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This introductory chapter has broadly discussed the underlying philosophical inspirations underpinning this thesis. Primarily, this revolves around finding a method for modelling the process of practice, through finding dynamic relationships between static contexts in an MCRDR knowledge base. This chapter also identified and discussed the two hypotheses that this thesis aims to address. Finally, this chapter very broadly described the algorithm developed in this project and the areas it may be applied, such as classification, prediction and prudence analysis tasks.

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‘It’s like this,’ he [Pooh] said, ‘When you go after honey with a balloon, the great thing is not to let the bees know you’re coming. Now, if you have a green balloon, they might think you were only part of the tree, and not notice you, and if you have a blue balloon, they might think you were only part of the sky, and not notice you, and the question is: Which is most likely?’

(Milne 1926, p11)

A basic axiom of software development is that any completed system will need to be changed and updated throughout its life span. This is particularly important for knowledge based systems, as Buchanan and Smith (1989) outlines that one of the five desirable attributes of such systems is that they retain flexibility. Conceptually, these systems are designed for adding new knowledge easily, due to being made up of many individual atomic pieces of knowledge or symbols. However, generally each piece is finely woven together with many of the other pieces, making a complex structure of relationships that is, more often than not, impossible to add new components to without introducing errors.

Difficulties can also arise during a system’s construction due to the major bottleneck in the deployment of expert systems, knowledge engineering (Feigenbaum 1977). Knowledge must be elicited from an expert and transformed into a form that is suitable for insertion into the knowledge base (KB), such that it adds to the overall knowledge and does not create contradictions. However, conflicting knowledge from different experts or even from only one expert renders this highly problematic. For instance, Shaw (1988) reports that experimental results show that two experts, at best, only agree on 33.3% of the knowledge base constructed and, at worst, merely 8.3%. Additionally, communication problems between the experts and the knowledge engineers cause the knowledge engineers to have to transform the supplied knowledge into an appropriate form for the knowledge base.

One solution to these engineering and maintenance issues has been through using ideas from situation cognition (SC). SC rejects the notion, that knowledge is a static entity and once captured remains correct. Instead, it claims that knowledge is dependent on the context of its use and that knowledge is generated or, more accurately, reinterpreted at that point. There have been many methodologies developed that attempt to incorporate the context of knowledge. One such group of methodologies, which uses knowledge in context and directly targets the issues of KBS maintenance, is the Ripple-Down Rules (RDR) family of techniques, which are described fully in chapter 3.

This work, however, has only latched onto ideas from weak SC, where context is viewed as static, and has chosen to ignore the concerns posed by advocates for strong SC. Strong SC researchers claim that the effect of context is so strong that any symbolic based representation is fundamentally flawed and such a system can never achieve any true understanding of the environment that it operates within (Menzies 1998). However, strong SC advocates do not recognise the symbolic approach’s success. Nor do they adequately define context and why it renders symbolic reasoning futile.

This chapter will look at the philosophical and theoretical basis of Knowledge Based Systems (KBSs) from three perspectives: knowledge acquisition (KA), knowledge representation (KR) and knowledge maintenance (KM). It will also, briefly look at some of the important expert system (ES) methodologies and tools: Knowledge Acquisition and Design Structuring (KADS), Protégé, Cyc, Case Based Reasoning (CBR), and Data Mining. The second section will review the relatively new field of applying the ideas from situated cognition and briefly examining the effects they have had on knowledge engineering. This will be followed by a brief description of two utilities for aiding the KA of context-based knowledge, Formal Concept Analysis (FCA) and Repertory Grids. Finally, the last section will discuss the differences between weak and strong situation cognition and propose a reinterpretation of SC. Fundamentally, this new interpretation allows for the incorporation of hidden and dynamic contexts into the artificial intelligence interpretation of SC. This new perspective will be discussed along with its implications for symbolic learning. It is believed that adequately providing for all forms of context will