The Future of Thinking:

Computers and Minds in mid-Twentieth Century Imaginations

Stephanie Dick

A Glimpse of the Future

On December 10, 1996, the New York Times published an article entitled “Computer Math Proof Shows Reasoning Power.”1 The article followed an announcement that a computer program had solved an open mathematical problem, one that had resisted the efforts of logicians for more than seventy years. The program was called the EQP, for “equational prover,” and it was designed to prove logical theorems.2 Computers had been used to prove theorems before, as early as the mid-1950s. For the most part, earlier successes were thought to represent comparatively simple problem-solving or mere brute force case checking. But this program, according to its primary architect William McCune, represented a “quantum leap forward”—it exhibited reasoning power.3

McCune and his colleagues at the Argonne National Laboratory had a vision of mathematics in which computers would not be mere tools for mathematical research but would rather become agents of mathematical research; “colleagues,” “mentors,” “co-workers,” or “collaborators.”4 They would be able to acquire and exhibit those faculties, like reasoning, that constituted mathematical work.

But what was the character of reasoning—that most mathematical of faculties—that the EQP was said to have exhibited? The EQP’s accomplishment was officially announced first in the Journal of Automated Reasoning – the flagship of an eponymous research field that developed through the late 1960s.5 The field was developed in opposition to the better-known research domain of Artificial Intelligence. The latter sought to simulate human reasoning by computer. Practitioners of automated reasoning, on the other hand, wanted to explore the possibility of new forms of reasoning in which computers could in fact surpass people, rather than simulate them. In imagining a computer-inclusive vision for the future of mathematics, McCune and his colleagues forged quite new meanings of “reasoning” itself.

Cover Art for Automated Reasoning: Introduction and Applications, authored by L. Wos, R. Overbook, E. Lusk, J. Boyle from the Argonne National Laboratory, 1984.]

A Glimpse of the Past

In 1854 English logician George Boole published one of the earliest texts in modern mathematical logic: An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities. The title of the work is usually abbreviated to The Laws of Thought, but the rest matters too. Boole intended to “investigate the fundamental laws of those operations of the mind by which reasoning is performed” and “to give expression to them in the symbolic language of a Calculus.”6 For Boole, the field of logic—the study of what follows from what—would be founded on those laws of human thinking. The rules of thought would be the laws of logical inference.

The development of logic became particularly important for late nineteenth century mathematics. At that time, there was growing concern that mathematical knowledge might be inconsistent and rife with contradictions. These sentiments emerged in part because of the discovery of certain paradoxes within mathematics, especially set theory. It also emerged as mathematicians became increasingly dispersed among subfields of research that often adhered to different standards and did not speak to one another.7

In order to salvage their discipline from these troubling contradictions and diffusions, some mathematicians set out in search of new foundations that could be used to standardize and rebuild mathematics from the bottom up, eliminating the possibility of paradox and providing step by step justification for mathematical truth claims. Many believed that logic, understood as the science of deduction, grounded on the laws of human reasoning, was the best possible such foundation.

Many attempts to represent the basic units of human reasoning within a formal deductive system followed Boole’s. Gottlob Frege published his Begriffsschrift: Eine der arithmetischen nachgebildete Formelsprache des reinen Denkens in 1879. Like Boole, Frege attempted to craft a formalized “concept notation,” a language of pure thought, modeled upon that of arithmetic. Later, two British mathematicians, Alfred North Whitehead and Bertrand Russell, would work within such a logical system to construct proofs of all existing results from the branches of mathematics.

They were after a “complete enumeration of all ideas and steps in reasoning employed in mathematics.”8 Their proofs would make their resulting conclusions certain because they would be constructed according to the supposedly self-evident steps and primitive ideas that constituted human thinking.9 Primitive notions like “disjunction” and “negation,” borrowed from natural language, were given a mathematical formalization. From these were crafted primitive propositions, for example, “Anything implied by a true elementary proposition is true” and “if q is true then ‘p or q’ is true.” These primitive propositions were the basic building blocks from which proof would be constructed.10 Mathematics, formulated this way, would be founded on logic. Logic would in turn be founded on the laws of human reasoning that Boole, Frege, Russell and Whitehead, and others sought to distill. These laws would also offer what seemed like a perfect recipe for the creation of “thinking machines” – if computers could be made to execute the laws of deduction, touted by logicians as the laws of thought itself, would they not be thinking?

A Future Failed

The question of whether or not machines could think long predated the advent of modern digital computing, coming in many forms alongside changing theories and experiences of machines and minds.11 But with computers, that dream seemed more possible than ever. And mathematical logic seemed an especially attractive problem domain in this regard. For one thing, computers are exemplary rule-followers – they can only do what they can be explicitly instructed to do according to a finite set of executable rules. Logic came with a ready-made formal calculus of explicit axioms and rules of inference. Moreover, this formal infrastructure, as we have seen, was designed to capture the most basic elements of human thinking itself. If computers could follow these “laws of thought” they would be able to prove theorems, and in so doing exhibit one of the faculties most closely associated with human intelligence. It is not surprising, then, that logical proof was among the earliest nonnumeric domains that researchers in the United States sought to automate following the advent of modern digital computers.

One intuitive approach to the automation of proof would be to provide a computer with the axioms and inference rules of logic. The computer could then be programmed to apply the latter to the former in order to deduce any provable logical consequences. Users could input a logical proposition and run the computer to see if any permitted sequences of inference led to that proposition. If so, the series of steps taken by the program in that sequence would constitute a proof, and the computer would have deployed the “laws of thought” to solve a mathematical problem. It didn’t work.

In spite of the incredible speed and efficiency with which computers (even in the 1950s) could execute instructions, practitioners quickly discovered that this method of proof-seeking on its own was so inefficient as to be unusable. Not only did it lead to an exponential explosion of data given how many inferences could be made, but there was also no way to know when or if a proof would ever be found. Larry Wos, from Argonne, observed in 1964 that “ if a proof existed of the desired theorem, it would be captured in the steadily expanding sets of instances.” However, the “disastrous rate of growth of these set […], spelled the doom of exhaustive instantiation.”12 Simply applying inference rules to axioms would not, it turned out, enable computers to prove theorems or to exhibit anything that practitioners recognized as intelligent, thinking behavior.

In light of this realization, research in the automation of proof turned towards the development of strategies for more efficient search. Practitioners each adopted different strategies, motivated by particular visions of the computer, its limitations and its possibilities for future mathematical work. Some believed that the “laws of thought” from nineteenth century logic in fact didn’t capture how mathematicians looked for proofs, and set out to discover and then automate actual human theorem-proving practice. Others, however, weren’t interested in getting computers to do what people do. They wanted to know what computers could do that people couldn’t. What paths of reasoning, proof, and discovery did computers make possible that were not otherwise possible?

Resolution

One year after Wos published his discouraging remarks about exhaustive instantiation, John Alan Robinson published a paper called “A Machine Oriented Logic Based on the Resolution Principle.”13 In it, he introduced what would become one of the most powerful tools for computer proof, and with it, a new vision for reasoning itself. Robinson, a classicist and a logician, characterized existing traditions of logical analysis as follows:

Traditionally, a single step in a deduction has been required, for pragmatic and psychological reasons, to be simple enough […] to be apprehended as correct by a human being in a single intellectual act. No doubt this custom originates in the desire that each single step of a deduction should be indubitable, even though the deduction as a whole may consist of a long chain of such steps. […] Part of the point, then, of the logical analysis of deductive reasoning has been to reduce complex inferences, which are beyond the capacity of the human mind to grasp as single steps, to chains of simpler inferences, each of which is within the capacity of the human mind to grasp as a single transaction.14

What Robinson hit upon was that the study of logic and the study of the so-called primitive psychological and cognitive elements of human deduction had been one and the same. For Boole, Frege, Whiteheand, and Russell, the basic elements of logic were the basic operations of the human mind.

Robinson saw in computing the possibility for a new logic, one designed to capitalize on the power of computing for following complex rules rather than one that accommodated the human minds: “When the agent carrying out the application of an inference principle is a modern computing machine, the traditional limitation on the complexity of inference principles is no longer very appropriate. More powerful principles, involving perhaps a much greater amount of combinatorial information-processing for a single application, become a possibility.”15 Robinson did not lament the differences between people and computers – it was precisely in those differences that he saw an exciting future for mathematics.

Robinson offered one such machine-oriented logic in 1965, grounded on what he called the “Resolution Principle.” The laws that grounded human-oriented logics were meant to be clear, in need of no further justification, like the age-old syllogism: All men are mortal. Socrates is a man. Therefore, Socrates is mortal. The conclusions of Resolution-based inference were not so obvious. From the premises (1) All hounds howl at night; (2) Anyone who has any cats will not have any mice; (3) Light sleepers do not have anything which howls at night; (4) John has neither a cat or a hound; Resolution would permit the conclusion: If John is a light sleeper, then John does not have any mice.16 Resolution was designed precisely not to accommodate human psychology but rather to circumvent it, capitalizing on the speed and efficiency of computing machines.

Resolution was incredibly powerful, and increasingly it and its variations were built in to the most powerful theorem-provers of the second half of the twentieth century, programs like the EQP which was celebrated as demonstrating “reasoning power” in the New York Times, in 1996. Its “reasoning power” was tied to this transformed view in which resolution and computer-oriented processes like it counted as reasoning. In this sense, the human faculty was just an example of a larger category of “reasoning” rather than the definition of it.

