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Natural and artificial reasoning : an exploration of modelling human thinking
Addis T., Springer Publishing Company, Incorporated, Cham, Switzerland, 2014. 199 pp. Type: Book (978-3-319112-85-5)
Date Reviewed: Apr 22 2015

Tom Addis has written a remarkable book in computing that implements the ideas of the philosophers Charles Sanders Peirce and Ludwig Wittgenstein. The origin of this book was an outline that he and his collaborator, the late David Gooding, prepared describing their research on modeling the discovery process in scientific research. Their work involved the study of historical records (research notebooks, especially those of Michael Faraday); the creation of a graphical functional programming language, Clarity, which is available on the site given in the book; critical analysis of the logic of induction, deduction, and abduction in human thought; and the elements of language in the successes and failures of communication, especially as they may apply to human–computer communication. Computer scientists are acquainted with the Turing test of machine intelligence. Addis’s more careful description of machine intelligence is that of a program that modifies itself in response to its experience. It can learn. But so do people. Modeling machine intelligence likewise requires models of biological (human) intelligence. He reports his investigations of the limitations of computer models and poses the question, “Why do we still not have a working model of people that is recognizably human?” How is it possible to model the machine if the biological reference is not modeled?

The first chapter is on intelligence. What is it? How do we recognize it and measure it? He describes intelligence as “that component of thinking that involves insight and reason.” Further, insight comes before reason. The raw material of insight is information. Information is the topic of chapter 2. Information can be measured by calculating the entropy of the statement. Knowledge is different from information in that it is information pertinent to insight. In this chapter, he also introduces the processes of induction and deduction, as traditionally understood, and abduction–the perception of patterns.

Chapter 3 is based on his graphical functional programming language, Clarity, that is used to select actions based on knowledge. He also argues that evolution is a manifestation of intelligence. Chapter 4 is also technically oriented in its focus on expert systems. He distinguishes between closed (as are commonly found) and open inference systems. Chapter 5 is a meta-analysis of the modeling of experiments. He takes a historical viewpoint. Experimentation is an iterative process in which the process is dynamic and socially mediated. Game theory is used to model the process of science. Chapter 6 follows from chapter 5 in its description of the modeling of the process of inference. He addresses measures of confidence, the use of entropy to evaluate hypotheses, and observes that science is a social phenomenon in that communication accelerates progress.

The conceptual basis of science is a belief system based on experimentation and inference. Chapter 7 describes the simulation of the dynamics of revising belief systems. The trajectories of the evolution of belief systems vary depending on contingencies of past development. The only way this can be studied is via simulation to explore alternatives to the sequence of events. The history of the interpretations of the Michelson-Morley experiment and its implications is used as an extended example.

Chapters 8 and 9 are closely based on Wittgenstein’s “Tractatus” and “Philosophical Investigations.” They describe the work Addis did in response to the 2005 Grand Challenge of new approaches to computing. In chapter 8 he describes a framework of “socially sensitive computing” and of referential semantics based on Wittgenstein’s “Tractatus” specifically. Chapter 9 makes the transition to “Philosophical Investigations” in the exploration of irrational reasoning and the limitations and ambiguity of the semantics of computer languages and programs that refer to the world beyond the computer (that is, to models and simulations).

Chapter 10 returns to the problem of capturing knowledge in expert systems and inference engines by studying the differences between first-order logic and second-order logic in an extended example based on a block sliding down an incline. Chapters 11 and 12 are related through the enumeration of five quantitative concepts as hypothesis generators. Chapter 11 is on measures of intelligence (IQ testing, for example), and chapter 12 is on the implementation of hypothesis generation and pattern recognition.

Chapters 13 and 14 treat factors from the emotional realm that may also impinge on thought processes. The author asks the question: “Is it possible for people to have a mutually consistent response to the world given only internal and private references?” The emotional landscape is important. Music and its effect on a person’s reactions and communication is the specific example studied.

This is an outstanding book for investigators in artificial and machine intelligence, simulationists and modelers, as well as experimental psychologists and teachers who have to deal with the development of scientific belief systems in their students.

Reviewer:  Anthony J. Duben Review #: CR143380 (1507-0559)
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