Acquisition of Abstract Words for Cognitive Robots
DOI:
https://doi.org/10.11113/jt.v72.3878Keywords:
Abstract words, symbol grounding problem, grounded cognition, knowledge representation, neural network, semantic networkAbstract
Abstract word learning and comprehension is a very crucial and important issue because of its application and problematic nature. This problem does not just belong to the cognitive robotics field, as it also has significance in neuroscience and cognitive science. There are many issues like symbol grounding problem and sensory motor processing within grounded cognition framework and conceptual knowledge representation methods that have to be addressed and solved for the acquisition of abstract words in cognitive robots. This paper explains these concepts and matters, and also elucidates how these are linked to this problem. In this paper, first symbol grounding problem is discussed, and after that an overview of grounded cognition be given along with detail of methods/ideas that suggest how abstract word representation could use sensory motor system. Finally, the computation methods used for the representation of conceptual knowledge are discussed. Two cognitive robotics models based on Neural network and Semantic network that ground abstract words are presented and compared via simulation experiment to find out the pros and cons of computation methods for this problem. The aim of this paper is to explore the building blocks of cognitive robotics model at theoretical and experimental level, for grounding of abstract words.
References
Barsalou, L. W. 1999. Perceptual Symbol Systems. Behavioral and Brain Sciences. 22(04): 577–660.
Glenberg, A. M. 1997. What Memory is For. The Behavioral and Brain Sciences. 20(1): 1–19.
Sandini, G., Metta, G., and Vernon, D. 2007. The Icub Cognitive Humanoid Robot: An Open-system Research Platform For Enactive Cognition. In 50 years of artificial intelligence, Springer. 358–369.
Marocco, D., Cangelosi, A., Fischer, K., & Belpaeme, T. 2010. Grounding Action Words in the Sensorimotor Interaction with the World: Experiments with a Simulated iCub Humanoid Robot. Frontiers in Neurorobotics. 4.
K. Karmiloff, K., and Karmiloff-Smith, A. 2009. Pathways to Language: from Fetus to Adolescent. Harvard University Press.
Veneziano, E. 2001. Displacement and Informativeness in Child-Directed Talk. First Language. 21(63): 323–356.
Cangelosi, A., and Riga, T. 2006. An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots. Cognitive Science. 30(4): 673–689.
Cangelosi, a., Hourdakis, E., and Tikhanoff, V. 2006. Language Acquisition and Symbol Grounding Transfer with Neural Networks and Cognitive Robots. Proceedings of The IEEE International Joint Conference on Neural Network.
Barsalou, L. W. and Wiemer-Hastings, K. 2005. Situating Abstract Concepts. In R. A. Pecher, D., Zwaan (Ed.). Grounding Cognition: The Role of Perception and Action in Memory, Language, and Thought. New York: Cambridge University Press. 129–163.
Stramandinoli, F., Cangelosi, A. and Marocco, D. 2011. Towards the Grounding of Abstract Words: A Neural Network Model for Cognitive Robots. Proceedings of The 2011 International Joint Conference on Neural Networks (IJCNN). 467–474.
Roy, D. 2003. Grounded Spoken Language Acquisition: Experiments in Word Learning. Multimedia, IEEE Transactions on. 5(2): 197–209.
De La Cruz, V. M., Di Nuovo, A., Di Nuovo, S. and Cangelosi, A. 2014. Making Fingers and Words Count in a Cognitive Robot. Frontiers in Behavioral Neuroscience. 13.
Stramandinoli, F., Marocco, D. and Cangelosi, A. 2013. Grounding Abstract Action Words Through the Hierarchical Organization of Motor Primitives. Proceedings of The 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL). 1–2.
Nakamura, T., Araki, T., Nagai, T. and Iwahashi, N. 2011. Grounding of Word Meanings in Latent Dirichlet Allocation-based Multimodal Concepts. Advanced Robotics. 25(17): 2189–2206.
Nakamura, T., Nagai, T. and Iwahashi, N. 2009. Grounding of Word Meanings in Multimodal Concepts Using LDA. Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009. 3943–3948.
Nagai, Y. and Rohlfing, K. J. 2009. Computational Analysis of Motionese Toward Scaffolding Robot Action Learning. IEEE Transactions on Autonomous Mental Development. 1(1): 44–54.
Harnad, S. 1990. The Symbol Grounding Problem. Physica D: Nonlinear Phenomena. 42(1–3): 335–346.
Perlovsky, L. I. 2004. Integrating Language and Cognition. IEEE Connections. 2(2): 8–12.
Zwaan, R. A. 1999. Embodied Cognition, Perceptual Symbols, and Situation Models. Discourse Processes.
Grush, R. 2004. The Emulation Theory of Representation: Motor Control, Imagery, and Perception. Behavioral and Brain Sciences. 27(03): 377–396.
Pecher, D., Zeelenberg, R. and Barsalou, L. W. 2003. Verifying Different-modality Properties for Concepts Produces Switching Costs. Psychological Science. 14(2): 119–124.
Glenberg, A. M. and Kaschak, M. P. 2002. Grounding Language in Action. Psychonomic Bulletin & Review. 9(3): 558–565.
Coventry, K. R. and Garrod, S. C. 2004. Saying, Seeing and Acting: The Psychological Semantics of Spatial Prepositions. Hove ,England: Psychology Press.
Pulvermüller, F. 1999. Words in the Brain’s Language. Behavioral and Brain Sciences. 22(02): 253–279.
