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  1. Stan Franklin, Cognitive Agents Architecture and Theory (CAAT).
    Cognition, writ broadly to include motivation and emotion, is best conceived of as control structure for autonomous agents . Autonomous agents are situated in a environment. They both sense and act on that environment, over time, so as to effect subsequent sensing. Examples of such agents include humans, animals, some mobile robots, some artificial life creatures (who "live" in a simulated environment on a computer) and some software agents (who "live" in a file system, a database, or on a network). (...)
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  2. Usef Faghihi & Stan Franklin (2012). The LIDA Model as a Foundational Architecture for AGI. In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. 103--121.
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  3. Uma Ramamurthy, Stan Franklin & Pulin Agrawal (2012). Self-System in a Model of Cognition. International Journal of Machine Consciousness 4 (02):325-333.
  4. Stan Franklin (2011). Global Workspace Theory, Shanahan, and Lida. International Journal of Machine Consciousness 3 (02):327-337.
  5. Wendell Wallach, Colin Allen & Stan Franklin (2011). Consciousness and Ethics: Artificially Conscious Moral Agents. International Journal of Machine Consciousness 3 (01):177-192.
  6. Wendell Wallach, Stan Franklin & Colin Allen (2010). A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents. Topics in Cognitive Science 2 (3):454-485.
    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational (...)
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  7. Bernard J. Baars & Stan Franklin (2009). Consciousness is Computational: The Lida Model of Global Workspace Theory. International Journal of Machine Consciousness 1 (01):23-32.
  8. Stan Franklin, Sidney D'Mello, Bernard J. Baars & Uma Ramamurthy (2009). Evolutionary Pressures for Perceptual Stability and Self as Guides to Machine Consciousness. International Journal of Machine Consciousness 1 (01):99-110.
  9. Uma Ramamurthy & Stan Franklin (2009). Resilient Architectures to Facilitate Both Functional Consciousness and Phenomenal Consciousness in Machines. International Journal of Machine Consciousness 1 (02):243-253.
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  10. Bernard J. Baars, Uma Ramamurthy & Stan Franklin (2007). How Deliberate, Spontaneous, and Unwanted Memories Emerge in a Computational Model of Consciousness. In John H. Mace (ed.), Involuntary Memory. New Perspectives in Cognitive Psychology. Blackwell Publishing. 177-207.
  11. Stan Franklin (2007). Walter J. Freeman, How Brains Make Up Their Minds. Minds and Machines 17 (3):353-356.
  12. Ron Sun & Stan Franklin (2007). Computational Models of Consciousness: A Taxonomy and Some Examples. In Philip David Zelazo, Morris Moscovitch & Evan Thompson (eds.), The Cambridge Handbook of Consciousness. Cambridge. 151--174.
  13. Bernard J. Baars & Stan Franklin (2003). How Conscious Experience and Working Memory Interact. Trends in Cognitive Sciences 7 (4):166-172.
  14. Stan Franklin (2003). A Conscious Artifact? Journal of Consciousness Studies 10 (4-5):4-5.
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  15. Stan Franklin (2003). Ida: A Conscious Artifact? Journal of Consciousness Studies 10 (4):47-66.
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  16. Stan Franklin, Conscious Software: A Computational View of Mind.
  17. Aregahegn S. Negatu & Stan Franklin (2002). An Action Selection Mechanism for "Conscious" Software Agents. Cognitive Science Quarterly. Special Issue 2 (3):362-384.
  18. Stan Franklin (2001). Models as Implementations of a Theory, Rather Than Simulations: Dancing to a Different Drummer. Behavioral and Brain Sciences 24 (6):1059-1059.
    Robots, as well as software agents, can be of use in biology as implementations of a theory rather than as simulations of specific real world target systems. Such implementations generate hypotheses rather than representing them. Their behavior is not predicted, but rather observed, and is not expected to duplicate that of a target system. Scientific knowledge is gained through the testing of generated hypotheses.
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  19. Stan Franklin (2001). Sense and Nonsense: Comments on Horgan's Precis of the Undiscovered Mind. [REVIEW] Brain and Mind 2 (2):231-234.
  20. Derek Harter, Arthur C. Graesser & Stan Franklin (2001). Bridging the Gap: Dynamics as a Unified View of Cognition. Behavioral and Brain Sciences 24 (1):45-46.
    Top-down dynamical models of cognitive processes, such as the one presented by Thelen et al., are important pieces in understanding the development of cognitive abilities in humans and biological organisms. Unlike standard symbolic computational approaches to cognition, such dynamical models offer the hope that they can be connected with more bottom-up, neurologically inspired dynamical models to provide a complete view of cognition at all levels. We raise some questions about the details of their simulation and about potential limitations of (...)
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  21. Myles Bogner, Uma Ramamurthy & Stan Franklin (2000). Consciousness and Conceptual Learning in a Socially Situated Agent. In Kerstin Dauthenhahn (ed.), Human Cognition and Social Agent Technology. Amsterdam: John Benjamins Publishing Company. 113--135.
  22. Stan Franklin, Action Selection and Language Generation in "Conscious" Software Agents.
  23. Stan Franklin (1999). Robert G. Burton, Ed., Natural and Artificial Minds, SUNY Series, Scientific Studies in Natural and Artificial Intelligence, Albany: State University of New York Press, 1993, VII + 245 Pp., $21.95 (Paper), ISBN 0-7914-1508-. [REVIEW] Minds and Machines 9 (1):143-156.
  24. Stan Franklin & Art Graesser (1999). A Software Agent Model of Consciousness. Consciousness And Cognition 8 (3):285-301.
    Baars (1988, 1997) has proposed a psychological theory of consciousness, called global workspace theory. The present study describes a software agent implementation of that theory, called ''Conscious'' Mattie (CMattie). CMattie operates in a clerical domain from within a UNIX operating system, sending messages and interpreting messages in natural language that organize seminars at a university. CMattie fleshes out global workspace theory with a detailed computational model that integrates contemporary architectures in cognitive science and artificial intelligence. Baars (1997) lists the psychological (...)
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  25. Stan Franklin (1997). Action Patterns, Conceptualization, and Artificial Intelligence. Behavioral and Brain Sciences 20 (1):23-24.
    This commentary connects some of Glenberg's ideas to similar ideas from artificial intelligence. Second, it briefly discusses hidden assumptions relating to meaning, representations, and projectable properties. Finally, questions about mechanisms, mental imagery, and conceptualization in animals are posed.
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  26. Stan Franklin (1997). Global Workspace Agents. Journal of Consciousness Studies 4 (4):322-324.
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  27. Stan Franklin & Max Garzon (1992). On Stability and Solvability (or, When Does a Neural Network Solve a Problem?). Minds and Machines 2 (1):71-83.
    The importance of the Stability Problem in neurocomputing is discussed, as well as the need for the study of infinite networks. Stability must be the key ingredient in the solution of a problem by a neural network without external intervention. Infinite discrete networks seem to be the proper objects of study for a theory of neural computability which aims at characterizing problems solvable, in principle, by a neural network. Precise definitions of such problems and their solutions are given. Some consequences (...)
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  28. Stan Franklin & Max Garzon (1988). Commentary on R. Cummins' “Radical Connectionism”. Southern Journal of Philosophy 26 (S1):63-65.
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