The publications listed below can generally be accessed in 3 different formats:-
The RTF format is used to allow ease of reuse of figures and text, quoted with citation.
Publication citations are given where appropriate. Often material has been edited in publication and differs slightly. Where publications have been substantially shortened, this is noted.
A Learning Model for Forecasting the Future of Information Technology, Brian R Gaines and Mildred L G Shaw, Future Computing Systems 1(1), 31-69, 1986. HTML, RTF, PostScript.
System-theoretic accounts of the epistemological processes underlying knowledge acquisition have been shown to apply to both individual human behavior and social development processes, and to enable algorithms to be developed for computer-based systems modeling. Such accounts are applicable to the upper levels of the hierarchy of autonomous systems to provide models of socio-economic behavior. In this paper they are applied to the development of information technology, and used to account for past events and predict future trends in relevant industries such as computing and genetic engineering. Underlying all developments in information technology is a tiered succession of learning curves which make up the infrastructure of the relevant industries. The paper provides a framework for the industries based on this logical progression of developments. It links this empirically to key events in the development of computing and genetic engineering. It links it theoretically to a model of economic, social, scientific and individual development as related learning processes with a simple phenomenological model. It uses this model to account for past developments in information technology and extrapolates it to predict future trends.
Modeling and Forecasting the Information Sciences, Brian R Gaines, Information Sciences 57-58, 3-22, 1991. HTML, RTF, PostScript.
A model of the development of the information sciences is described and used to account for past events and predict future trends, particularly fifth and sixth generation priorities. The information sciences came into prominence as electronic device technology enabled the social need to cope with an increasingly complex world to be satisfied. Underlying all developments in computing is a tiered succession of learning curves which make up the infrastructure of the computing industry. The paper provides a framework for the information sciences based on this logical progression of developments. It links this empirically to key events in the development of computing. It links it theoretically to a model of economic, social, scientific and individual development as related learning processes with a simple phenomenological model. The fifth generation development program with its emphasis on human-computer interaction and artificial intelligence, and the sixth generation research program with its emphasis on knowledge science are natural developments in the foci of attention indicated by the model.
Adapting to a Highly Automated World, Brian R Gaines, Canadian Engineering Centennial Convention: Proceedings of Electrical Engineering Sessions. IEEE 87TH0186-7, pp.42-49, 1987. HTML, RTF, PostScript.
This paper considers the role of technology in society and the concept of trust applied to technological systems. It analyzes the social and technical mechanisms existing for the containment of problems in terms of their capabilities to adapt to a highly automated world. Our adaption to the automated world that we have created requires engineering disciplines to formalize the social dimension of their activities as much as they have formalized the technological dimension. We can "trust" technology only to the extent that we can trust the engineering professions to accept the responsibility for this formalization. The complexity of modern technological systems and the social structures they serve is in danger of going beyond our conceptual capabilities to understand, anticipate and manage. A significant activity of all engineering professions must be to harness the power of modern information technology, of expert and knowledge-based systems, to enhance their abilities to model and manage the impacts of decisions falling within their professions.
Positive Feedback Processes Underlying the Formation of Expertise, Brian R Gaines, IEEE Transactions on Systems, Man & Cybernetics, SMC-18(6), 1016-1020, 1988. HTML, RTF, PostScript.
Experts may be modeled as managing the inductive dynamics of knowledge acquisition in the knowledge processes of society. Who becomes an expert may be modeled as a random process under the influence of strong positive feedback loops in the social mechanisms giving access to knowledge. These models have implications for the design of expert systems.
Kelly's "Geometry of Psychological Space" and its Significance for Cognitive Modeling, Mildred L G Shaw and Brian R Gaines, The New Psychologist, 23-31, October, 1992. HTML, RTF, PostScript.
Personal construct psychology is a theory of individual and group psychological and social processes that takes a constructivist position in modeling human knowledge but bases this on a positivist scientific position that characterizes conceptual structures in axiomatic terms. It provides a fundamental framework for both theoretical and applied studies of knowledge acquisition and representation. This paper presents Kelly's original intuitions underlying personal construct psychology and links these to its foundational role in cognitive and computational knowledge representation.
The Collective Stance in Modeling Expertise in Individuals and Organizations, Brian R Gaines, (short version appeared in the International Journal of Expert Systems 7(1) 21-51, 1994.) HTML, RTF, PostScript.
This paper is concerned with modeling the nature of expertise and its role in society in relation to research on expert systems and enterprise models. It argues for the adoption of a collective stance in which the human species is viewed as a single organism recursively partitioned in space and time into sub-organisms that are similar to the whole. These parts include societies, organizations, groups, individuals, roles, and neurological functions. Notions of expertise arise because the organism adapts as a whole through adaptation of its interacting parts. The phenomena of expertise correspond to those leading to distribution of tasks and functional differentiation of the parts. The mechanism is one of positive feedback from parts of the organism allocating resources for action to other parts on the basis of those latter parts past performance of similar activities. Distribution and differentiation follow if performance is rewarded, and low performers of tasks, being excluded by the feedback mechanism from opportunities for performance of those tasks, seek out alternative tasks where there is less competition. The knowledge-level phenomena of expertise, such as meaning and its representation in language and overt knowledge, arise as byproducts of the communication, coordination and modeling processes associated with the basic exchange-theoretic behavioral model. The model is linked to existing analyses of human action and knowledge in biology, psychology, sociology and philosophy, and is used to analyze the role of information technology in supporting activities in the lifeworld.
Comparing Conceptual Structures: Consensus, Conflict, Correspondence and Contrast, Mildred L G Shaw and Brian R Gaines, Knowledge Acquisition 1(4), 341-363, 1989. HTML, RTF, PostScript.
One problem of eliciting knowledge from several experts is that experts may share only parts of their terminologies and conceptual systems. Experts may use the same term for different concepts, use different terms for the same concept, use the same term for the same concept, or use different terms and have different concepts. Moreover, clients who use an expert system have even less likelihood of sharing terms and concepts with the experts who produced it. This paper outlines a methodology for eliciting and recognizing such individual differences. It can be used to focus discussion between experts on those differences between them which require resolution, enabling them to classify them in terms of differing terminologies, levels of abstraction, disagreements, and so on. The methodology promotes the full exploration of the conceptual framework of a domain of expertise by encouraging experts to operate in a "brain-storming" mode as a group, using differing viewpoints to develop a rich framework. It reduces social pressures forcing an invalid consensus by providing objective analysis of separately elicited conceptual systems.
An Interactive Visual Language for Term Subsumption Languages, Brian R Gaines, IJCAIÕ91: Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. pp.817-823, 1991. HTML, RTF, PostScript.
A visual language is defined equivalent in expressive power to term subsumption languages expressed in textual form. To each knowledge representation primitive there corresponds a visual form expressing it concisely and completely. The visual language and textual languages are intertranslatable. Expressions in the language are graphs of labeled nodes and directed or undirected arcs. The nodes are labeled textually or iconically and their types are denoted by six different outlines. Computer-readable expressions in the language may be created through a structure editor that ensures that syntactic constraints are obeyed. The editor exports knowledge structures to a knowledge representation server computing subsumption and recognition, and maintaining a hybrid knowledge base of concept definitions and individual assertions. The server can respond to queries graphically displaying the results in the visual language in editable form. Knowledge structures can be entered directly in the editor or imported from knowledge acquisition tools such as those supporting repertory grid elicitation and empirical induction. Knowledge structures can be exported to a range of knowledge-based systems.
Eliciting Knowledge and Transferring it Effectively to a Knowledge-Based System, Brian R Gaines and Mildred L G Shaw, IEEE Transactions on Knowledge and Data Engineering 5(1) 4-14, 1993. HTML, RTF, PostScript.
Knowledge acquisition research supports the generation of knowledge-based systems through the development of principles, techniques, methodologies and tools. What differentiates knowledge-based system development from conventional system development is the emphasis on in-depth understanding and formalization of the relations between the conceptual structures underlying expert performance and the computational structures capable of emulating that performance. Personal construct psychology is a theory of individual and group psychological and social processes that has been used extensively in knowledge acquisition research to model the cognitive processes of human experts. The psychology takes a constructivist position appropriate to the modeling of human knowledge processes but develops this through the characterization of human conceptual structures in axiomatic terms that translate directly to computational form. In particular, there is a close correspondence between the intensional logics of knowledge, belief and action developed in personal