A whole new research field—called Automated Reasoning or Automated Deduction—emerged in the wake of Resolution’s success. This field was created in opposition to that more famous discipline, whose visions of the future are so much more familiar: Artificial Intelligence (AI). AI practitioners did not recognize Resolution as a step towards their goals. Reflecting on the history of their field, Argonne practitioners wrote:

When [our colleague] submitted a lovely paper on qualified hyperresolution to one of the main AI journals, a senior editor did not even send it out for refereeing; he just returned a short note stating, “the JACM [Journal of the Association of Computing Machinery] is still publishing such papers, although I don’t know why.” This last phrase symbolized the broader AI community in our eyes. Like perceptrons, formal logic had […] been evaluated and found lacking.17

Artificial Intelligence sought to make computers like people. Automated Reasoning sought to find problem-solving paths that would be inaccessible to humans, to open up reasoning and logic themselves, to untether them from the laws of human thought. Part of the criticism from AI, and others, concerned the fact that Resolution and tools like it, took mathematical proof further and further from human view, displacing the indubitable clarity and understanding that earlier logical systems had tried to provide.

The problem that the EQP solved in 1996 when its “reasoning power” was announced in the New York Times concerned none other than Boolean Logic, the formal system developed out of An Investigation of the Laws of Thought, on which are Founded the Mathematical Theories of Logic and Probabilities. The problem, called Robbins’ Conjecture, was part of an ongoing project to find the “simplest” set of axioms for the logical system, its most basic and primitive ideas. In the 1930s, Herbert Robbins proposed a new set of three axioms, conjecturing that they were the simplest sufficient set, but offered no proof. None was found until the problem was given to the EQP in 1996.18 Where logicians, equipped with their faculties and practices of reasoning, failed for seven decades, the EQP found a proof within mere minutes.19 The computer program, executing “automated reasoning,” provided new information about that century old logic that was meant to capture the laws of human thought but seemed, in this case, to elude them.

  1. Gina Kolata, “Computer Math Proof Shows Reasoning Power,” New York Times (December 10, 1996).

  2. The program was developed primarily by William McCune, under the directorship of Lawrence Wos within the Applied Mathematics division at the Argonne National Laboratory.

  3. Kolata, “Computer Math Proof.” The phrase “reasoning power” is attributed to Wos.

  4. See, for example, Lawrence Wos, “Solving Open Questions with an Automated Theorem-Proving Program” in 6th Conference on Automated Deduction [Lecture Notes in Computer Science, Vol. 138] (1982), 1-31.

  5. William McCune, “Solution of the Robbins Problem,” in Journal of Automated Reasoning 19/3 (1997): 263-276.

  6. George Boole, The Laws of Thought on which are founded the Mathematical Theories of Logic and Probabilities (Cambridge, UK: Macmillan and Co., 1854), 1.

  7. This period has often been called “The Foundations Crisis”, following Herman Weyl’s publication “über die neue Grundlagenkrise der Mathematik,” Mathematische Zeitschrift 10 (1921): 39-79.

  8. Alfred North Whitehead and Bertrand Russell, Principia Mathematica Vol. 1 (Cambridge, UK: Cambridge University Press, 1910), 1.

  9. For a clever and insightful criticism of this notion of self-evidence in late nineteenth-century logic, see Lewis Carroll, “What the Tortoise Said to Achilles,” in Mind 4/14 (1895): 278-280.

  10. Whitehead and Russell took these primitive propositions to be both self-evident and irreducible. They were more commonly called “axioms” in twentieth century logic.

  11. See, for example, Margaret Boden, Mind as Machine: A History of Cognitive Science (Oxford: Oxford University Press, 2006); Phil Husbands, Owen Holland, Michael Wheeler, eds. The Mechanical Mind in History (Cambridge, MA: The MIT Press, 2008).

  12. Larry Wos. “The Unit Preference Strategy in Theorem Proving”, in The Collected Works of Larry Wos, Vol. 1 (Hackensack, NJ: World Scientific Publishing Company, 2001), 17-28 [originally American Federation of Information Processing Society, Proceedings 26 (1964), 615-621].

  13. John A. Robinson, “A Machine-Oriented Logic Based on the Resolution Principle,” Journal of the Association for Computing Machinery 12/1 (1965): 23-41.

  14. Robinson, “A Machine-Oriented Logic,” 23.

  15. Robinson, “A Machine-Oriented Logic,” 24.

  16. This example is borrowed from Gordon S. Novak Jr.: “Resolution Example and Exercises,” http://www.cs.utexas.edu/users/novak/reso.html.

  17. Ross Overbeek, Ewing Lusk, “Wos and Automated Deduction at ANL: The Ethos” in Automated Reasoning and its Applications; Essays in Honor of Larry Wos (Cambridge, MA: The MIT Press, 1997): 1-12, quote on 6.

  18. McCune’s presentation of the result and the proof are available in “Robbins Algebras are Boolean” (2006 [1996]).

  19. The core lemmas of the proof were proved in about 5 seconds and 2319 seconds respectively. See McCune, “Solution of the Robbins Problem,” 274.

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