Susan G. Sterrett

Demonstrating the different roles that logic plays in the disciplines of computer science, mathematics, and philosophy, this concise undergraduate textbook covers select topics from three different areas of logic: proof theory, computability theory, and nonclassical logic. The book balances accessibility, breadth, and rigor, and is designed so that its materials will fit into a single semester. Its distinctive presentation of traditional logic material will enhance readers' capabilities and mathematical maturity. The proof theory portion presents classical propositional logic and first-order logic using a computer-oriented (resolution) formal system. Linear resolution and its connection to the programming language Prolog are also treated. The computability component offers a machine model and mathematical model for computation, proves the equivalence of the two approaches, and includes famous decision problems unsolvable by an algorithm. The section on nonclassical logic discusses the shortcomings of classical logic in its treatment of implication and an alternate approach that improves upon it: Anderson and Belnap's relevance logic. Applications are included in each section. The material on a four-valued semantics for relevance logic is presented in textbook form for the first time. Aimed at upper-level undergraduates of moderate analytical background, Three Views of Logic will be useful in a variety of classroom settings. Gives an exceptionally broad view of logic. Treats traditional logic in a modern format. Presents relevance logic with applications. Provides an ideal text for a variety of one-semester upper-level undergraduate courses

In this paper I discuss the relationship between model, theories, and laws in the practice of experimental scale modeling. The methodology of experimental scale modeling, also known as physical similarity, differs markedly from that of other kinds of models in ways that are important to issues in philosophy of science. Scale models are not discussed in much depth in mainstream philosophy of science. In this paper, I examine how scale models are used in making inferences. The main question I address in this talk is ``How are fundamental laws involved in the construction of, and inferences drawn from, experimental scale models?'' We shall see that there is a refreshing alternative to the mainstream view that models can serve only as intermediaries between theory and experiment. Using the methodology of scale models, one can use observations on one piece of the world to make inferences about another piece of the world, without involving an intermediate abstract model about which one reasons. The philosophical significance of that point to philosophy of science is that the method of physical similarity, which provides the basis for inferences based upon scale models, is a qualitatively different way in which fundamental laws can be used in analogical reasoning that is truly informative. Finally, as this method provides a formal basis for case-based reasoning, it may be helpful in formalizing methods used in some of the so-called ``special sciences''.

How are beliefs efficacious? One answer is: via rational intentional action. But there are other ways that beliefs are efficacious. This dissertation examines these other ways, and sketches an answer to the question of how beliefs are efficacious that takes into account how beliefs are involved in the full range of behavioral disciplines, from psychophysiology and cognition to social and economic phenomena. The account of how beliefs are efficacious I propose draws on work on active accounts of perception. I develop an account based on a proposal sketched by the cognitive scientist Ulrich Neisser. Neisser sketched an active account of perception, on which dynamic anticipatory schemata direct an organism's exploration and action, and are in turn revised as a result of exploration and action. This notion of schema has roots in nineteenth century neurophysiology and in Frederick Bartlett's subsequent work on memory. Neisser appealed to it to unite what he thought was right about information-processing accounts of perception with what he thought was right about ecological accounts of perception. The point that we must anticipate in order to perceive has been recognized by philosophers in the form of the "theory-ladenness of observation." I extend the concept of anticipatory schema to include its role in social perception and social interaction; the concept of anticipatory schema provides a more interactive account of the role of expectations in the maintenance and existence of social institutions, and can be used to enrich the account of convention David Lewis provided. I also show that the concept of rational expectations, which explains the neutrality of money in terms of the efficacy of anticipatory expectations, is compatible with the proposed account of how beliefs are efficacious. I discuss how the proposal accounts for the three main modes by which beliefs can be efficacious: via their role in causing intentional action, via their role in causing economic phenomena and the existence and maintenance of social institutions, and via their role in causing unintentional physiological responses, including anticipatory physiological responses that can enable perception, cause involuntary actions and give rise to the placebo effect.

Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an obvious fact in the bigger scheme of things regarding human intelligence: grownups were once children; this leads him to imagine what a machine analogue of childhood might be. In this paper, I’ll discuss Turing’s child-machine, what he said about different ways of educating it, and what impact the “bringing up” of a child-machine has on its ability to behave in ways that might be taken for intelligent. I’ll also discuss how some of the various games he suggested humans might play with machines are related to this approach.

This paper investigates the following proposal about machine intelligence: that behaviour in which a habitual response that would have been inappropriate in a certain unfamiliar situation is overridden and replaced by a more appropriate response be considered evidence of intelligence. The proposal was made in an earlier paper (Sterrett 2000) and arose from an analysis of a neglected test for intelligence hinted at in Turing's legendary 'Computing Machinery and Intelligence'; it was also argued there that it was a more principled test of machine intelligence than straightforward comparisons with human behaviour. The present paper first summarizes the previous claim then looks at writings about intelligence, or the lack of it, in animals and machines by various writers (Descartes, Hume, Darwin and James). It is then shown that, despite their considerable differences regarding fundamental things such as what kinds of creatures are intelligent and the relationship between reason, instinct and behaviour, all of these writers would regard behaviour that meets the proposed criterion as evidence of intelligence. Finally, some recent work in employing logic and reinforcement learning in conjunction with 'behaviour-based' principles in the design of intelligent agents is described; the significance for the prospect of machine intelligence according to the proposed criterion is discussed