Artificial Intelligence and Scientific Method [Hardcover]

Donald Gillies (Author)

Book Description

Publication Date: September 26, 1996 | ISBN-10: 0198751583 | ISBN-13: 978-0198751588

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies focuses on two key topics within AI: machine learning in the Turing tradition and the development of logic programming and its connection with non-monotonic logic. Demonstrating how current views on scientific method are challenged by this recent research, he goes on to suggest a new framework for the study of logic. He draws on work by such seminal thinkers as Bacon, G�del, Popper, Penrose, and Lucas to address the hotly contested question of whether computers might become intellectually superior to human beings. These topics will attract a wide readership from followers of advances in artificial intelligence, to students and scholars of the history and philosophy of science.

Editorial Reviews

Review

`crisp, clear and concise' THES

`An old-fashioned monograph: tightly argued, heavily referenced.' New Scientist

`This is an original and very interesting book ... it is obviously a good place to start for anyone who would like to examine the notions of logic and scientific method in the light of recent developments in artificial intelligence.' Peter Ohrstrom, Aalborg University

`if you are not a philosopher this book is worth reading - but for interest alone ... If you know any philosophers, however, you should make sure they read it.' Mike James, Scientific Computing World, June 1997

'offers an interseting view on recent developments in AI, particularly in machine learning, form a philosopher's perspective. the book is of value to all AI practioneers' Zentralblatt Math

About the Author

Donald Gillies is Professor of the Philosophy of Science and Mathematics at King's College, London. His books include An Objective Theory of Probability (1973), Revolutions in Mathematics (1992), and Philosophy of Science in the Twentieth Century (1993). He was the editor of the British Journal for the Philosophy of Science from 1982 to 1985.

Product Details

• Hardcover: 190 pages
• Publisher: Oxford University Press, USA (September 26, 1996)
• Language: English
• ISBN-10: 0198751583
• ISBN-13: 978-0198751588
• Product Dimensions: 8.8 x 5.7 x 0.6 inches
• Shipping Weight: 13.1 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,761,584 in Books (See Top 100 in Books)

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6 of 6 people found the following review helpful:

4.0 out of 5 stars A prelude to fully automated scientific discovery, February 15, 2004

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This review is from: Artificial Intelligence and Scientific Method (Paperback)

This book is interesting in that it attempts to cast questions of the scientific method into the language and concepts of artificial intelligence (AI), instead of in terms of philosophy, as is usually done. The two main camps of philosophy of science, namely inductivism, represented by Sir Francis Bacon, and that of falsificationism, represented by Sir Karl Popper are both discussed in the context of AI. Two examples, one dealing with the discovery of the laws of planetary motion due to Johannes Kepler, and the discovery of sulphonamide drugs, are chosen to illustrate the author's ideas. The author asserts that these examples do not entirely agree with either Bacon or Popper. Kepler used an "intuitive induction" which involved human insight and creativity, which is quite different from the "mechanical induction" of Bacon. The discovery of sulphonamide drugs was a more "mechanized" process, but the author believes it was more of a "mechanical falsification" rather than Baconian induction. In addition, this discovery, he asserts, has introduced the concept of heuristics, which of course is ubiquitous in artificial intelligence.

The author is certainly correct in his belief that Baconian induction, as outlined in the Novum Organum of 1620, has been applied only sparingly in the development of science. He believes that this is changing though thanks to the advent of machine intelligence. Indeed, the existence of machines able to recommend and design experiments, analyze the data from these experiments, and then formulate hypotheses to explain the data was reported just weeks ago in a major scientific journal. These machines were based on inductive logic programming in the guise of a language called PROGOL, which performs relational learning and was just getting started as this book went to press. The author does discuss relational learning in this book, and details algorithms for machine learning that are based on inductive rules of inference and background knowledge and data in these rules. He also discusses the role of testing and falsification in the actual process of using inductive rules of inference in order to produce the final result.

The specific machine learning algorithms that the author does discuss are ID3 and GOLEM, with ID3 being a "top-down" and attribute-based learning algorithm, and GOLEM a "bottom-up" and relational learning algorithm. ID3 makes use of rules that take the from of decision trees, begins with simple and general rules, and these are then modified or refuted to produce more specific generalizations. The author discusses the role that these programs have in negating the Popperian assertion that induction "is a myth". Even more interesting is the author's belief that these programs in fact illustrate the "mechanical" principles of induction that Bacon laid down in 1620. In fact, he states that he has been unable to find an example of the use of Baconian "mechanical" induction in the history of science before the advent of these languages.

Naturally logic programming and its main example PROLOG will arise in any discussion of machine intelligence, and it does so here. PROLOG as a language based on nonmonotonic logic is discussed in detail along with the "closed world assumption", this being done in order to construct a "new framework for logic". This framework involves viewing logic as made up not only of inferences but also a "control component", the latter of which follows either its own autonomous control decisions, or those provided by the programmer. PROLOG is viewed as a language that introduces control into deductive logic, and its development an example of a process that replaced "craft skill by mechanization". Generation (and checking) of proofs in mathematics is given as an example of this craft skill, having been done to date by trained mathematicians who have the `craft skills' to carry this out. PROLOG is able to construct proofs via its control mechanism and has both a declarative and procedural interpretation. The author shows in what sense PROLOG can lead to what he calls a `new framework for logic', and consequently as evidence that logic is really empirical, and not `a priori' as is typically assumed. The empiricism of logic was argued in another context, namely that of quantum mechanics, but the author believes that `quantum logic' has failed to support the empiricism of logic. PROLOG, he asserts, is a better example of the empirical nature of logic.

The author also addresses the possibility of constructing a detailed example of inductive logic, which he believes was not done in traditional circles of logic, these being concerned mostly with deductive inference. After discussing the history of the divergence between the schools of deductive and inductive logic, he expands further on his paradigm of logic as being `inference + control' in showing how ideas from conformation theory can be used as a control mechanism in deductive logic. To illustrate just how this could be done, the author draws on the work of J. Cussens, A. Hunter, and A. Srinivasan in a class of nonmonotonic logics called `prioritized' logics. These authors show that a prioritized logic will allow the inference of formulas that are `most preferred", with preferences being accomplished relative to some preference criterion. The author shows how to use relative-frequencies to estimate conformation values. What is most interesting about the work of these three authors, and the author points this out emphatically, is that it may permit the differentiating of one system of logic from another using experimental criteria in the context of a particular application. The author discusses how these authors were able to carry out the empirical testing of different systems of logic using the GOLEM programming language. An explicit example in bioinformatics is discussed, and the author concludes from this example that the choice of logic will depend on the interests of a particular user. Empirical evidence can thus decide on the logic used in a domain, and this choice may also depend on the requirements of the user.

I did not read the last chapter of the book, so its review will be omitted.