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Timothy R. Colburn [7]Timothy Colburn [4]
  1. Timothy Colburn & Gary Shute (2011). Decoupling as a Fundamental Value of Computer Science. Minds and Machines 21 (2):241-259.
    Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm shifts in both natural and computer science (...)
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  2. Timothy Colburn & Gary Shute (2010). Abstraction, Law, and Freedom in Computer Science. Metaphilosophy 41 (3):345-364.
    Abstract: Laws of computer science are prescriptive in nature but can have descriptive analogs in the physical sciences. Here, we describe a law of conservation of information in network programming, and various laws of computational motion (invariants) for programming in general, along with their pedagogical utility. Invariants specify constraints on objects in abstract computational worlds, so we describe language and data abstraction employed by software developers and compare them to Floridi's concept of levels of abstraction. We also consider Floridi's structural (...)
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  3. Timothy Colburn & Gary Shute (2007). Abstraction in Computer Science. Minds and Machines 17 (2):169-184.
    We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that abstraction through information hiding is a (...)
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  4. Timothy Colburn (2004). Methodology of Computer Science. In L. Floridi (ed.), The Blackwell Guide to the Philosophy of Computing and Information. Blackwell. 318--326.
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  5. Robert Almeder, Lynne Rudder Baker, José Luis Bermúdez, James Robert Brown, Jeremy Butterfield, Constantine Pagonis, Steven M. Cahn, John D. Caputo, J. Michael & Timothy R. Colburn (2000). Books for Review and for Listing Here Should Be Addressed to Emily Zakin, Review Editor, Teaching Philosophy, Department of Philosophy, Miami University, Oxford, OH 45056. Teaching Philosophy 23 (2):227.
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  6. Timothy R. Colburn (1999). Software, Abstraction, and Ontology. The Monist 82 (1):3-19.
    This paper analyzes both philosophical and practical assumptions underlying claims for the dual nature of software, including software as a machine made of text, and software as a concrete abstraction. A related view of computer science as a branch of pure mathematics is analyzed through a comparative examination of the nature of abstraction in mathematics and computer science. The relationship between the concrete and the abstract in computer programs is then described by exploring a taxonomy of approaches borrowed from philosophy (...)
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  7. Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.
    The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development of general programming (...)
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  8. Justin Leiber, Robert M. French, John A. Barnden, Syed S. Ali, Richard Wyatt, Timothy R. Colburn, Brian Harvey, Norman R. Gall, Susan G. Josephson, Francesco Orilia & Achille C. Varzi (1996). Book Reviews. [REVIEW] Minds and Machines 6 (1):89-129.
  9. Timothy R. Colburn (1995). Heuristics, Justification, and Defeasible Reasoning. Minds and Machines 5 (4):467-487.
    Heuristics can be regarded as justifying the actions and beliefs of problem-solving agents. I use an analysis of heuristics to argue that a symbiotic relationship exists between traditional epistemology and contemporary artificial intelligence. On one hand, the study of models of problem-solving agents usingquantitative heuristics, for example computer programs, can reveal insight into the understanding of human patterns of epistemic justification by evaluating these models'' performance against human problem-solving. On the other hand,qualitative heuristics embody the justifying ability of defeasible rules, (...)
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  10. Timothy R. Colburn (1991). Defeasible Reasoning and Logic Programming. Minds and Machines 1 (4):417-436.
    The general conditions of epistemic defeat are naturally represented through the interplay of two distinct kinds of entailment, deductive and defeasible. Many of the current approaches to modeling defeasible reasoning seek to define defeasible entailment via model-theoretic notions like truth and satisfiability, which, I argue, fails to capture this fundamental distinction between truthpreserving and justification-preserving entailments. I present an alternative account of defeasible entailment and show how logic programming offers a paradigm in which the distinction can be captured, allowing (...)
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  11. Timothy R. Colburn (1991). Program Verification, Defeasible Reasoning, and Two Views of Computer Science. Minds and Machines 1 (1):97-116.
    In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and formal verification do (...)
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