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2014 | Book

Insight and Reason Read chapter Read first chapter

Natural and Artificial Reasoning

An Exploration of Modelling Human Thinking

Author: Tom Addis

Publisher: Springer International Publishing

Book Series : Advanced Information and Knowledge Processing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

What are the limitations of computer models and why do we still not have working models of people that are recognizably human? This is the principle puzzle explored in this book where ideas behind systems that behave intelligently are described and different philosophical issues are touched upon.

The key to human behavior is taken to be intelligence and the ability to reason about the world. A strong scientific approach is taken, but first it was required to understand what a scientific approach could mean in the context of both natural and artificial systems. A theory of intelligence is proposed that can be tested and developed in the light of experimental results. The book illustrates that intelligence is much more than just behavior confined to a unique person or a single computer program within a fixed time frame. Some answers are unraveled and some puzzles emerge from these investigations and experiments.

Natural and Artificial Reasoning provides a few steps of an exciting journey that began many centuries ago with the word ‘why?’

Table of Contents

Frontmatter
1. Insight and Reason
Abstract
One way of understanding a natural process or mechanism is to build a working model and then see if the model has some of the behavior or features of the observed phenomenon. In this book I will describe an attempt at understand the nature of people through computer modeling. It is hoped that this understanding will lead to the possibility of increasing our abilities through artificial mechanisms.
Tom Addis
2. Information and Intelligence
Abstract
The key to understanding intelligence is ‘information’, since it is information that is the raw material used to gain insight. So we need to appreciate ‘information’ in a very precise way. The next section will explore a formal definition of ‘information’ to see if this will help us. It may also give us a different perception of intelligence.
Tom Addis
3. Identifying Intelligence
Abstract
Formally identifying intelligence would seem like a gross simplification of what has always seemed a complex and slightly mysterious process. What we have done is created a starting point for our investigation by proposing a concrete description we can then try to use. We will expect that this initial description to be inadequate in explaining many aspects of our experience of intelligence, but it will give us a starting point to grow something better as in the following story. The ‘concrete description’ is the ‘stone’ in the soup.
Tom Addis
4. Knowledge Science
Abstract
Once we have defined and implemented a simple version of intelligence, the problem now arises as to how this might be extended to be useful. One way is to use it in conjunction with the expert knowledge of some professional. To capture this expertise on a computer is particularly important when it comes to rare, expensive or vanishing skills. Intelligence itself is of no real value unless it can be used in the world of human affairs; it is this view that stimulated the idea of an ‘Expert System’. An Expert System is intended to capture the knowledge and skills of an expert in a computer program so that such a program can either replace an expert or amplify a novice’s knowledge to the point of being equivalent to an expert. The questions then arise of how we might harvest this knowledge and represent it in a computer, and how we can use such knowledge.
Tom Addis
5. Modelling Experiments
Abstract
Professor David Gooding (Nov 1947–Dec 2009), a science historian, and I worked together for many years on the topic of modelling the science process. These models were validated from examples drawn from history. This chapter presents some of these examples.
Tom Addis
6. Modelling Inference
Abstract
Historians and students of scientific method know that scientists evaluate hypotheses and theories comparatively, not in isolation. In the early stages of the development of a new field, many hypotheses may be proposed. Scientists generally seek to narrow down the range of potential hypotheses while increasing their precision. Nevertheless, attempts to improve the empirical adequacy of theories via experiments sometimes lead to further hypotheses, introduced to protect other, more fundamental assumptions of a theory. For example, evidence against the existence of luminiferous ether arose through Michelson and Morley’s experiments, which were designed to produce a definitive empirical support for this core assumption of the wave theory of light. The Lorentz-Fitzgerald contraction hypothesis was introduced to save the ontological commitment to the ether in the face of this evidence because the ether was considered essential to the wave theory of light.
Tom Addis
7. Simulating Belief and Action
Abstract
In Chap. 6 we noted that the concept of ‘truth’ was limited to deductive inference only. For other kinds of inference we have introduced the new concept of ‘belief’ to replace ‘truth’. Our approach is now to represent experiments with a computer model that allows for variability in four key components of the scientific process.
Tom Addis
8. Programming and Meaning
Abstract
During April 2005 at the University of York, an international workshop was held called ‘The Grand Challenge in Non-Classical Computation’. The purpose of the conference was to stimulate those doing research and development in Computer Science to consider new approaches to computing. The stimulation for this challenge was triggered by the novel concept of ‘Quantum Computing’, which seemed to offer the potential for ultra fast parallel processing using quantum mechanical principles such as superposition and entanglement.
Tom Addis
9. Irrational Reasoning
Abstract
In Chap. 8 referential semantics was shown to fit a computer programming language since the meaning of a word could be related to the bit as an object in the machine. This is not the complete story. This is because computer languages have a dual semantics in that the program signs (e.g. the names/labels given to data items, procedures and sub-routines) at the highest level also have referents in the world other than the computer.
Tom Addis
10. Knowledge for Design
Abstract
Having dealt early with the issues of intelligence and the dual semantics of computer programs in Chap. 9, we must now address the issue of how to design a knowledge-based system.
Tom Addis
11. Measures of Intelligence
Abstract
We will now describe a program, devised and constructed by Dr. Mohamad S. Zakaria. This program uses the models of only the three forms of inference, ‘abduction’, ‘deduction’ and ‘induction’ as described in Chap. 4. Abstraction will be done manually and is therefore pre-defined. The roles of the three forms of inference in creating and validating a hypothesis will be tested using a simple IQ test. This test requires the inferring of a hypothesis that is the generator of a sequence of numbers. The origin of these hypotheses was taken from Eysenck’s numerical sequence IQ tests. The IQ test sequences and the extensions of the sequences generated by applying the inferred hypothesis are used as a testing ground for the implementation.
Tom Addis
12. Implementing Intelligence
Abstract
I have specified in Chap. 11 the different concepts as defined by Zakaria 1994) in his thesis which characterize sequence types. However, this does not help in identifying what concept should be tried when given an IQ test sequence. For this it is required to identify features of sequences and associate them with the range of generating concepts. The problem is that the features assigned can apply to more than one concept. This issue can be readdressed as a form of pattern recognition in which the pattern of features will identify the most likely concept to apply to a particular sequence.
Tom Addis
13. Figuratively Speaking
Abstract
Dr. David Billinge, a Computer Science lecturer at Portsmouth University, gives regular pre-concert lectures at the local Guildhall. His interest in music and language raised the question of how the communication of the emotional content of music can be justified using referential semantics. This was particularly puzzling because emotions do not have any externally shared reference points. This apparent lack of external references for emotion raises the interesting primary question, “How can the semantics of emotion ever be established?”
Tom Addis
14. Seeking Allies
Abstract
In this chapter I will describe in some detail a formal computer model of inferential discourse based on the belief system (see Chaps. 6 and 7). The key issue is that a logical model in a computer, based on rational sets, can usefully model a human situation grounded on irrational sets (see Chap. 9). The background of this work is explained elsewhere, as is the issue of rational and irrational sets. The model is based on the Belief System and it provides a mechanism for choosing queries based on a range of belief. We explain how it provides a way to update the belief based on query results, thus modelling others’ experience by inference. We also demonstrate that for the same internal experience, different models can be built for different actors.
Tom Addis
Metadata
Title
Natural and Artificial Reasoning
Author
Tom Addis
Copyright Year
2014
Electronic ISBN
978-3-319-11286-2
Print ISBN
978-3-319-11285-5
DOI
https://doi.org/10.1007/978-3-319-11286-2

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