Results for 'Artificially intelligent systems'

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  1.  37
    Beyond Simon’s Means-Ends Analysis: Natural Creativity and the Unanswered ‘Why’ in the Design of Intelligent Systems for Problem-Solving. [REVIEW]Dongming Xu - 2010 - Minds and Machines 20 (3):327-347.
    Goal-directed problem solving as originally advocated by Herbert Simon’s means-ends analysis model has primarily shaped the course of design research on artificially intelligent systems for problem-solving. We contend that there is a definite disregard of a key phase within the overall design process that in fact logically precedes the actual problem solving phase. While systems designers have traditionally been obsessed with goal-directed problem solving, the basic determinants of the ultimate desired goal state still remain to be (...)
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  2. What Decision Theory Provides the Best Procedure for Identifying the Best Action Available to a Given Artificially Intelligent System?Samuel A. Barnett - 2018 - Dissertation, University of Oxford
    Decision theory has had a long-standing history in the behavioural and social sciences as a tool for constructing good approximations of human behaviour. Yet as artificially intelligent systems (AIs) grow in intellectual capacity and eventually outpace humans, decision theory becomes evermore important as a model of AI behaviour. What sort of decision procedure might an AI employ? In this work, I propose that policy-based causal decision theory (PCDT), which places a primacy on the decision-relevance of predictors and (...)
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  3.  16
    An Emotion Theory Approach to Artificial Emotion Systems for Robots and Intelligent Systems: Survey and Classification.Arvin Agah & Sylvia Tidwell Scheuring - 2014 - Journal of Intelligent Systems 23 (3):325-343.
    To assist in the evaluation process when determining architectures for new robots and intelligent systems equipped with artificial emotions, it is beneficial to understand the systems that have been built previously. Other surveys have classified these systems on the basis of their technological features. In this survey paper, we present a classification system based on a model similar to that used in psychology and philosophy for theories of emotion. This makes possible a connection to thousands of (...)
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  4.  3
    Something Old, Something New, Something Borrowed, Something Blue Part 1: Alan Turing, Hypercomputation, Adam Smith and Next Generation Intelligent Systems.Mark Dougherty - 2012 - Journal of Intelligent Systems 21 (4):325-330.
    . In this article intelligent systems are placed in the context of accelerated Turing machines. Although such machines are not currently a reality, the very real gains in computing power made over previous decades require us to continually reevaluate the potential of intelligent systems. The economic theories of Adam Smith provide us with a useful insight into this question.
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  5.  3
    Digital Nets and Intelligent Systems.K. N. Gumey & M. J. Wright - 1992 - Journal of Intelligent Systems 2 (1-4):1-10.
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  6.  17
    Science with Artificially Intelligent Agents: The Case of Gerrymandered Hypotheses.Ioannis Votsis - unknown
    Barring some civilisation-ending natural or man-made catastrophe, future scientists will likely incorporate fully fledged artificially intelligent agents in their ranks. Their tasks will include the conjecturing, extending and testing of hypotheses. At present human scientists have a number of methods to help them carry out those tasks. These range from the well-articulated, formal and unexceptional rules to the semi-articulated rules-of-thumb and intuitive hunches. If we are to hand over at least some of the aforementioned tasks to artificially (...)
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  7.  8
    Improving the Design of Intelligent Systems: Outstanding Problems and Some Solutions.L. J. Kohout & I. Stabile - 1998 - Journal of Intelligent Systems 8 (3-4):225-238.
  8.  6
    Intelligent Systems as Cooperative Systems.L. G. Terveen - 1993 - Journal of Intelligent Systems 3 (2-4):217-250.
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  9.  1
    Intelligent Systems for Structural Damage Assessment.Eleni Vrochidou, Petros-Fotios Alvanitopoulos, Ioannis Andreadis & Anaxagoras Elenas - forthcoming - Journal of Intelligent Systems.
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  10.  50
    Probabilistic Reasoning in Intelligent Systems.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  11.  59
    Intelligent Design From the Viewpoint of Complex Systems Theory.Chunyu Dong - 2010 - Frontiers of Philosophy in China 5 (3):461-470.
    Based on an analysis of the origins and characteristics of Intelligent Design, this essay discusses the related issues of probability and irreducible complexity. From the viewpoint of complex systems theory, I suggest that Intelligent Design is not, as certain advocates claim, the only reasonable approach for dealing with the current difficulties of evolutionary biology.
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  12. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1995 - Synthese 104 (1):161-176.
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  13.  13
    Intelligent Diagnosis Systems.K. Balakrishnan & V. Honavar - 1998 - Journal of Intelligent Systems 8 (3-4):239-290.
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  14.  6
    Seeking to Deploy Intelligent Transportation Systems:The Surrey County Council Study.I. Nnatuanya & J. Alexander - 2006 - Journal of Intelligent Systems 15 (1-4):39-54.
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  15.  6
    Remedial Tutoring with Intelligent Tutoring Systems - The Case of INTUITION.J. Siemer & M. C. Angelides - 1996 - Journal of Intelligent Systems 6 (3-4):279-308.
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  16. Circuit Sharing and the Implementation of Intelligent Systems.Michael Anderson - manuscript
    One of the most foundational and continually contested questions in the cognitive sciences is the degree to which the functional organization of the brain can be understood as modular. In its classic formulation, a module was defined as a cognitive sub-system with nine specific properties; the classic module is, among other things, domain specific, encapsulated, and implemented in dedicated neural substrates. Most of the examinations—and especially the criticisms—of the modularity thesis have focused on these properties individually, for instance by finding (...)
     
