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References ACT-R 6.0 Tutorial. 2012. Unpublished manuscript. Retrieved from Albus, J. S., and Barbera, A. J. 2005. RCS: A cognitive architecture for intelligent multi-agent systems. Annual Reviews in Control, 29(1):87-99. Anderson, J. 2007. How can the human mind occur in the physical universe? : Oxford University Press. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., and Qin, Y. 2004. An integrated theory of the mind. Psychological Review, 111

.; Ducharme, R.; Vincent, P.; and Janvin, C. 2003. A neural probabilistic language model. The Journal of Machine Learning Research. 3: 1137-1155. Best, B.; Lebiere, C.; and Scarpinatto, C. 2002. A model of synthetic opponents in MOUT training simulations using the ACT-R cognitive architecture. In Proceedings of the Eleventh Conference on Computer Generated Forces and Behavior Representation . Bonasso, R. P.; Firby, R. J.; Gat, E.; Kortenkamp, D.; Miller, D. P.; and Slack, M. G. 1997. Experiences with an Architecture for Intelligent Reactive Agent. Journal of

Society. Edwards, W. 1962. Dynamic decision theory and probabilistic information processing. Human Factors 4:59-73. Fitts, P. M. 1954. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47:381-391. Fleetwood, M. D., and Byrne, M. D. 2006. Modeling the visual search of displays: A revised ACT-R/PM model of icon search based on eye tracking data. Human Computer Interaction 21:153-197. Gluck, K. A., and Pew, R. W., eds. 2005. Modeling Human Behaviour with Integrated Cognitive Architectures

Journal of Artificial General Intelligence 3(2) 31-63, 2012 DOI: 10.2478/v10229-011-0016-2 Submitted 2012-06-07 Accepted 2012-10-07 © 2012 Duch et al; Distributed under Creative Commons Attribution License Cognitive Architectures and Autonomy: Commentary and Response Editor: Włodzisław Duch, Ah-Hwee Tan, Stan Franklin Autonomy for AGI Cristiano Castelfranchi ISTC-CNR Italy CRISTIANO.CASTELFRANCHI@ISTC.CNR.IT This paper provides a very useful and promising analysis and comparison of current architectures of autonomous intelligent systems

the System Dynamics Society (pp. 79). Boston, MA: System Dynamics Society. Foyle, D. & Hooey, B. (2008). Human Performance Modeling in Aviation. Mahwah, NJ: Erlbaum. Gluck, K, & Pew, R. (2005). Modeling Human Behavior with Integrated Cognitive Architectures. Mahwah, NJ: Erlbaum. Gonzalez, C., & Dutt, V. (2007). Learning to control a dynamic task: A system dynamics cognitive model of the slope effect. In Lewis, Polk, & Laird (Eds.), 8th International Conference on Cognitive Modeling (pp. 61-66). Ann Arbor, MI. Kaminka, G. A., & Burghart, C. R. (2007

. Garlan, D. & J. Ockerbloom 1995. Architectural Mismatch or Why it's Hard to Build Systems out of Existing Parts. Proceedings of the Seventh International Conference on Software Engineering, Seattle WA, April Hall, J. S. 2008. VARIAC: An Autogenous Cognitive Architecture. In Proceedings of First Conference of Artificial General Intelligence , 176-187. Memphis, Tenn.: ISO Press. Huang, H., Messina, E., Albus, J. 2003. Towards a Generic Model for Autonomy Levels for Unmanned Systems (ALFUS). In Proceedings of the 2003 PerMIS Workshop , Gaithersburg, MD: National Schmidhuber, J. (2004). Optimal Ordered Problem Solver. Machine Learning, 54, 211–254. Kluwer Academic Publishers. Skaba, W. (2011). Heuristic Search in Program Space for the AGINAO Cognitive Architecture. AGI 2011 Self-Programming Workshop. Available electronically at Skaba, W. (2012). Binary Space Partitioning as Intrinsic Reward. Proceedings of Artificial General Intelligence 2012. LNAI 7716, Springer-Verlag. Sutton R. S. and Barto A. G. (1998

Method]. Warsaw: Academica. Stemplewska-Żakowicz, K., Suszek, H., Kobylińska, D., & Szymczyk, B. (2010). Explorations in the discursive mind. Th eoretical model. International Journal for Dialogical Science, 4 (1), 81-94. Stemplewska-Żakowicz, K., Zalewski, B., Suszek, H., Fira-Krempa, E., & Kobylińska, D. (2009). Relacyjnie strukturalizowany umysł. Model Teoretyczny [Relationally structured mind. A theoretical model]. Przegląd Psychologiczny, 52 (1), 69-85. Stemplewska-Żakowicz, K., Zalewski, B., Suszek, H., & Kobylińska, D. (2012). Cognitive architecture of the


This paper describes a design-based implementation research (DBIR) approach to the development and trialling of a new generation massive open online course (ngMOOC) situated in an instructional setting of undergraduate mathematics at a regional Australian university. This process is underscored by two important innovations: (a) a basis in a well-established human cognitive architecture in terms of cognitive load theory; and (b) point-of-contact feedback based in a well-tested online system dedicated to enhancing the learning process. Analysis of preliminary trials suggests that the DBIR approach to the ngMOOC construction and development supports theoretical standpoints that argue for an understanding of how design for optimal learning can utilise conditions, such as differing online or blended educational contexts, in order to be effective and scalable. The ngMOOC development described in this paper marks the adoption of a cognitive architecture in conjunction with feedback systems, offering the groundwork for use of adaptive systems that cater for learner expertise. This approach seems especially useful in constructing and developing online learning that is self-paced and curriculum-based.

., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396-408. Ball, J. T., Myers, C. W., Heiberg, A., Cooke, N. J., Matessa, M., Freiman, M., et al. (under review). The Synthetic Teammate Project. Computational and Mathematical Organization Theory . Byrne, M. D. (2003). Cognitive Architecture. In J. Jacko & A. Sears (Eds.), The human-computer interaction handbook: Fundamentals, evolving technologies and emerging applications (pp. 97-117). Mahwah, NJ: Lawrence Erlbaum. Dimperio, E., Gunzelmann, G., & Harris, J. (2008). An initial