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Publication details
B. Bredeweg. 1992.
Expertise in Qualitative Prediction of Behaviour.
Ph.D. thesis, University of Amsterdam, Amsterdam, The Netherlands.
Short abstract to each chapter
Chapter 0: Long Abstract
In this thesis we present a unified approach to qualitative
prediction of behaviour, both as a knowledge level description and
in terms of a detailed account of an implemented system that performs
state of the art qualitative reasoning. The integrated framework is
based on the three major approaches to qualitative reasoning: the
component centred approach (deKleer, 1884), the process
centred approach (Forbus, 1984), and the constraint centred
approach (Kuipers, 1986).
Chapter 1: Introduction
Research on qualitative reasoning about the physical world has
provided a number of major results, which at a first glance
appear to be rather different (cf. Bobrow, 1984): the
component centred approach (deKleer, 1984), the process
centred approach (Forbus, 1984), and the constraint centred
approach (Kuipers, 1986). Based on these
initial approaches qualitative reasoning has gradually evolved into
an independent area of research concerned with `automated
reasoning about the physical world using qualitative representations'
(cf. Weld, 1990).
Many researchers in the area of qualitative reasoning agree that
there are similarities between the three main approaches, but little
effort has been spent on uncovering what they are. The goal of the
research presented in this thesis is to construct a theory of
qualitative prediction of behaviour that encompasses the original
approaches and that points out the essential conceptualisations of
this problem solving task, thus enabling a better understanding
of the similarities and differences between the original approaches.
Chapter 2: Approaches to Qualitative Reasoning
This chapter describes the state of the art of qualitative
reasoning in three subsections. First an introduction to the field
is given. The purpose of this introduction is to allow the reader
to become familiar with the objectives in this area of artificial
intelligence. The second section gives a detailed description
of the three main approaches to qualitative reasoning. The last
section discusses the main problems within the area of qualitative
prediction. In this discussion we will concentrate on the problems
concerning the three approaches mentioned before.
Chapter 3: Modelling Problem Solving
This section describes a theory for modelling problem solving
behaviour, based on the
$KADS$ methodology for building Knowledge Based Systems (KBS)
(Wielinga, 1986; Breuker, 1987; Wielinga, 1988; Breuker, 1989;
Hayward, 1987).
We will use this methodology as a method for integration of the
three main approaches to qualitative reasoning described in the
previous section. It is therefore relevant that we give a detailed
account of the important aspects of this methodology.
Chapter 4: Qualitative Prediction of Behaviour
This chapter describes an
integrated conceptual framework for qualitative prediction of
behaviour
(cf. Bredeweg, 1988; Bredeweg, 1989; Bredeweg, 1990).
In order to clarify the boundaries between prediction
and other related tasks, such as modelling, the first section
classifies the tasks relevant to behaviour prediction according
to the criteria proposed by the theory discussed in the previous
chapter. Having set these boundaries the description of the four layer
model for qualitative prediction of behaviour is presented. In
particular, the discussion of the framework focuses on an extended
world view for representing partial behaviour models, the
representation of parameter specific quantity spaces, an
integrated set of parameter relations with additional functionality
for causal value correspondence, and a transformation step
between states of behaviour that encorporates an explicit selection
and ordering of possible terminations.
Having described the problem
solving roles (meta classes) in detail, the canonical
inferences, from a conceptual point of view, turn out to be relatively
straightforward. However, realising their problem solving potential
in a computer program is a complex matter. The algorithms
developed for that purpose are discussed in the next chapter
which describes the design and implementation of the conceptual
model.
Chapter 5: Problem Solving Behaviour in GARP
This chapter describes how the conceptual framework for qualitative
prediction of behaviour can be transformed into a design model for the
construction of a computer program. A design model in KADS consists of
three views. The functional view (5.1) describes
the functions that must be realised by the artifact. In particular, this
section discusses some of the additional functionality that is required,
besides problem solving, for implementation of the artifact (for example,
the interaction with the user).
The most interesting problem to solve in this chapter is the
construction of a behavioural view of the artifact. Algorithms must
be developed that realise the problem solving behaviour required by
the inferences in the conceptual model. Our solution to this problem
is described in two sections. Section (5.2.1) describes
how the meta-classes from the conceptual model are mapped onto design
elements, and section (5.2.2) describes how these
design elements are used by algorithms for realising the problem solving
behaviour that is specified by the knowledge sources.
The physical view describes how the design elements
and algorithms are composed into the different physical modules
which constitute the actual artifact (5.3).
The implemented program is called GARP,
which is an acronym for General Architecture for Reasoning about
Physics.
The last section describes two prediction models, one of the cooling
mechanism of a refrigerator and one of heart diseases. Both models
are implemented in GARP and illustrate important aspects of the
problem solving behaviour manifested by GARP.
Chapter 6: Cognitive Plausibility
In this chapter we investigate the cognitive plausibility of the
conceptual framework for qualitative prediction of behaviour
(cf. Bredeweg, 1991). We
compare think-aloud protocols of human subjects predicting the
behaviour of a complex configuration of balances with a computer
model of the same problem solving task, implemented in GARP.
The contents of this chapter are as follows. The first section
explains why cognitive plausibility is important. The second section
discusses the nature of the problems on balances in more detail. The
third section focuses on potential strategic knowledge that
subjects may use during a behaviour prediction task. The fourth section
describes the protocol analysis of the problem solving task and
discusses to what extent the framework for qualitative prediction
of behaviour fits the think-aloud protocol data. The last section
summarises and further discusses the important results .
Chapter 7: Reflective Improvement
Strategic reasoning, in terms of the KADS four-layer model, is rarely
employed in previous approaches to qualitative reasoning (see
chapter 4). However, as discussed in
the previous chapter, it is precisely
this strategic layer that should contain the knowledge for reasoning
about the knowledge represented in the other layers. This chapter
describes how the strategic knowledge in the model of expertise can be
operationalised as a reflective component `on top of' the artifact. In
particular, this chapter discusses ways of improving the problem solving
behaviour of GARP. These improvements are based on the strategic
reasoning found in the protocols of human problem solving
which was described in the previous section. In addition, the framework
for strategic reasoning is based on the notion of reasoning about
a problem solver.
Chapter 8: Conclusions and Outlook
In this thesis we have presented an integrated approach to qualitative
prediction of behaviour, both as a knowledge level description and
in terms of a detailed account of an implemented system that performs
state of the art qualitative reasoning. Protocol analysis of human
problem solving behaviour supports the cognitive plausibility
hypothesis of the conceptual model and thereby its utility for
knowledge acquisition. In addition, the approach has been augmented
with an initial step towards reflective competence assessment and
improvement. In the following sections the contributions
of our research to these issues are further discussed. The last
section points out a number of issues that are worthwhile for further
investigation.
Last modified on May 9th, 2001
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