledge about the particular application, the user and the particular tasks the user is to perform. In addition, they possess inferencing capabilities to model the individual user's goals, plans, intentions and knowledge. This paper provides an overview of on-line assistance research, from empirical studies that attempt to determine the nature of effective assistance, to the design of intelligent on-line assistance systems capable of tailoring the assistance to the individual user. For readers interested in more details, recommendations for further reading in the related areas of advice-giving and intelligent help, intelligent user interfaces, critiquing, intelligent tutoring systems, and on-line documentation and hypertext are given.

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.

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.

Finance is a challenging yet appropriate domain for model-based reasoning, an area of research otherwise grounded in classical physics. Among the many features that suggest a model-based approach are that firms have formal internal structures, business entities have idealizable behaviours, and there is a history of formal analysis of business problems. This article discusses the motivations and foundations of the model-based approach, and surveys several existing artificial intelligence programs that exploit its advantages. The survey shows that there are ample opportunities for useful systems and significant research in this area. However, accomplishing either of these goals depends crucially upon moving beyond qualitative models based only on accounting information, which tend not to capture the actual complexities of the domain.

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.

Based on a survey of recent literature, this report aims to highlight the issues associated with the verification and validation of knowledge based systems. The confusion arising from the lack of clear terminology is considered, along with some of the characteristics of knowledge based systems that cause particular difficulties for verification and validation. The various approaches that can be adopted to address these difficulties are discussed, followed by a survey of recent research initiatives.

The author concludes that many of the difficulties associated with the verification and validation of knowledge based systems are a feature of the complexity of the system being built and the manner of its development rather than of the specific technology chosen to implement it.

The representation of physical systems using qualitative formalisms is examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but has now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties, has shifted as the field has developed. The evolution of causal and functional representations is thus examined. Finally, a growing body of work that allows reasoning systems to utilize multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are examined, including mode of behaviour, granularity, ontology, and representational depth.

This paper surveys the verification of expert system knowledge bases by detecting anomalies. Such anomalies are highly indicative of errors in the knowledge base. The paper is in two parts. The first part describes four types of anomaly: redundancy, ambivalence, circularity, and deficiency. We consider rule bases which are based on first-order logic, and explain the anomalies in terms of the syntax and semantics of logic. The second part presents a review of five programs which have been built to detect various subsets of the anomalies. The four anomalies provide a framework for comparing the capabilities of the five tools, and we highlight the strengths and weaknesses of each approach. This paper therefore provides not only a set of underlying principles for performing knowledge base verification through anomaly detection, but also a survey of the state-of-the-art in building practical tools for carrying out such verification. The reader of this paper is expected to be familiar with first-order logic.

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.

The preceding paper on the refinement of rule-based systems by Precce et al. reflects a recent, rapid growth of interest in verification and validation of knowledge based systems. Much of the literature on this subject is thinly spread over conference proceedings, professional periodicals and academic journals, so we have tried to collect together some of the more frequently cited work to assist readers interested in the area.

Many benefits potentially stem from a structured representation and use of “design rationales”, i.e. the deliberations underlying a software design process. Explicitly-represented rationales help us understand the design better so that we can produce a better design, maintain the resulting artifact better, and exploit the cumulated knowledge when we need to redesign it. Explicit representation of the rationales also provide a basis for reviewing or justifying the decisions that have been made, for communicating with other members of the design team more easily, and for defining computer services that support various design activities, such as keeping track of dependencies or managing multiple viewpoints. Technologies, that have recently become available, such as multi-media and distributed databases, provide the necessary ingredients for pursuing these potential benefits seriously. As a result, in the past few year we have seen growing interest in design rationale management.