• Bernard J. Baars. Some Essential Differences between Consciousness and Attention, Perception, and Working Memory. Consciousness and Cognition, 6(2-3):363–371, 1997. (doi:10.1006/ccog.1997.0307)
• Stan Franklin, Arpad Kelemen, and Lee Mccauley. IDA: A Cognitive Agent Architecture. In Proceedings of the IEEE International Conference Systems, Man, and Cybernetics, volume 3, 1998.
• S Franklin and A Graesser. A software agent model of consciousness. Consciousness and cognition, 8(3):285–301, 1999. (doi:10.1006/ccog.1999.0391)
• Myles Bogner, Uma Ramamurthy, and Stan Franklin. Consciousness" and Conceptual Learning in a Socially Situated Agent. Human Cognition and Social Agent Technology, 19:113–135, 2000.
• S. Franklin. Building life-like'conscious' software agents. AI Communications, 13(3):183–193, 2000.
• S. Franklin. Modeling Consciousness and Cognition in Software Agents. In Proceedings of the Third International Conference on Cognitive Modeling, pages 27–58, 2000.
• Stan Franklin. Deliberation and voluntary action in ‘conscious' software agents. Neural Network World, 10:505–521, 2000.
• Stan Franklin. Learning in “Conscious” Software Agents. In Workshop on Development and Learning, 2000.
• Lee McCauley, Stan Franklin, and Myles Bogner. An Emotion-Based “Conscious” Software Agent Architecture. In Affective Interactions, pages 107–120. 2000. (doi:10.1007/10720296_8)
• S. Franklin. Automating human information agents. In Z. Chen and L. C. Jain, editors, Practical Applications of Intelligent Agents, pages 27–58. Springer-Verlag, Berlin, 2001.
• Stan Franklin. An Agent Architecture Potentially Capable of Robust Autonomy. AAAI Spring Symposium on Robust Autonomy, 2001.
• Stan Franklin and Art Graesser. Modeling Cognition with Software Agents. In Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pages 301–306, 2001.
• R. Kondadadi and Stan Franklin. Deliberative Decision Making in “Conscious” Software Agents. In Proceedings Of Sixth International Symposium on Artificial Life and Robotics, 2001.
• Uma Ramamurthy, Aregahegn Negatu, and Stan Franklin. Learning Mechanisms for Intelligent Systems. In Proceedings of SSGRR-2001: International Conference on Advances in Infrastructure for e-Business, e-Education and e-Science on the Internet, 2001.
• Lee McCauley and Stan Franklin. A large-scale multi-agent system for navy personnel distribution. Connection Science, 14(4):371–385, 2002. (doi:10.1080/0954009021000068934)
• Aregahegn Negatu and Stan Franklin. An action selection mechanism for'conscious' software agents. Cognitive Science Quarterly, 2:363–386, 2002.
• Ashraf Anwar and Stan Franklin. Sparse distributed memory for 'conscious' software agents. Cognitive Systems Research, 4:339–354, 2003. (doi:10.1016/S1389-0417(03)00015-9)
• Bernard J. Baars and Stan Franklin. How conscious experience and working memory interact. Trends in Cognitive Sciences, 7(4):166–172, 2003. (doi:10.1016/S1364-6613(03)00056-1)
• Stan Franklin. A computer-based model of Crick and Koch's Framework for Consciousness. Science & Consciousness Review, pages 1–8, 2003.
• Stan Franklin. An Autonomous Software Agent for Navy Personnel Work: A Case Study. Human Interaction with Autonomous Systems in Complex Environments: Papers from 2003 AAAI Spring Symposium, pages 60–65, 2003.
• Stan Franklin. IDA, a Conscious Artifact?. Journal of Consciousness Studies, 10(4-5):47–66, 2003.
• Arpad Kelemen, Yulan Liang, Robert Kozma, and Stan Franklin. Optimizing intelligent agent's constraint satisfaction with neural networks. In Recent advances in intelligent paradigms and applications, pages 255–272. Physica Verlag, 2003.
• Stan Franklin and Dan Jones. A Triage Information Agent (TIA) based on the IDA Technology. In AAAI Fall Symposium on Dialogue Systems for Health Communication, 2004.
