Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Summary
Design Issues and Case Studies
Design Issues for Metacognition
Discussion and Conclusions
References

Metacognition for a Common Model of Cognition

Profile image of Oscar J . RomeroOscar J . Romero

2018, Procedia Computer Science

Abstract

This paper provides a starting point for the development of metacognition in a common model of cognition. It identifies significant theoretical work on metacognition from multiple disciplines that the authors believe worthy of consideration. After first defining cognition and metacognition, we outline three general categories of metacognition, provide an initial list of its main components, consider the more difficult problem of consciousness, and present examples of prominent artificial systems that have implemented metacognitive components. Finally, we identify pressing design issues for the future.

Related papers

Metacognition: computation, biology and function
Chris Frith

Philosophical Transactions of the Royal Society B: Biological Sciences, 2012

Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape.

View PDFchevron_right
Integrating Metacognition into Artificial Agents
Kenneth MBale

2013

Artificial agents need to adapt in order to perform effectively in situations outside of their normal operation specifications. Agents that do not have the capability to adapt to unanticipated situations cannot recover from unforeseen failures and hence are brittle systems. One approach to deal with the brittleness problem is to have a metacognitive component that watches the performance of a host agent and suggests corrective actions to recover from failures. This paper presents the architecture of a metacognitive agent that can be integrated with any host cognitive agent so that the resulting system can dynamically create expectations about observations from a host agent’s sensors, and make use of these expectations to notice expectation violations, assess the cause of a violation and guide a correction if required to deal with the violation. The agent makes use of the metacognitive loop (MCL) and three generic ontologies -- indications of failures, causes of failures and response...

View PDFchevron_right
Two Approaches to Implementing Metacognition
Matthew Paisner

2014

Metacognition, the ability to monitor and regulate cognition, is important for an agent to adapt to novel situations and fix discrepancies in its knowledge base. In this paper, we discuss two different approaches to implementing metacognition in artificial systems: internally and externally. In the internal approach, metacognition is built into the agent, and thus, is combined with its cognitive reasoning abilities. The same Knowledge Base (KB) is fully shared between the metacognitive and cognitive processes of the artificial system. In the external metacognition approach, only portions of the agent's KB are shared between the agent and the external metacognition. We describe the implementation of external metacognition using our own Metacognitive Loop (MCL2) and internal metacognition using active logic in the context of a dialog agent called Alfred. We discuss how the two systems handle long pauses in dialog and compare the pros and cons of each. Our experiments show that for a system with time-related expectations, it is more efficient to use interleaved metacognition rather than an external metacognition module as the internal metacognition has access to the entire KB of the agent.

View PDFchevron_right
Learning Based Integrated Metacognitive Architecture for Conscious Agents (LIMA
Journal of Computer Science IJCSIS

Metacognition in robotics refers to machines having capability to monitor and control own processes. Research in metacognition has grown during last decade and recent machines have increased freedom of decision making. During last decade several models have been proposed to study Metacognition in machines, but these models have several limitations including complexity, partial architectural support of Metacognition functions and components. This paper propose the architecture for meta-cognitive agents having ability to perform human like meta-cognitive functions.

View PDFchevron_right
Metacognitive AI: Framework and the Case for a Neurosymbolic Approach
Bruce Draper, Paulo Shakarian

NeSy, 2024

Metacognition is the concept of reasoning about an agent's own internal processes and was originally introduced in the field of developmental psychology. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.

View PDFchevron_right
The role of metacognition in robust AI systems
Michael Anderson

2008

Abstract As automated systems become more complex, their propensity to fail in unexpected ways increases. As humans, we often notice their failures with the same ease that we recognize our own plans going awry. Yet the systems themselves are frequently oblivious that the function they are designed to perform is no longer being performed. This is because humans have explicit expectations–about both the system's behavior and our own behaviors–that allow us to notice an unexpected event.

View PDFchevron_right
Metacognition Revealed Computationally
Narendra Kumar Kamila

International Journal of Instrumentation Control and Automation, 2012

This paper focuses on current progress for the understanding of human cognition. Here different models have been considered such as MLP, FLANN, PNN, MLR, and HSN for recognition of one of the state of mind. It is argued that in addition to other models, PSO occupies a prominent place in the future of cognitive science, and that cognitive scientists should play an active role in the process. Baysian Approach in the same context has also discussed. The special case of predicting harm doing in a particular mental state has been experimented taking different models into account in depicting decision making as a process of probabilistic, knowledge-driven inference.

