Computing as a discipline

Comer, Diana E.; Gries, David; Mulder, Michael C.; Tucker, Allen B.; Turner, A. Joe; Young, Paul R.; Denning, Peter J.

doi:10.1145/63238.63239

Cited by 528 publications

(124 citation statements)

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“…Viz., the speed of memory storage and retrieval wasn't adapted at all to that of the computation. 11 As these two bottlenecks got steadily and partially resolved, it became possible to store internally into the machine more and more subroutines and the process of the so-called "algorithmization" of knowledge could be started. Also within mathematics one can observe a steady "algorithmization" of mathematical knowledge and skills.…”

Section: Internalizationmentioning

confidence: 99%

Some Reflections on Mathematics and Its Relation to Computer Science

Mol

2015

Emergence, Complexity and Computation

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Section: Internalizationmentioning

confidence: 99%

Some Reflections on Mathematics and Its Relation to Computer Science

Mol

2015

Emergence, Complexity and Computation

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“…It is proper to take an analogous approach to informing as to "Computing as a Discipline," by Denning et al (1989). The fundamental question underlying all of computing is "What can be (efficiently) automated?"…”

Section: Paradigmsmentioning

confidence: 99%

“…The main contributions of this paper are the initial articulations of • the concept of informing • a short description and definition of informing • the paradigms of informing • the initial division of informing into major sub-areas • the schema of informing with major distinctions of its basic terms • the universal conditions of effective informing • the identification of major clusters of informing problems • the identification of major problems with data and information in informing • the place of informing within the major taxonomies of academic disciplines • the discussion of informing from the perspectives of frameworks used in computing (Denning et al, 1989) and management information systems (Ives et al, 1980) • the identification of what informing lacks to become a separate academic discipline For focused reading, key terms in paragraphs are in bold font, emphasis is in italics, highest emphasis is underlined, and terms followed by a definition are in bold italics. The author is particularly indebted to the final report of the Task Force on the Core of Computer Science, authored by Denning et al (1989), with all its fine examples, which are used and adopted for informing.…”

Section: Introductionmentioning

confidence: 99%

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Informing as a Discipline: An Initial Proposal

Gackowski

2007

Proceedings of the 2007 InSITE Conference

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This paper presents an inquiry and a proposal of a structured way of thinking about informing as a separate field of scientific inquiry and practical endeavors and possibly a separate academic discipline. Informing is the science and art of practical endeavors to increase its effectiveness and efficiency; it is an interdisciplinary applied science with some components of universal validity, which according to Biglan (1979) mark pure science. It is a field with a defined model, a fundamental research question, a point of reference, a framework, observation points, and a way of measuring results. It has fundamental concepts, basic distinctions, a universal taxonomy of informing factors and their qualities, universal quality requirements, and priorities of research. It lacks, however, a clear division into sub-areas. To attain the status of a separate academic discipline, it needs further elaboration of sub-areas and a curriculum model that specifies competencies, an introductory course, prerequisite courses, and laboratories. This is an initial position paper written mainly from the operations management and decision sciences viewpoint with the purpose of eliciting challenge, critique, discussion, and suggestions for expansions in order to gradually develop a mutual consensus among those dealing with informing.

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“…This arguably places the design of such systems outside the paradigm for Computer Science proposed in Denning al. [41]. For instance, it raises issues such as shared meaning, and the management of ambiguity, inconsistencies and conflict in system specifications.…”

Section: Critical Perspectives On Logicismmentioning

confidence: 99%

Empirical Modelling and the Foundations of Artificial Intelligence

Beynon

1999

Computation for Metaphors, Analogy, and Agents

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Abstract. This paper proposes Empirical Modelling (EM) as a possible foundation for AI research outside the logicist framework. EM offers principles for constructing physical models, typically computer-based, by construing experience in terms of three fundamental concepts: observables, dependency and agency. EM is discussed in the context of critiques of logicism drawn from a variety of sources, with particular reference to the five foundational issues raised by Kirsh in his paper Foundations of AI: the Big Issues (AI, 47:3-30, 1991), William James's Essays on Radical Empiricism (Bison Books, 1996), and the controversy surrounding formal definitions for primitive concepts such as metaphor and agent that are recognised as fundamental for AI. EM principles are motivated and illustrated with reference to a historic railway accident that occurred at the Clayton Tunnel in 1861. The principal thesis of the paper is that logicist and non-logicist approaches to AI presume radically different ontologies. Specifically, EM points to a fundamental framework for AI in which experimentally guided construction of physical artefacts is the primary mode of knowledge representation. In this context, propositional knowledge is associated with phenomena that are perceived as circumscribed and reliable from an objective 'third-person' perspective. The essential need to incorporate subjective 'first-person' elements in an account of AI, and the role that commitment plays in attaching an objective meaning to phenomena, are seen to preclude a hybrid approach to AI in the conventional sense.

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Computing as a discipline

Abstract: The final report of the Task Force on the Core of Computer Science presents a new intellectual framework for the discipline of computing and a new basis for computing curricula. This report has been endorsed and approved for release by the ACM Education Board.

Cited by 528 publications

References 1 publication

Some Reflections on Mathematics and Its Relation to Computer Science

Some Reflections on Mathematics and Its Relation to Computer Science

Informing as a Discipline: An Initial Proposal

Empirical Modelling and the Foundations of Artificial Intelligence

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