• Stan Franklin and Lee McCauley. Feelings and emotions as motivators and learning facilitators. AAAI Spring Symposium - Technical Report, pages 48–51, 2004. (doi:10.1.1.2.5301)
• Uma Ramamurthy, Sidney K. D'Mello, and Stan Franklin. Modified sparse distributed memory as transient episodic memory for cognitive software agents. In Conference Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, volume 6, pages 5858–5863, 2004. (doi:10.1109/ICSMC.2004.1401130)
• Bernard J. Baars. Global workspace theory of consciousness: Toward a cognitive neuroscience of human experience. Progress in Brain Research, 150:45–53, 2005. (doi:10.1016/S0079-6123(05)50004-9)
• Stan Franklin. A “ Conciousness"-Based Architecture for a Functioning Mind. In Visions of Mind, pages 181–207. 2005.
• Stan Franklin. A “Consciousness” Based Architecture for a Functioning Mind. In Visions of Mind, pages 149–175. 2005.
• Stan Franklin. Perceptual Memory and Learning: Recognizing, Categorizing, and Relating. In Proceedings of the Symposium on Developmental Robotics: American Association for Artificial Intelligence (AAAI), 2005.
• Stan Franklin. Perceptual Memory and Learning: Recognizing, Categorizing, and Relating. In Proceedings of the Symposium on Developmental Robotics: American Association for Artificial Intelligence (AAAI), 2005.
• Stan Franklin, Bernard J. Baars, Uma Ramamurthy, and Matthew Ventura. The role of consciousness in memory. Brains, Minds and Media, 1(1), 2005.
• Arpad Kelemen and Yulan Liang. Optimizing Decision Making with Neural Networks in Software Agents. In Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization, pages 256–261, 2005.
• Arpad Kelemen, Stan Franklin, and Yulan Liang. Constraint Satisfaction in “Conscious” Software Agents—A Practical Application. Applied Artificial Intelligence, 19(5):491–514, 2005. (doi:10.1080/08839510590917870)
• Anil K. Seth and Bernard J. Baars. Neural Darwinism and consciousness. Consciousness and Cognition, 14(1):140–168, 2005. (doi:10.1016/j.concog.2004.08.008)
• S. K. D'Mello, Stan Franklin, Uma Ramamurthy, and Bernard J. Baars. A Cognitive Science Based Machine Learning Architecture. AAAI Spring Symposium: Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems, 2006.
• Sidney K. D'Mello, Uma Ramamurthy, Aregahegn Negatu, and Stan Franklin. A Procedural Learning Mechanism for Novel Skill Acquisition. In Adaptation in Artificial and Biological Systems, AISB'06, pages 184–185, 2006.
• Stan Franklin and F. G. Patterson. The LIDA architecture: Adding new modes of learning to an intelligent, autonomous, software agent. In Integrated Design and Process Technology, 2006.
• Stan Franklin and Uma Ramamurthy. Motivations, Values and Emotions: Three sides of the same coin. In Proceedings of the Sixth International Workshop on Epigenetic Robotics, pages 41–48, 2006.
• Uma Ramamurthy, Bernard J. Baars, Sidney K. D'Mello, and Stan Franklin. LIDA: A Working Model of Cognition. In Proceedings of the 7th International Conference on Cognitive Modeling, pages 244–249, 2006.
• Uma Ramamurthy, Sidney D'Mello, and Stan Franklin. Realizing Forgetting in a Modified Sparse Distributed Memory System. In CogSci 2006, pages 1992–1997, 2006.
• Bernard J. Baars and Stan Franklin. An architectural model of conscious and unconscious brain functions: Global Workspace Theory and IDA. Neural Networks, 20(9):955–961, 2007. (doi:10.1016/j.neunet.2007.09.013)
• Bernard J. Baars, Uma Ramamurthy, and Stan Franklin. How deliberate, spontaneous and unwanted memories emerge in a computational model of consciousness. Involuntary memory, 2007.
• Sidney K. D'Mello and Stan Franklin. Exploring the Complex Interplay between AI and Consciousness. AAAI Fall Symposium on AI and Consciousness: Theoretical Foundations and Current Approaches, pages 49–54, 2007.
• Stan Franklin. A Foundational Architecture for Artificial General Intelligence. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, pages 36–54, 2007.
• Stan Franklin, Uma Ramamurthy, Sidney K. D´Mello, Lee McCauley, Aregahegn Negatu, Rodrigo Silva L., and Vivek Datla. LIDA: A computational model of global workspace theory and developmental learning. AAAI Fall Symposium on AI and Consciousness: Theoretical Foundations and Current Approaches, pages 61–66, 2007.
• Aregahegn Negatu, Sidney K. D'Mello, and Stan Franklin. Cognitively Inspired Anticipation and Anticipatory Learning Mechanisms for Autonomous Agents. In Workshop on Anticipatory Behavior in Adaptive Learning Systems, pages 108–127, 2007.
• Ron Sun and Stan Franklin. Computational models of consciousness: A taxonomy and some examples. In The Cambridge handbook of consciousness, pages 151–174. 2007.
• Stan Franklin and Michael H. Ferkin. Using Broad Cognitive Models and Cognitive Robotics to Apply Computational Intelligence to Animal Cognition. In Applications of Computational Intelligence in Biology, pages 363–394. Springer Berlin Heidelberg, 2008.
• David Friedlander and Stan Franklin. LIDA and a theory of mind. In Proceedings of the first AGI conference, volume 171, pages 137–148, 2008.
• Bernard J. Baars and Stan Franklin. Consciousness Is Computational: the LIDA Model of Global Workspace Theory. International Journal of Machine Consciousness, 1(1):23–32, 2009. (doi:10.1142/S1793843009000050)
• Stan Franklin, Sidney D'Mello, Bernard J. Baars, and Uma Ramamurthy. Evolutionary Pressures for Perceptual Stability and Self As Guides To Machine Consciousness. International Journal of Machine Consciousness, 1(1):99–110, 2009. (doi:10.1142/S1793843009000104)
• S. Franklin and B. J. Baars. Two varieties of unconscious processes. In New Horizons inthe Neuroscience of Consciousness, pages 91–102. 2010.
• Stan Franklin and Bernard J. Baars. Spontaneous Remembering is the Norm: What Integrative Models Tell Us About Human Consciousness and Memory. In J. H. Mace, editor, The Act of Remembering: Toward an Understanding of How We Recall the Past, pages 83–110. Wiley-Blackwell, Oxford, 2010. (doi:10.1002/9781444328202.ch6)
• Ryan McCall, Stan Franklin, and David Friedlander. Grounded Event-Based and Modal Representations for Objects, Relations, Beliefs, etc. In Proceedings of the Florida Artificial Intelligence Research Society Conference, 2010.
• Ryan McCall, Javier Snaider, and Stan Franklin. Sensory and Perceptual Scene Representation. Journal of Cognitive Systems Research, 2010.
• Wendell Wallach, Stan Franklin, and Colin Allen. A conceptual and computational model of moral decision making in human and artificial agents. Topics in Cognitive Science, 2(3):454–485, 2010. (doi:10.1111/j.1756-8765.2010.01095.x)
• D. M. Wilkes, Stan Franklin, Erdem Erdemir, Stephen Gordon, Steve Strain, Karen Miller, and Kazuhiko Kawamura. Heterogeneous artificial agents for triage nurse assistance. In Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots, pages 130–137, 2010. (doi:10.1109/ICHR.2010.5686839)
• Daniela López De Luise, Gabriel Barrera, and Stan Franklin. Robot Localization Using Consciousness. Journal of Pattern Recognition Research, 1:96–119, 2011.
• Sidney D'Mello and Stan Franklin. A Cognitive Model's View of Animal Cognition. Current Zoology, 47(4), 2011. (doi:10.1017/CBO9781107415324.004)
• Tamas Madl, Bernard J. Baars, and Stan Franklin. The timing of the cognitive cycle. PLoS ONE, 6(4), 2011. (doi:10.1371/journal.pone.0014803)
• Stephen Strain and Stan Franklin. Modeling Medical Diagnosis Using a Comprehensive Cognitive Architecture. Journal of Healthcare Engineering, 2:241–258, 2011. (doi:10.1260/2040-2295.2.2.241)
• Usef Faghihi and Stan Franklin. The LIDA Model as a Foundational Architecture for AGI. Theoretical Foundations of Artificial General Intelligence, 4:103–121, 2012. (doi:10.2991/978-94-91216-62-6_7)
• Usef Faghihi, Ryan McCall, and Stan Franklin. A Computational Model of Attentional Learning in a Cognitive Agent. Biologically Inspired Cognitive Architectures, 2:48–56, 2012. (doi:10.1016/j.knosys.2011.09.005)
• Stan Franklin, Steve Strain, Javier Snaider, Ryan McCall, and Usef Faghihi. Global Workspace Theory, its LIDA model and the underlying neuroscience. Biologically Inspired Cognitive Architectures, 1:32–43, 2012. (doi:10.1016/j.bica.2012.04.001)
• Tamas Madl and Stan Franklin. A LIDA-based model of the attentional blink. In Proceedings of International Conference on Cognitive Modeling (ICCM), pages 283–288, 2012.
• Uma Ramamurthy and Stan Franklin. Self-System in a Model of Cognition. International Journal of Machine Consciousness, 4(2):325–333, 2012. (doi:10.1142/S1793843012400185)
• Uma Ramamurthy, Stan Franklin, and Pulin Agrawal. Self-System in a Model of Cognition. International Journal of Machine Consciousness, 4(4):325–333, 2012. (doi:10.1142/S1793843012400185)
• Javier Snaider, Ryan McCall, and Stan Franklin. Time production and representation in a conceptual and computational cognitive model. Cognitive Systems Research, 13(1):59–71, 2012. (doi:10.1016/j.cogsys.2010.10.004)
• Stan Franklin, Steve Strain, Ryan McCall, and Bernard Baars. Conceptual Commitments of the LIDA Model of Cognition. Journal of Artificial General Intelligence, 4(2):1–22, 2013. (doi:10.2478/jagi-2013-0002)
• Tamas Madl, Stan Franklin, Ke Chen, and Robert Trappl. Spatial Working Memory in the LIDA Cognitive Architecture. In Proceedings of the 12th international conference on cognitive modelling, volume 384-390, 2013.
• Daqi Dong and Stan Franklin. Sensory Motor System: Modeling the Process of Action Execution. In Proceedings of the 36th Annual Conference of the Cognitive Science Society, pages 2145–2150, 2014.
• Stan Franklin, Tamas Madl, Sidney D'Mello, and Javier Snaider. LIDA: A systems-level architecture for cognition, emotion, and learning. IEEE Transactions on Autonomous Mental Development, 6(1):19–41, 2014. (doi:10.1109/TAMD.2013.2277589)
• Javier Snaider and Stan Franklin. Vector LIDA. Energy Procedia, 41:188–203, 2014. (doi:10.1016/j.procs.2014.11.103)
• Pulin Agrawal and Stan Franklin. Multi-layer Cortical Learning Algorithms. In IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, pages 141–147, 2015. (doi:10.1109/CCMB.2014.7020707)
• Daqi Dong and Stan Franklin. A New Action Execution Module for the Learning Intelligent Distribution Agent (LIDA): The Sensory Motor System. Cognitive Computation, 7(5):552–568, 2015. (doi:10.1007/s12559-015-9322-3)
• Daqi Dong and Stan Franklin. Modeling Sensorimotor Learning in a Cognitive Model using a Dynamic Learning Rate. Biologically Inspired Cognitive Architectures, 14, 2015.
• Daqi Dong, Stan Franklin, and Pulin Agrawal. Estimating Human Movements Using Memory of Errors. Procedia Computer Science, 71:1–10, 2015. (doi:10.1016/j.procs.2015.12.174)
• Usef Faghihi, Clayton Estey, Ryan McCall, and Stan Franklin. A cognitive model fleshes out Kahneman's fast and slow systems. Biologically Inspired Cognitive Architectures, 11(2015):38–52, 2015. (doi:10.1016/j.bica.2014.11.014)
• Tamas Madl and Stan Franklin. Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture. In Robert Trappl, editor, A Construction Manual for Robots' Ethical Systems. 2015. (doi:10.1017/CBO9781107415324.004)
• Tamas Madl, Stan Franklin, Ke Chen, Daniela Montaldi, and Robert Trappl. Towards real-world capable spatial memory in the LIDA cognitive architecture. Biologically Inspired Cognitive Architectures, 16, 2015. (doi:10.1016/j.bica.2016.02.001)
• Steve Strain, Sean Kugele, and Stan Franklin. The learning intelligent distribution agent (LIDA) and medical agent X (MAX): Computational intelligence for medical diagnosis. IEEE Symposium on Computational Intelligence for Human-like Intelligence, 2015. (doi:10.1109/CIHLI.2014.7013390)
• Stan Franklin, Tamas Madl, Steve Strain, Usef Faghihi, Daqi Dong, Sean Kugele, Javier Snaider, Pulin Agrawal, and Sheng Chen. A LIDA cognitive model tutorial. Biologically Inspired Cognitive Architectures, 16, 2016. (doi:10.1016/j.bica.2016.04.003)
• Tamas Madl, Stan Franklin, Javier Snaider, and Usef Faghihi. Continuity and the Flow of Time — A Cognitive Science Perspective. Philosophy and Psychology of Time, 2016.