Kiefer, M. and Pulvermüller, F. 2012. Conceptual Representations in Mind and Brain: Theoretical Developments, Current Evidence and Future Directions. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior. 48(7): 805–25.
Steels, L. 2003. Evolving Grounded Communication for Robots. Trends in Cognitive Sciences. 7(7): 308–312.
Cangelosi, A., & Parisi, D. 2002. Simulating the Evolution of Language (vol. 1). Springer London.
P. Schwanenflugel, P. J. and Shoben, E. J. 1983. Differential Context Effects in the Comprehension of Abstract snd Concrete Verbal Materials. Journal of Experimental Psychology: Learning, Memory, and Cognition. 9(1): 82.
Schwanenflugel, P. J. and Stowe, R. W. 1989. Context Availability and the Processing of Abstract and Concrete Words in Sentences. Reading Research Quarterly. 114–126.
Nosofsky, R. M. 1988. Similarity, Frequency, and Category Representations. Journal of Experimental Psychology: Learning, Memory, and Cognition. 14(1): 54.
Lakoff, G. and Johnson, M. 2008. Metaphors We Live By. University of Chicago Press.
Lakoff, G., & Turner, M. 2009. More Than Cool Reason: A Field Guide to Poetic Metaphor. University of Chicago Press.
Lakoff, G. and Johnson, M. 1999. Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic books.
Johnson, M. 2013. The Body in the Mind: The Bodily Basis of Meaning, Imagination, and Reason. University of Chicago Press.
Humphreys, G. W., Riddoch, M. J. and Quinlan, P. T. 1988. Cascade Processes in Picture Identification. Cognitive Neuropsychology. 5(1): 67–104.
Levelt, W. J. M., Roelofs, A. and Meyer, A. S. 1999. A Theory of Lexical Access in Speech Production. Behavioral and Brain Sciences. 22(01): 1–38.
Collins, A. M. and Quillian, M. R. 1969. Retrieval Time from Semantic Memory. Journal of Verbal Learning and Verbal Behavior. 8(2): 240–247.
Collins, A. M. and Loftus, E. F. 1975. A Spreading-activation Theory of Semantic Processing. Psychological Review. 82(6): 407.
Anderson, J. (n.d.). R. 1983. The Architecture of Cognition. Cambridge, Mass. HalYard University Press.
Rumelhart, D. E., McClelland, J. L. and PDP Research Group, C. (Eds.). 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. Cambridge, MA, USA: MIT Press.
Simmons, W. K. and Barsalou, L. W. 2003. The Similarity-in-Topography Principle: Reconciling Theories of Conceptual Deficits. Cognitive Neuropsychology. 20(3–6): 451–486.
Avery, E., Kelley, T. D. and Davani, D. 2006. Using Cognitive Architectures to Improve Robot Control: Integrating Production Systems, Semantic Networks, and Sub-symbolic Processing. Proceedings of The 15th Annual Conference on Behavioral Representation in Modeling and Simulation (BRIMS). Citeseer.
Fonooni, B., Hellström, T. and Janlert, L.-E. 2012. Learning High-level Behaviors from Demonstration through Semantic Networks. In ICAART (1). 419–426.
Kelley, T. D. and Avery, E. 2010. A Cognitive Robotics System: The Symbolic and Sub-Symbolic Robotic Intelligence Control System (SS-RICS). In SPIE Defense, Security, and Sensing . 77100I–77100I.
Pastra, K., Balta, E., Dimitrakis, P. and Karakatsiotis, G. 2011. Embodied Language Processing: A New Generation of Language Technology. In Language-Action Tools for Cognitive Artificial Agents.
Ha, I., Tamura, Y., Asama, H., Han, J. and Hong, D. W. 2011. Development of Open Humanoid Platform DARwIn-OP. Proceedings of The 2011 ICE Annual Conference (SICE) . 2178–2181.
Borghi, A. M., Flumini, A., Cimatti, F., Marocco, D. and Scorolli, C. 2010. Manipulating Objects and Telling Words: A Study on Concrete and Abstract Words Acquisition. Embodied and Grounded Cognition. 173.
Acuna, B. D., Eliassen, J. C., Donoghue, J. P. and Sanes, J. N. 2002. Frontal and Parietal Lobe Activation During Transitive Inference in Humans. Cerebral Cortex. 12(12): 1312–1321.
Heckers, S., Zalesak, M., Weiss, A. P., Ditman, T., & Titone, D. 2004. Hippocampal Activation During Transitive Inference in Humans. Hippocampus. 14(2): 153–162.
Vasconcelos, M. 2008. Transitive Inference in Non-human Animals: An Empirical and Theoretical Analysis. Behavioural Processes. 78(3): 313–334.
Kunze, L., Roehm, T. and Beetz, M. 2011. Towards Semantic Robot Description Languages. Proceedings of The 2011 IEEE International Conference on Robotics and Automation (ICRA). 5589–5595.
Nakamura, Y. and Sekiguchi, A. 2001. The Chaotic Mobile Robot. Robotics and Automation, IEEE Transactions. 17(6): 898–904.
Rich, E., & Knight, K. 1991. Artiï¬cial Intelligence. 3rd ed. Tata McGraw Hill,New Dehli
Dreisiger, P. 2008. Cognitive Approaches to Learning in a Hybrid Semantic Network : An Examination of Localist and Dual Localist – Distributed Representations and their Role in Concept Formation. 1–18.
Barsalou, L. W. 2008. Grounded cognition. Annu. Rev. Psychol. 59: 617–645.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
