Enabling an autonomous agent sharing its minds, describing its conscious contents
Tài liệu tham khảo
Baars, 1988
Baars, 2002, The conscious access hypothesis: Origins and recent evidence, Trends in Cognitive Sciences, 6, 47, 10.1016/S1364-6613(00)01819-2
Bono, 2020, An ACT-R based humanoid social robot to manage storytelling activities, Robotics, 9, 25, 10.3390/robotics9020025
Brooks, R. A. (1991). How to build complete creatures rather than isolated cognitive simulators. In: Architectures for intelligence: The twenty-second carnegie mellon symposium on cognition (pp. 225–239).
Bullock, T. H. (1993). Goals and strategies in brain research: The place of comparative neurology. In: How do brains work? (pp. 1–8). Springer.
Chin-Parker, 2010, Background shifts affect explanatory style: How a pragmatic theory of explanation accounts for background effects in the generation of explanations, Cognitive Processing, 11, 227, 10.1007/s10339-009-0341-4
Dong, D., & Franklin, S. (2014). Sensory Motor System: Modeling the process of action execution. In: Paper presented at the Proceedings of the 36th Annual Conference of the Cognitive Science Society (2145-2150). Quebec, Canada.
Dong, 2015, A new action execution module for the Learning Intelligent Distribution Agent (LIDA): The sensory motor system, Cognitive Computation, 1
Franklin, 1995
Franklin, S., & Graesser, A. (1997). Is it an agent, or just a program?: A taxonomy for autonomous agents. In: Intelligent agents III agent theories, architectures, and languages (pp. 21–35). London, UK: Springer-Verlag.
Franklin, 2016, A LIDA cognitive model tutorial, Biologically Inspired Cognitive Architectures, 105, 10.1016/j.bica.2016.04.003
Goodale, 1992, Separate visual pathways for perception and action, Trends in Neurosciences, 15, 20, 10.1016/0166-2236(92)90344-8
Khayi, 2018, Initiating language in LIDA: Learning the meaning of vervet alarm calls, Biologically Inspired Cognitive Architectures, 23, 7, 10.1016/j.bica.2018.01.003
Laird, 2017, Interactive task learning, IEEE Intelligent Systems, 32, 6, 10.1109/MIS.2017.3121552
Laird, 2017, A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics, Ai Magazine, 38, 13, 10.1609/aimag.v38i4.2744
Langley, 2009, Cognitive architectures: Research issues and challenges, Cognitive Systems Research, 10, 141, 10.1016/j.cogsys.2006.07.004
Lebiere, C., Jentsch, F., & Ososky, S. (2013). Cognitive models of decision making processes for human-robot interaction. In: Paper presented at the International Conference on Virtual, Augmented and Mixed Reality (pp. 285–294).
Lindes, 2018, The Common Model of Cognition and humanlike language comprehension, Procedia Computer Science, 145, 765, 10.1016/j.procs.2018.11.032
Lindes, P., Mininger, A., Kirk, J. R., & Laird, J. E. (2017). Grounding language for interactive task learning. In: Paper presented at the Proceedings of the First Workshop on Language Grounding for Robotics (pp. 1-9).
Lombrozo, 2006, The structure and function of explanations, Trends in Cognitive Sciences, 10, 464, 10.1016/j.tics.2006.08.004
Madl, 2011, The timing of the cognitive cycle, PLoS One, 6, e14803, 10.1371/journal.pone.0014803
Matarese, M., Rea, F., & Sciutti, A. (2021). A user-centred framework for explainable artificial intelligence in human-robot interaction. arXiv preprint arXiv:2109.12912.
McCall, 2020, Artificial motivation for cognitive software agents, Journal of Artificial General Intelligence, 11, 38, 10.2478/jagi-2020-0002
Milner, 2008, Two visual systems re-viewed, Neuropsychologia, 46, 774, 10.1016/j.neuropsychologia.2007.10.005
Mutlu, B., Roy, N., & Šabanović, S. (2016). Cognitive human–robot interaction. In: Springer handbook of robotics (pp. 1907–1934).
Newell, A. (1973). You can't play 20 questions with nature and win: Projective comments on the papers of this symposium.
Ramaraj, P., Klenk, M., & Mohan, S. (2020). Understanding intentions in human teaching to design interactive task learning robots. In: Paper presented at the RSS 2020 Workshop: AI & Its Alternatives in Assistive & Collaborative Robotics: Decoding Intent.
Ramaraj, P. (2021). Robots that Help Humans Build Better Mental Models of Robots. In: Paper presented at the Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (pp. 595–597).
Snaider, J., McCall, R., & Franklin, S. (2010). The immediate present train model time production and representation for cognitive agents. In: Paper presented at the 2010 AAAI Spring Symposium Series.
Snaider, J., McCall, R., & Franklin, S. (2011). The LIDA framework as a general tool for AGI. In: Artificial general intelligence (pp. 133–142). Berlin Heidelberg: Springer.
Sofge, D., Trafton, J. G., Cassimatis, N., Perzanowski, D., Bugajska, M., Adams, W., & Schultz, A. (2004). Human-robot collaboration and cognition with an autonomous mobile robot. In: Paper presented at the In Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS-8) (pp. 80–87).
Trafton, 2013, Act-r/e: An embodied cognitive architecture for human-robot interaction, Journal of Human-Robot Interaction, 2, 30, 10.5898/JHRI.2.1.Trafton
Umbrico, 2022, A mind-inspired architecture for adaptive HRI, International Journal of Social Robotics, 1