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  17.  74
    Architectures of Intelligent Systems.David Kirsh - 1992 - Exploring Brain Functions:293-321.
    Theories of intelligence can be of use to neuroscientists if they: 1. Provide illuminating suggestions about the functional architecture of neural systems; 2. Suggest specific models of processing that neural circuits might implement. The objective of our session was to stand back and consider the prospects for this interdisciplinary exchange.
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  18.  23
    Emergent Functionality Among Intelligent Systems: Cooperation Within and Without Minds. [REVIEW]Cristiano Castelfranchi & Rosaria Conte - 1992 - AI and Society 6 (1):78-87.
    In this paper, the current AI view that emergent functionalities apply only to the study of subcognitive agents is questioned; a hypercognitive view of autonomous agents as proposed in some AI subareas is also rejected. As an alternative view, a unified theory of social interaction is proposed which allows for the consideration of both cognitive and extracognitive social relations. A notion of functional effect is proposed, and the application of a formal model of cooperation is illustrated. Functional cooperation shows the (...)
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  19.  14
    Potential of Full Human–Machine Symbiosis Through Truly Intelligent Cognitive Systems.Sun Ron - forthcoming - AI and Society:1-12.
    It is highly likely that, to achieve full human–machine symbiosis, truly intelligent cognitive systems—human-like —may have to be developed first. Such systems should not only be capable of performing human-like thinking, reasoning, and problem solving, but also be capable of displaying human-like motivation, emotion, and personality. In this opinion article, I will argue that such systems are indeed possible and needed to achieve true and full symbiosis with humans. A computational cognitive architecture is used in this (...)
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  20.  20
    Patiency is Not a Virtue: The Design of Intelligent Systems and Systems of Ethics.Joanna J. Bryson - 2018 - Ethics and Information Technology 20 (1):15-26.
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  21.  65
    Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1991 - Journal of Philosophy 88 (8):434-437.
  22. Semiotics and Intelligent Systems Development.R. Gudwin & J. Queiroz (eds.) - 2007 - Idea Group.
  23.  3
    A Norm Compliance Approach for Open and Goal-Directed Intelligent Systems.Patrizia Ribino & Carmelo Lodato - 2019 - Complexity 2019:1-20.
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  24. Must Intelligent Systems Be Scruffy.Aaron Sloman - 1990 - In J. E. Tiles, G. T. McKee & G. C. Dean (eds.), Evolving Knowledge in Natural Science and Artificial Intelligence. Pitman. pp. 17.
     
