A Connectionist Encoding of Parameterized Schemas and Reactive Plans

We present a connectionist realization of parameterized schemas that can model highlevel sensory-motor processes and be a candidate representation for implementing reactive behaviors. The connectionist realization involves a number of ideas including the use of focal-clusters and feedback loops to control a distributed process without a central controller and the expression and propagation of dynamic bindings via temporal synchrony. We employ a uniform mechanism for interaction between schemas, low-level somatosensory and proprioceptive processes, and high-level reasoning and memory processes. Our representation relates to work in connectionist models of rapid — reflexive — reasoning and also suggests solutions to several problems in language acquisition and understanding.

[1]  D. R. Mani,et al.  Reflexive Reasoning with Multiple Instantiation in a Connectionist Reasoning System with a Type Hierarchy , 1993 .

[2]  Jerome A. Feldman,et al.  Modeling Embodied Lexical Development , 1997 .

[3]  G. R. Potts,et al.  Assessing the occurrence of elaborative inferences: Lexical decision versus naming , 1988 .

[4]  G. Rizzolatti,et al.  Localization of grasp representations in humans by PET: 1. Observation versus execution , 1996, Experimental Brain Research.

[5]  D. R. Mani,et al.  The design and implementation of massively parallel knowledge representation and reasoning systems: a connectionist approach , 1996 .

[6]  G. Rizzolatti,et al.  Premotor cortex and the recognition of motor actions. , 1996, Brain research. Cognitive brain research.

[7]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[8]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[9]  David Bailey,et al.  Layered Hybrid Connectionist Models for Cognitive Science , 1998, Hybrid Neural Systems.

[10]  Lokendra Shastri,et al.  Soft Computing in SHRUTI - A Neurally Plausible Model of Reflexive Reasoning and Relational Information Processing , 1999, IIA/SOCO.

[11]  J. Tanji.  The supplementary motor area in the cerebral cortex , 1994, Neuroscience Research.

[12]  Jerome A. Feldman,et al.  Dynamic connections in neural networks , 1990, Biological Cybernetics.

[13]  N. A. Bernshteĭn.  The co-ordination and regulation of movements , 1967 .

[14]  Stephen Monsell,et al.  The Latency and Duration of Rapid Movement Sequences: Comparisons of Speech and Typewriting , 1978 .

[15]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[16]  L. Shastri,et al.  Knowledge Fusion in the Large - taking a cue from the brain , 1999 .

[17]  Scott T. Grafton,et al.  Localization of grasp representations in humans by positron emission tomography , 1996, Experimental Brain Research.

[18]  Andreas Stolcke,et al.  L0-The first five years of an automated language acquisition project , 2004, Artificial Intelligence Review.

[19]  Lokendra Shastri,et al.  A Biological Grounding of Recruitment Learning and Vicinal Algorithms , 1999 .

[20]  Lokendra Shastri,et al.  Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony , 1999, Applied Intelligence.

[21]  Roger Ratcliff,et al.  The comprehension processes and memory structures involved in anaphoric reference , 1980 .

[22]  Srinivas Narayanan,et al.  Reasoning About Actions in Narrative Understanding , 1999, IJCAI.

[23]  W. Kintsch.  The role of knowledge in discourse comprehension: a construction-integration model. , 1988, Psychological review.

[24]  Nils J. Nilsson,et al.  Teleo-Reactive Programs for Agent Control , 1993, J. Artif. Intell. Res..

[25]  H. Head.  STUDIES IN NEUROLOGY , 1921 .

[26]  Lokendra Shastri,et al.  Rules and Variables in Neural Nets , 1991, Neural Computation.

[27]  M. Jeannerod.  The cognitive neuroscience of action , 1997, Trends in Cognitive Sciences.

[28]  J. Keenan,et al.  The effects of causal cohesion on comprehension and memory , 1984 .

[29]  Rajesh P. N. Rao,et al.  Embodiment is the foundation, not a level , 1996, Behavioral and Brain Sciences.

[30]  B. Habibi,et al.  Pengi : An Implementation of A Theory of Activity , 1998 .

[31]  Lokendra Shastri,et al.  A Computational Model of Tractable Reasoning - Taking Inspiration from Cognition , 1993, IJCAI.

[32]  Daniel Bullock,et al.  Motorneuron Recruitment , 2001 .

[33]  Ronald C. Arkin,et al.  Integrating behavioral, perceptual, and world knowledge in reactive navigation , 1990, Robotics Auton. Syst..

[34]  L. Shastri,et al.  A Connectionist Treatment of Negation and Inconsistency , 1996 .

[35]  Michael A. Arbib,et al.  The Metaphorical Brain 2 , 1989 .

[36]  W. Singer.  Synchronization of cortical activity and its putative role in information processing and learning. , 1993, Annual review of physiology.

[37]  G. Rizzolatti,et al.  Action recognition in the premotor cortex. , 1996, Brain : a journal of neurology.

[38]  K. Pearson.  Common principles of motor control in vertebrates and invertebrates. , 1993, Annual review of neuroscience.

[39]  C. von der Malsburg,et al.  Am I Thinking Assemblies , 1986 .

[40]  Vladimir Lifschitz,et al.  Frames in the Space of Situations , 1990, Artif. Intell..

[41]  Lokendra Shastri,et al.  Recruitment of binding and binding-error detector circuits via long-term potentiation , 1999, Neurocomputing.

[42]  Michael Gelfond,et al.  Representing Action and Change by Logic Programs , 1993, J. Log. Program..

[43]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .