Kenneth M. Ford

Knowledge acquisition as a constructive modeling activity

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer.” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application, and that support knowledge‐based system development from a life‐cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. to counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism.

ICONKAT: an integrated constructivist knowledge acquisition tool

Abstract In this paper, we report on a continuing research effort aimed at the development of an integrated knowledge acquisition system, ICONKAT. We describe the components of the tool and discuss how they may be used to facilitate the design, construction, testing, maintenance and explanation of knowledge bases. ICONKATs knowledge elicitation subsystem, based on both personal construct theory and assimilation theory, interactively assists the domain expert in the task of building a model of his or her expertise. ICONKAT employs a collection of modeling primitives (i.e. the glue) as the material basis for the construction of a conceptual domain model. The maintenance subsystem provides support tools for use by the knowledge engineering team, as well as the domain expert, when testing the systems performance, refining the knowledge base, and maintaining the overall system. The components of the maintenance subsystem employ a variety of mediating representations (e.g. concept maps, repertory grids) to furnish various perspectives of the evolving domain model as embodied in the modeling primitives. Moreover, the domain model that emerges from the knowledge acquisition process is subsequently exported from the development environment to the delivery environment where it serves as the foundation of the explanation capability for the deployed system. ICONKAT is currently employed in the design and construction of an expert system for the diagnosis of first pass functional cardiac images.

An approach to knowledge acquisition based on the structure of personal construct systems

A research effort aimed at the development and unification of the prerequisite underlying theoretical foundations for an adequate approach to knowledge elicitation from repertory grid data is described. A theory of confirmation that incorporates the basic tenets of personal construct psychology directly into the logic as a basis for the determination of relevance is offered, thus strengthening the logic and extending personal construct psychology. These largely theoretical developments are applied to the representation and analysis of repertory grid data. The concept of an alpha -plane is introduced as a binary decomposition of repertory grid data that furnishes the realization of construct extensions (or ranges of convenience) needed to determine the range of relevance of a particular generalization or hypothesis. In addition, they provide the uniquely determined string of incidences required by any application of Bundys truth functional incidence calculus. The theories are applied to the design and construction of NICOD-a semiautomated medical knowledge acquisition system. The system has been successfully employed in the elicitation of valuable heuristic radiological knowledge (mammography) that the domain experts (radiologists) were otherwise unable to articulate. >

An Approach to the Linguistic Summarization of Data

We introduce a new approach to the summarization of data based upon the theory of fuzzy subsets. This new summarization allows for a linguistic summary of the data and is useful for both numeric and non-numeric data items. It summarizes the data in terms of three values: a summarizer, a quantity in agreement, and a truth value. We also discuss a procedure for investigating the informativeness of a summary. Finally, we describe Summarizer, an implementation of this new approach to the summarization of data.

Terraforming cyberspace

Like preterraformed Mars, cyberspace currently offers a lonely, dangerous, and relatively impoverished environment for software agents. Although promoted as collaborative, agents do not easily sustain rich, long-term, peer-to-peer relationships, let alone any semblance of meaningful community involvement. Rather than just building smarter and stronger agents, researchers must transform the wasteland of cyberspace itself, making it a safe and habitable environment for both agents and humans. The paper discusses how the basic infrastructure for beginning a terraforming effort is becoming more available. Designed specifically to exploit next-generation Internet capabilities, grid-based approaches provide a universal source of dynamically pluggable, pervasive, and dependable computing power, while guaranteeing levels of security and duality of service that will make new kinds of applications possible.

On Babies and Bathwater: A Cautionary Tale

■ One should not throw out the baby with the bathwater, according to an old aphorism. Some popular recent positions in AI thinking have done just this, we suggest, by rejecting the useful idea of mental representations in their overenthusiastic zeal to correct some simplifications and naiveties in the way traditional AI ideas have sometimes been understood. These “situated” perspectives correctly emphasize that agents live in a social world, using their environments to help guide their actions without needing to always plan their futures in detail; but they incorrectly conclude that the very idea of mental representation is mistaken. This perspective has its intellectual roots in parts of recent sociological thinking which reject the entire fabric of western science. We discuss these ideas and disputes in the form of an illustrated fable concerning nannies and babies.

Knowledge acquisition and constructivist epistemology

The most fundamental step in the knowledge acquisition phase of the development of an expert system is the elicitation of knowledge from a skilled individual. The knowledge acquisition phase has typically involved the knowledge engineer’s working closely with a specialist to elicit relevant knowledge from the latter’s domain. This is typically a tedious and ad hoc cycle that consists of extensive verbal interviews followed by the construction of prototypes, testing, and more interviews. This approach has two significant drawbacks—it has been extremely laborious, and domain experts often have difficulty articulating their knowledge in forms useful to the knowledge engineer. Indeed, it has been suggested (Feigenbaum & McCorduck, 1983) that “the problem of knowledge acquisition is the critical bottleneck in artificial intelligence” (p. 80).

Storm-LK: A Human-Centered Knowledge Model for Weather Forecasting

STORM-LK (System To Organize Representations in Meteorology-Local Knowledge) is a Human-Centered system that used the CMap Tools© software to represent the knowledge and reasoning of expert forecasters. It demonstrates the feasibility of using Concept-Mapping to generate large-scale multi-media knowledge models. STORM-LK can support knowledge preservation, distance learning and collaboration, and navigation through the data that are used in weather forecasting.

Why Go¨del's theorem cannot refute computationalism

Abstract Godels theorem is consistent with the computationalist hypothesis. Roger Penrose, however, claims to prove that Godels theorem implies that human thought cannot be mechanized. We review his arguments and show how they are flawed. Penroses arguments depend crucially on ambiguities between precise and imprecise senses of key terms. We show that these ambiguities cause the Godel/Turing diagonalization argument to lead from apparently intuitive claims about human abilities to paradoxical or highly idiosyncratic conclusions, and conclude that any similar argument will also fail in the same ways.

The triples rule

A fundamental stance taken in human-centered computing is that information processing devices must be thought of in systems terms. At first blush, this seems self-evident. However, the notion has a long history, and not just in systems engineering. In this new age of symbiosis, machines are made for specific humans for use in specific contexts. The unit of analysis for cognitive engineering and computer science is a triple: person, machine and context The triples rule asserts that system development must take this triple as the unit of analysis, which has strong implications, including a mandate that the engineering of complex systems should include detailed cognitive work analysis. It also has implications for the meaning of intelligence, including artificial intelligence.