creators_name: Prince, Christopher type: confpaper datestamp: 2001-08-12 lastmod: 2011-03-11 08:54:43 metadata_visibility: show title: Theory Grounding in Embodied Artificially Intelligent Systems ispublished: inpress subjects: comp-sci-art-intel subjects: comp-sci-robot subjects: dev-psy full_text_status: public keywords: symbol grounding, robotics, theory development abstract: Theory grounding is suggested as a way to address the unresolved cognitive science issues of systematicity and productivity. Theory grounding involves grounding the theory skills and knowledge of an embodied artificially intelligent (AI) system by developing theory skills and knowledge from the bottom up. It is proposed that theory grounded AI systems should be patterned after the psychological developmental stages that infants and young children go through in acquiring naïve theories. Systematicity and productivity are properties of certain representational systems indicating the range of representations the systems can form. Systematicity and productivity are likely outcomes of theory grounded AI systems because systematicity and productivity are theoretical concepts. Theory grounded systems should be well oriented to acquire and develop these theoretical concepts. date: 2001 date_type: published refereed: TRUE referencetext: Breazeal, C. & Scassellati, B. (2000). Infant-like social interactions between a robot and a human caregiver. Adaptive Behavior, 8, 49-74. Brooks, R. A. (1989). A robot that walks: Emergent behavior from a carefully evolved network. Neural Computation, 1, 253-262. Brooks, R. A. (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, MA: MIT Press. Byrne, R. W. & Russon, A. E. (1998). Learning by Imitation: a Hierarchical Approach, Behavioral and Brain Sciences, 16, 667-721. Chomsky, N. (1968). Language and Mind. New York: Harcourt, Brace and World. Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195-225. Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press. Fahlman, S. E. & Lebiere, C. (1990). The cascade-correlation learning architecture. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol 2 (pp. 524-532). San Mateo: Morgan Kaufmann. Fodor, J. A. & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71. Flavell, J. H. (2000). Development of children’s knowledge about the mental world. International Journal of Behavioral Development, 24, 15-23. Gallistel, C. R. (2001). The Symbolic Foundations of Conditioned Behavior. Colloquium given at University of Minnesota, Minneapolis, MN on May 11, 2001. Gallistel, C. R. & Gibbon, J. (2000). Time, rate, and conditioning. Psychological Review, 107, 289-344. Gopnik, A. & Meltzoff, A. N. (1997). Words, Thoughts, and Theories. Cambridge, MA: MIT Press. Herman, L. M., Richards, D. G., & Wolz, J. P. (1984). Comprehension of sentences by bottlenosed dolphins. Cognition, 16, 129-219. Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335-346. Kirsh, D. (1991). Today the earwig, tomorrow man? Artificial Intelligence, 47, 161-184. MacDorman, K. F. (1999). Grounding symbols through sensorimotor integration. Journal of the Robotics Society of Japan, 17, 20-24. Macuda, T., & Roberts, W. A. (1995). Further evidence for hierarchical chunking in rat spatial memory. Journal of Experimental Psychology: Animal Behavior Processses, 21, 20-32. Meltzoff, A. N. (1999). Origins of theory of mind, cognition and communication. Journal of Communication Disorders, 32, 251-269. Mitchell, T. M. (1980). The Need for Biases in Learning Generalizations. Technical report CBM-TR-117, Computer Science Department, Rutgers University, New Brunswick, NJ. Mitchell, T. M., Keller, R., & Kedar-Cabelli, S. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1, 47-80. Mooney, R. J. (1993). Integrating theory and data in category learning. In: G. Nakamura, R. Taraban, & D. L. Medin (Eds.), Categorization by Humans and Machines: The Psycholology of Learning and Motivation, Vol. 29 (pp. 189-218). Orlando, FL: Academic Press. Munakata, Y. (1998). Infant perseveration and implications for object permanence theories: A PDP model of the A task. Developmental Science, 1, 161-211. Newcombe, N. S. & Huttenlocher, J. (2000). Making Space: The Development of Spatial Reasoning. Cambridge, MA: MIT Press. Pepperberg, I. M. (1992). Proficient performance of a conjunctive, recursive task by an African gray parrot (Psittacus erithacus). Journal of Comparative Psychology, 106, 295-305. Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77-105. Prince, C. G. (1993). Conjunctive Rule Comprehension in a Bottlenosed Dolphin. Unpublished masters thesis, University of Hawaii. Rovee-Collier, C. (1990). The “memory system” of prelinguistic infants. In A. Diamond (Ed.), The Development and Neural Bases of Higher Cognitive Functions. Annals of the New York Academy of Sciences (no. 608, pp. 517-542). New York: New York Academy of Sciences. Savage-Rumbaugh, E. S., Murphy, J., Sevcik, R. A., Brakke, K. E., Williams, S. L., & Rumbaugh, D. M. (1993). Language comprehension in ape and child. Monographs of the Society for Research in Child Development, 58, pp. v-221. Schlesinger, M. & Barto, A. (1999). Optimal control methods for simulating the perception of causality in young infants. In M. Hahn & S. C. Stoness (Eds.), Proceedings of the Twenty First Annual Conference of the Cognitive Science Society (pp. 625-630). New Jersey: Erlbaum. Schlesinger, M. & Parisi, D. (2001). The agent-based approach: A new direction for computational models of development. Developmental Review, 21, 121-146. Touretzky, D. S. & Pomerleau, D. A. (1994). Reconstructing physical symbol systems. Cognitive Science, 18, 345-354. Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception. Cognition, 13, 103-128. Watson, J. S. (1972). Smiling, cooing, and “The Game.” Merrill-Palmer Quarterly, 18, 323-339. Wellman, H. M. (1990). The Child’s Theory of Mind. Cambridge, MA: MIT Press. Weng, J. & Stockman, I. (2000). Workshop on Development and Learning held at Michigan State University, Kellogg Center, East Lansing, MI, USA, April 5-7. Woodward, A. L. (1999). Infants’ ability to distinguish between purposeful and non-purposeful behaviors. Infant Behavior & Development, 22, 145-160. citation: Prince, Christopher (2001) Theory Grounding in Embodied Artificially Intelligent Systems. [Conference Paper] (In Press) document_url: http://cogprints.org/1635/1/TheoryGrounding.doc document_url: http://cogprints.org/1635/5/TheoryGrounding.pdf