View PDFchevron_right
Extended Metacognition for Artificially Intelligent Systems (AIS): Artificial Locus of Control and Cognitive Economy
James Crowder

Theories into human learning and cognition have led to much research into new methods and structures for Artificial Intelligence (AI) and Artificially Intelligent Systems (AIS) to learn and reason like humans. As we move toward completely autonomous AIS, the ability to provide metacognitive capabilities becomes important [Crowder and Friess 2011b] in order for the AIS to deal with entirely new situations within the environment it may find itself (e.g., deep space, deep undersea). Presented here are theories and methodologies for Constructivist Learning (CL) processes that provide the methodologies to allow completely autonomous AIS to understand, evaluate, and evolve its "Locus of Control [Watts 2003]." Presented will be the a discussion of how the use of AI learning systems, like Occam [Crowder and Carbone 2011a] and PAC learning can be combined with Cognitive Economy concepts to provide this constructivist learning process to allow a Locus of Control evolution within the...

View PDFchevron_right
Proceedings of the 2013 Annual Conference on Advances in Cognitive Systems: Workshop on Metacognition about Artificial Situated Agents
Darsana Purushothaman Josyula

2013

We describe ongoing work toward automating human-level behavior that pulls together much of traditional artificial intelligence in a real-time robotic setting. Natural-language dialog, planning, perception, locomotion, commonsense reasoning, memory, and learning all have key roles in this; and metareasoning is a sort of glue to guide the robot through rough spots.

View PDFchevron_right
Thinking Fast and Slow in AI: the Role of Metacognition
Marianna Bergamaschi Ganapini
View PDFchevron_right

References (60)

