Modelling Concept Prototype Competencies using a Developmental Memory Model

Paul Baxter 1 , Joachim de Greeff 1 , Rachel Wood 2  and Tony Belpaeme 1
  • 1 Centre for Robotics and Neural Systems, Cognition Institute, Plymouth University, U.K.
  • 2 Intelligent Computer Systems, Faculty of Information & Communication Technology, University of Malta, Malta

Abstract

The use of concepts is fundamental to human-level cognition, but there remain a number of open questions as to the structures supporting this competence. Specifically, it has been shown that humans use concept prototypes, a flexible means of representing concepts such that it can be used both for categorisation and for similarity judgements. In the context of autonomous robotic agents, the processes by which such concept functionality could be acquired would be particularly useful, enabling flexible knowledge representation and application. This paper seeks to explore this issue of autonomous concept acquisition. By applying a set of structural and operational principles, that support a wide range of cognitive competencies, within a developmental framework, the intention is to explicitly embed the development of concepts into a wider framework of cognitive processing. Comparison with a benchmark concept modelling system shows that the proposed approach can account for a number of features, namely concept-based classification, and its extension to prototype-like functionality.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] M. Bar, The proactive brain: using analogies and associations to generate predictions, Trends in cognitive sciences, vol. 11, no. 7, pp. 280–289, (2007)

  • [2] P. Baxter, Foundations of a constructivist memory-based approach to cognitive robotics, PhD. Thesis, University of Reading, U.K., (2010)

  • [3] P. Baxter, J. de Greeff, R. Wood, T. Belpaeme, “And what is a Seasnake?”: Modelling the Acquisition of Concept Prototypes in a Developmental Framework, 2nd joint International Conference on Developmental Learning (ICDL) & Epigenetic Robotics, San Diego, USA, IEEE Press, (2012)

  • [4] P. Baxter and W. Browne, Memory as the substrate of cognition: a developmental cognitive robotics perspective, 10th International Conference on Epigenetic Robotics, pp. 19–26, (2010)

  • [5] P. Baxter, R. Wood, A. Morse, and T. Belpaeme, Memory-Centred Architectures: Perspectives on Human-level Cognitive Competencies, AAAI Fall 2011 Symposium on Cognitive Systems, pp. 26–33, (2011)

  • [6] H. Branigan, M. Pickering, J. Pearson and J. McLean, Linguistic alignment between people and computers, Journal of Pragmatics, vol. 42, no. 9, pp 2355–2368, (2010)

  • [7] A. Burton, Learning new faces in an interactive activation and competition model, Visual Cognition, vol. 1, no. 2, pp. 313–348, (1994)

  • [8] A. Chella, S. Gaglio, and R. Pirrone, Conceptual representations of actions for autonomous robots, Robotics and Autonomous Systems, vol. 34, no. 4, pp. 251–263, doi:10.1016/S0921-8890(00)00121-4, (2001)

  • [9] A. Frank and A. Asuncion, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml (accessed 15/12/2012), Irvine, CA: University of California, School of Information and Computer Science, (2010)

  • [10] P. Gärdenfors, Conceptual Spaces: The Geometry of Thought, Cambridge, MA: MIT Press, (2000)

  • [11] P. Gärdenfors, and M. Warglien, Using Conceptual Spaces to Model Actions and Events, Journal of Semantics, doi:10.1093/jos/ffs007, (2012)

  • [12] J. de Greeff, F. Delaunay, and T. Belpaeme, Human-Robot Interaction in Concept Acquisition: a computational model, International Conference on Development and Learning, pp. 1–6, (2009)

  • [13] J. de Greeff, F. Delaunay, and T. Belpaeme, Active robot learning with human tutelage, 2nd joint International Conference on Developmental Learning (ICDL) & Epigenetic Robotics, San Diego, USA, IEEE Press, (2012)

  • [14] M. Kiefer, and F. Pulvermuller, Conceptual representations in mind and brain: theoretical developments, current evidence and future directions, Cortex, vol. 48, no. 7, pp. 805–25. doi:10.1016/j.cortex.2011.04.006, (2012)

  • [15] R. Leech, D. Mareschal, and R. Cooper, Analogy as relational priming: a developmental and computational perspective on the origins of a complex cognitive skill, Behavioral and Brain Sciences, vol. 31, no. 4, pp. 357–78; discussion 378–414, (2008)

  • [16] E. Margolis and S. Laurence, Concepts: Core Readings, MIT Press, (1999)

  • [17] J. McClelland and D. Rumelhart, An Interactive Activation Model of Context Effects in Letter Perception: Part 1, an account of basic findings, Psychological Review, vol. 88, no. 5, pp. 375–407, (1981)

  • [18] C. McNorgan, J. Reid, and K. McRae, Integrating conceptual knowledge within and across representational modalities, Cognition, doi:10.1016/j.cognition.2010.10.017, (2010)

  • [19] A. Morse, J. De Greeff, T. Belpaeme, and A. Cangelosi, Epigenetic Robotics Architecture (ERA), IEEE Transactions on Autonomous Mental Development, vol. 2, no. 4, pp. 325–339, (2010)

  • [20] G. Murphy, The Big Book of Concepts, MIT Press, (2002)

  • [21] R. Nosofsky, Attention, similarity, and the identification/categorization relationship, Journal of Experimental Psychology-General, vol. 115, no. 1, pp. 39–57, (1986)

  • [22] M. Pickering, and S. Garrod, Toward a mechanistic psychology of dialogue, The Behavioral and brain sciences, vol. 27, no. 2, pp 169–190, (2004)

  • [23] R. Pfeifer, M. Lungarella, and F. Iida, Self-organization, embodiment, and biologically inspired robotics, Science, vol. 318, no. 5853, pp 1088–1093, (2007)

  • [24] E. Rosch, Natural categories, Cognitive Psychology, vol. 4, no. 3, pp. 328–350, (1973)

  • [25] R. Shepard, Toward a universal law of generalization for psychological science, Science, vol. 237, no. 4820, pp. 1317–1323, (1987)

  • [26] F. Shic, and B. Scassellati, Pitfalls in the Modeling of Developmental Systems, International Journal of Humanoid Robotics, vol. 4, no. 2, pp. 435–454, doi:10.1142/S0219843607001084, (2007)

  • [27] E. Smith and D. Medin, Categories and Concepts, vol. 4, Harvard University Press, (1981)

  • [28] L. Steels, The Talking Heads Experiment. Volume 1: Words and Meanings, Laboratorium, Antwerpen, (1999)

  • [29] R. Sun, Desiderata for cognitive architectures, Philosophical Psychology, vol. 17, no. 3, pp 341–373, (2004)

  • [30] A. Thomaz, Socially Guided Machine Learning, PhD thesis, MIT, (2006)

  • [31] J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. Thelen, Autonomous mental development by robots and animals, Science, vol. 291, pp. 599–600, (2001)

  • [32] R. Wood, P. Baxter, and T. Belpaeme, A review of long-term memory in natural and synthetic systems, Adaptive Behavior, vol. 20, no. 2, pp. 81–103, (2012)

OPEN ACCESS

Journal + Issues

Paladyn. Journal of Behavioral Robotics is a fully peer-reviewed, open access journal that publishes original, high-quality research works and review articles on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems. The journal is indexed in SCOPUS.

Search