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  25.  23
    Claims and Challenges in Evaluating Human-Level Intelligent Systems.John E. Laird, Robert Wray, Robert Marinier & Pat Langley - 2009 - In B. Goertzel, P. Hitzler & M. Hutter (eds.), Proceedings of the Second Conference on Artificial General Intelligence. Atlantis Press.
  26.  23
    Appearance in This List Neither Guarantees nor Precludes a Future Review of the Book. Albus, James S., and Alexander M. Meystel, Engineering of Mind: An Introduction to the Science of Intelligent Systems, New York: John Wiley & Sons, 2001, Pp. Xv+ 411,£ 57.50 Aristotle, Translated by Glen Coughlin, Physics, Or Natural Hearing, South Bend, Indi. [REVIEW]Thomas E. Brown, Maria Cerezo, Earl Conee, Theodore Sider, John Cottingham & Sandra M. Dingli - 2006 - Mind 115:457.
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  27.  17
    Explanations and Context in Ambient Intelligent Systems.Anders Kofod-Petersen & Jörg Cassens - 2007 - In D. C. Richardson B. Kokinov (ed.), Modeling and Using Context. Springer. pp. 303--316.
  28.  15
    Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl. [REVIEW]Henry E. Kyburg - 1991 - Journal of Philosophy 88 (8):434-437.
  29.  22
    Intelligent Systems at Home in 2000.Jay Liebowitz, Janet Liebowitz, Jason Liebowitz & Kenny Liebowitz - 1994 - AI and Society 8 (4):359-363.
  30.  8
    Pearl Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Series in Representation and Reasoning. Morgan Kaufmann, San Mateo 1988, Xix + 552 Pp. [REVIEW]Eric Neufeld - 1993 - Journal of Symbolic Logic 58 (2):721-721.
  31.  10
    Artificially Intelligent Mental Models.Michael Lebowitz - 1984 - Behavioral and Brain Sciences 7 (4):633.
  32.  12
    Review: Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [REVIEW]Eric Neufeld - 1993 - Journal of Symbolic Logic 58 (2):721-721.
  33.  4
    Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl. [REVIEW]Henry E. Kyburg Jr - 1991 - Journal of Philosophy 88 (8):434-437.
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  34.  8
    Appearance in This List Neither Guarantees nor Precludes a Future Review of the Book. Abdoullaev, Azamat, Artificial Superintelligence, Moscow, Russia, EIS Encyclopedic Intelligent Systems, Ltd., 1999, Pp. 184. Adams, Robert Merrihew, Finite and Infinite Goods, Oxford, UK, Oxford University Press, 1999, Pp. 410,£ 35.00. [REVIEW]Theodor Adorno & Walter Benjamin - 1999 - Mind 108:432.
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  35.  2
    Cramming Mind Into Computer: Knowledge and Learning for Intelligent Systems.M. Gams - 1997 - In Matjaz Gams (ed.), Mind Versus Computer: Were Dreyfus and Winograd Right? Amsterdam: Ios Press. pp. 43--52.
  36. Frontiers of Life, Vol III: The Intelligent Systems, Part One: The Brain of Homo Sapiens.E. Bizzi, P. Calissano & V. Volterra (eds.) - 2001 - Academic Press.
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  37. Applications of Intelligent Systems-Intelligent Signal Processing, Control and Robotics-Designing a Self-Adaptive Union-Based Rule-Antecedent Fuzzy Controller Based on Two Step Optimization.Chang-Wook Han & Jung-Il Park - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4251--850.
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  38. Generic Intelligent Systems-Evolutionary Computation-Self-Adaptive Classifier Fusion for Expression-Insensitive Face Recognition.Eun Sung Jung, Soon Woong Lee & Phill Kyu Rhee - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 78-85.
  39. Generic Intelligent Systems-Agent Systems-Automatic Classification for Grouping Designs in Fashion Design Recommendation Agent System.Kyung-Yong Jung - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4251--310.
  40. Applications of Intelligent Systems-Intelligent Signal Processing, Control and Robotics-Intelligent Multi-Modal Recognition Interface Using Voice-XML and Embedded KSSL Recognizer.Jung-Hyun Kim & Kwang-Seok Hong - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4251--798.
  41. Applications of Intelligent Systems-Web Intelligence, Multimedia, E-Learning and Teaching-Modeling Collaborators From Learner's Viewpoint Reflecting Common Collaborative Learning Experience.Akira Komedani, Tomoko Kojiri & Toyohide Watanabe - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4251--771.
     
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  42. Representing Knowledge in Intelligent Systems.John McDermott - 1979 - In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press.
     
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  43. Generic Intelligent Systems-Artificial Neural Networks and Connectionists Systems-An Improved OIF Elman Neural Network and Its Applications to Stock Market.Limin Wang, Yanchun Liang, Xiaohu Shi, Ming Li & Xuming Han - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 21-28.
     
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  44. Using Fuzzy Logic: Towards Intelligent Systems.Jun Yan - 1994 - Prentice-Hall.
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  45.  5
    “Super-Intelligent” Machine: Technological Exuberance or the Road to Subjection.Peter Brödner - 2018 - AI and Society 33 (3):335-346.
    Looking back on the development of computer technology, particularly in the context of manufacturing, we can distinguish three big waves of technological exuberance with a wave length of roughly 30 years: In the first wave, during the 1950s, mainframe computers at that time were conceptualized as “electronic brains” and envisaged as central control unit of an “automatic factory”. Thirty years later, during the 1980s, knowledge-based systems in computer-integrated manufacturing were adored as the computational core of the “unmanned factory”. Both (...)
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  46. Intelligent Artificial Systems.Salvatore Gaglio - 2007 - In Antonio Chella & Riccardo Manzotti (eds.), Artificial Consciousness. Imprint Academic. pp. 97-115.
     
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  47.  17
    Ontologies and Reasoning Techniques for (Legal) Intelligent Information Retrieval Systems.Gian Piero Zarri - 2007 - Artificial Intelligence and Law 15 (3):251-279.
    An application of Narrative Knowledge Representation Language (NKRL) techniques on (declassified) ‘terrorism in Southern Philippines’ documents has been carried out in the context of the IST Parmenides project. This paper describes some aspects of this work: it is our belief, in fact, that the Knowledge Representation techniques and the Intelligent Information Retrieval tools used in this experiment can be of some interest also in an ‘Ontological Modelling of Legal Events and Legal Reasoning’ context.
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  48. Intelligent Simulation for Manufacturing Systems.Nirmal K. Baid N. N. Nagarur - forthcoming - Proceedings: Ai, Simulation and Planning in High Autonomy Systems.
  49. Intelligent Knowledge-Based Systems—AI in the UK In R. Kurzweil.B. W. Oakley - 1990 - In R. Kurzweil (ed.), The Age of Intelligent Machines. MIT Press. pp. 346--349.
  50.  36
    What Learning Systems Do Intelligent Agents Need? Complementary Learning Systems Theory Updated.Dharshan Kumaran, Demis Hassabis & James L. McClelland - 2016 - Trends in Cognitive Sciences 20 (7):512-534.
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