  1. Alechina, N., Dastani, M., Logan, B., 2012. Programming norm-aware agents, in: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2, International Foundation for Autonomous Agents and Multiagent Systems. pp. 1057- 1064.
  2. Aleksander, I., Morton, H., 2007. Depictive architectures for synthetic phenomenology. Artificial consciousness , 67-81.
  3. Anderson, J.R., 2009. How can the human mind occur in the physical universe? Oxford University Press.
  4. Anderson, J.R., Fincham, J.M., 2014. Extending problem-solving procedures through reflection. Cognitive psychology 74, 1-34.
  5. Baars, B.J., 2007. The global workspace theory of consciousness, in: The Blackwell companion to consciousness, pp. 236-246.
  6. Bandura, A., 2001. Social cognitive theory: An agentic perspective. Annual review of psychology 52, 1-26.
  7. Boureau, Y.L., Sokol-Hessner, P., Daw, N.D., 2015. Deciding how to decide: self-control and meta-decision making. Trends in cognitive sciences 19, 700-710.
  8. Chalmers, D.J., 1995. Facing up to the problem of consciousness. Journal of consciousness studies 2, 200-219.
  9. Coward, L.A., Sun, R., 2004. Criteria for an effective theory of consciousness and some preliminary attempts. Consciousness and Cognition 13, 268-301.
  10. Cox, M.T., Alavi, Z., Dannenhauer, D., Eyorokon, V., Munoz-Avila, H., Perlis, D., 2016. Midca: A metacognitive, integrated dual-cycle architecture for self-regulated autonomy., in: AAAI, pp. 3712-3718.
  11. Damasio, A., 2011. Thinking about brain and consciousness, in: Characterizing Consciousness: From Cognition to the Clinic?. Springer, pp. 47-54.
  12. Daw, N.D., Niv, Y., Dayan, P., 2005. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature neuroscience 8, 1704-1711.
  13. Dehaene, S., 2014. Consciousness and the brain: Deciphering how the brain codes our thoughts. Penguin.
  14. Dennett, D., 1991. Consciousness explained. New York: Little Brown & Co .
  15. Doya, K., 1999. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural networks 12, 961-974.
  16. Epstein, S.L., 1994. For the right reasons: The FORR architecture for learning in a skill domain. Cognitive science 18, 479-511.
  17. Epstein, S.L., Freuder, E.C., Wallace, R.J., 2005. Learning to support constraint programmers. Computational Intelligence 21, 336-371.
  18. Epstein, S.L., Petrovic, S., 2011. Learning a mixture of search heuristics, in: Autonomous Search. Springer, pp. 97-127.
  19. Flavell, J.H., 1976. Metacognitive aspects of problem solving. The nature of intelligence , 231-235.
  20. Franklin, S., Madl, T., Strain, S., Faghihi, U., Dong, D., Kugele, S., Snaider, J., Agrawal, P., Chen, S., 2016. A lida cognitive model tutorial. Biologically Inspired Cognitive Architectures 16, 105-130.
  21. Gamez, D., 2018. Human and machine consciousness. Open Book Publishers.
  22. Gazzaniga, M., 2011. Who's in Charge? NY:Harper Collins.
  23. Gazzaniga, M., Ivry, R., Mangun, G., 2013. Cognitive Neuroscience: The Biology of the Mind (Fourth Edition). W. W. Norton. URL: https://books.google.co.kr/books?id=MBdBmwEACAAJ.
  24. Gazzaniga, M.S., 2018. The Consciousness Instinct: Unraveling the Mystery of How the Brain Makes the Mind. Farrar Straus and Giroux.
  25. Hare, T.A., Camerer, C.F., Rangel, A., 2009. Self-control in decision-making involves modulation of the vmpfc valuation system. Science 324, 646-648.
  26. Hernández, C., Bermejo-Alonso, J., Sanz, R., 2018. A self-adaptation framework based on functional knowledge for augmented autonomy in robots. Integrated Computer-Aided Engineering , 1-16.
  27. Holyoak, K.J., Morrison, R.G., 2012. The Oxford handbook of thinking and reasoning. Oxford University Press.
  28. Jackson, P.C., 2014. Toward human-level artificial intelligence: Representation and computation of meaning in natural language .
  29. Jackson, P.C., 2018. Toward human-level models of minds, in: The 9th Annual International Conference on Biologically Inspired Cognitive Architectures. (To appear).
  30. Johnson-Laird, P.N., 1983. Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Harvard University Press, Cambridge, MA, USA.
  31. Kahneman, D., Egan, P., 2011. Thinking, fast and slow. volume 1. Farrar, Straus and Giroux New York.
  32. Korpan, R., Epstein, S.L., Aroor, A., Dekel, G., 2017. Why: Natural explanations from a robot navigator. arXiv preprint arXiv:1709.09741 .
  33. Kowaguchi, M., Patel, N.P., Bunnell, M.E., Kralik, J.D., 2016. Competitive control of cognition in rhesus monkeys. Cognition 157, 146-155.
  34. Kralik, J., 2017. Architectural design of mind & brain from an evolutionary perspective, in: Proceedings of the AAAI Fall Symposium A Standard Model of the Mind.
  35. Laird, J.E., Lebiere, C., Rosenbloom, P.S., 2017. A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine 38.
  36. Lee, J., Kralik, J., Jeong, J., 2018a. A general architecture for social intelligence in the human mind and brain, in: AAAI Fall Symposium: Common Model of Cognition. (To appear).
  37. Lee, J., Kralik, J., Jeong, J., 2018b. A sociocognitive-neuroeconomic model of social information communication: To speak directly or to gossip, in: The 40th Annual Meeting of the Cognitive Science Society.
  38. Lee, J., Padget, J., Logan, B., Dybalova, D., Alechina, N., 2014a. N-jason: Run-time norm compliance in agentspeak (l), in: Engineering Multi-Agent Systems, Springer. pp. 367-387.
  39. Lee, J., Padget, J., Logan, B., Dybalova, D., Alechina, N., 2014b. Run-time norm compliance in bdi agents, in: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, pp. 1581-1582.
  40. Lee, S.W., Shimojo, S., O'Doherty, J.P., 2014c. Neural computations underlying arbitration between model-based and model-free learning. Neuron 81, 687-699.
  41. McGreggor, K., 2017. An experience is a knowledge representation, in: AAAI Fall Symposium Series Technical Reports.
  42. Miller, E.K., Cohen, J.D., 2001. An integrative theory of prefrontal cortex function. Annual review of neuroscience 24, 167-202.
  43. Nelson, T.O., 1992. Metacognition: Core readings. Allyn & Bacon.
  44. Newell, A., 1973. You can't play 20 questions with nature and win: Projective comments on the papers of this symposium. Visual Information Processing , 283-310.
  45. Newell, A., 1990. Unified theories of cognition. Harvard University Press.
  46. Ortony, A., Norman, D.A., Revelle, W., 2005. Affect and proto-affect in effective functioning. Who needs emotions? , 173-202.
  47. Pinker, S., 1999. How the mind works. Annals of the New York Academy of Sciences 882, 119-127.
  48. Project CogX, . http://cogx.eu/. Accessed 20180930.
  49. Pynadath, D.V., Marsella, S.C., 2005. Psychsim: Modeling theory of mind with decision-theoretic agents, in: IJCAI, pp. 1181-1186.
  50. Pynadath, D.V., Rosenbloom, P.S., Marsella, S.C., 2014. Reinforcement learning for adaptive theory of mind in the sigma cognitive architecture, in: International Conference on Artificial General Intelligence, Springer. pp. 143-154.
  51. Rosenbloom, P.S., Demski, A., Ustun, V., 2016. The sigma cognitive architecture and system: Towards functionally elegant grand unification. Journal of Artificial General Intelligence 7, 1-103.
  52. Rosenbloom, P.S., Laird, J.E., Newell, A., 1988. Meta-levels in soar, in: Rosenbloom, P.S., Laird, J.E., Newell, A. (Eds.), Meta-Level Archi- tectures and Reflection. Amsterdam, NL: North Holland, pp. 227-240.
  53. Sampson, W.W., Khan, S.A., Nisenbaum, E.J., Kralik, J.D., 2018. Abstraction promotes creative problem-solving in rhesus monkeys. Cognition 176, 53-64.
  54. Sanz, R., López, I., Rodríguez, M., Hernández, C., 2007. Principles for consciousness in integrated cognitive control. Neural Networks 20, 938-946.
  55. Schmidtke, H.R., 2018. Logical lateration-a cognitive systems experiment towards a new approach to the grounding problem. Cognitive Systems Research .
  56. Sloman, A., 1999. What sort of architecture is required for a human-like agent?, in: Foundations of rational agency. Springer, pp. 35-52.
  57. Sun, R., 2007. The motivational and metacognitive control in clarion. Modeling integrated cognitive systems , 63-75.
  58. Tononi, G., 2008. Consciousness as integrated information: a provisional manifesto. The Biological Bulletin 215, 216-242.
  59. Tononi, G., Koch, C., 2015. Consciousness: here, there and everywhere? Phil. Trans. R. Soc. B 370, 20140167.
  60. Wright, I., 2000. The society of mind requires an economy of mind, in: Proceedings AISB'00 Symposium Starting from Society -the Application of Social Analogies to Computational Systems, AISB, Birmingham, UK. pp. 113-124.

Related papers

Metacognition and Metamemory Concepts for AI Systems
James Crowder

Recent advances in cognitive computing suggest that computers can do as well as humans, even across multiple information sources and information types [4]. This work establishes an artificial cognitive neural framework to provide Metacognitive and Metamemory processing concepts similar to how the human brain operates to process, categorize and link information. Metacognition provides an Artificial Intelligence system with a sense of Self-Analysis, or Introspection, allowing the system to "think about what it thinks." Metamemory is a concept of an Artificial Intelligence system's memory capabilities and strategies that can aid in memory representation, retention, mining, retrieval, as well as the processes involved in memory Self-Monitoring. Metamemory constructs for an Artificially Intelligent system has important implications about how the system learns and uses memory. For example, the system can make a judgment on whether it has enough information to complete a miss...

View PDFchevron_right
Design and validation of a metamodel for metacognition support in artificial intelligent systems
Michael Cox

Biologically Inspired Cognitive Architectures, 2014

Computational metacognition is a technical area of artificial intelligence whose aim is to increase the degree of autonomy and awareness an intelligent system has about its own reasoning and learning. In the literature, different models of metacognition are applied to artificial intelligent systems. However many of these models have a narrow focus, because they do not address comprehensively the elements of metacognition. This paper presents an analysis of metacognitive models discussed in the literature in order to discover the common (invariants) and varying (variants) elements. The main contribution of this work is the development of a comprehensive and general purpose metamodel named MISM that covers and describes a broad range of commonly referenced concepts in metacognitive models in the area of artificial intelligence. A validation process was conducted to ensure the reliability of MISM in terms of generality, expressiveness and completeness. The validation was performed using three techniques for improvements and adjustments to the metamodel: (i) comparison with other models; (ii) frequency-based selection; and (iii) model tracing. The adjusted and improved version of the metamodel was named MISM 1.1.

View PDFchevron_right
Computational Metacognition
Michael Cox

ArXiv, 2022

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.

View PDFchevron_right
Modeling metacognition for learning in artificial systems
Darsana Purushothaman Josyula

Nature & Biologically …

Evidence suggests that metacognition-the ability to monitor the cognitive processes and regulate them-exists not only in humans but also in some animals. In nature, humans and animals use metacognition to self-regulate their learning process. This paper gathers evidence of metacognition in nature from research in various disciplines. It also shows how metacognition can be modeled in artificial systems and how the model is applied in an Air Traffic Control Simulator system.

View PDFchevron_right
Grounded Metacognitive Architectures For Machine Consciousness
Ron Chrisley

2019

Multiple approaches to machine consciousness emphasise the importance of metacognitive states and processes. A considerable num- ber of cognitive systems researchers prefer architectures that are not classically symbolic, and in which learning, rather a priori structure, is central. But it is unclear how these grounded architectures can support metacognition of the required sort. To investigate this possibility, a basic design sketch of such an architecture is presented.

View PDFchevron_right

Related topics

  • Technology
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved