Sun, Ron - Cognitive Science Department, Rensselaer Polytechnic Institute

• Cognitive Architectures and Multi-Agent Social Rensselaer Polytechnic Institute, Troy, NY 12180, USA
• The Motivational Subsystem 1.1 Introduction
• Beyond Simple Rule Extraction: The Extraction of Planning Knowledge from Reinforcement Learners
• Autonomous Learning of Sequential Tasks: Experiments and Analyses Ron Sun 1;2 , Todd Peterson 2
• Commonsense Reasoning with Rules, Cases, and Connectionist Models: A Paradigmatic Comparison
• Implicit Learning of Serial Reaction Time Tasks: Connectionist vs. Symbolic Models
• Cognitive Simulation of Academic Science Ron Sun and Isaac Naveh
• Journal of Cognitive Systems Research 1 (2001) 241249 www.elsevier.com/locate/cogsys
• The Interaction of Explicit and Implicit Learning: An Integrated Model Paul Slusarz (kmicic@ideaworks.com)
• AAAI-06 Workshop on Cognitive Modeling and
• Connectionist Variable Binding Antony Browne1
• Theoretical status of computational cognitive modeling Action editor: Vasant Honavar
• The Interaction of Implicit Learning, Explicit Hypothesis Testing Learning, and Implicit-to-Explicit Knowledge Extraction
• The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach
• International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.1, No.1 (2005), pp. 4258
• Effects of Model-Based and Memory-Based Processing on Speed and Accuracy of Grammar String Generation
• PHILOSOPHICAL PSYCHOLOGY, VOL. 17, NO. 3, SEPTEMBER 2004 Desiderata for cognitive architectures
• Top-down versus bottom-up learning in cognitive skill acquisition
• Contributed article Connectionist inference models
• From implicit skills to explicit knowledge: a bottom-up model of skill learning
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• Contributed article Multi-agent reinforcement learning: weighting and partitioning
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• Cognitive Social Simulation Incorporating Cognitive Architectures
• Implicit and Explicit Processes in the Development of Cognitive Skills: A Theoretical Interpretation with Some
• A Cognitively Based Simulation of Simple Organizations Ron Sun (rsun@rpi.edu)
• Abstract--Most psychological theories of problem solving have focused on modeling explicit processes that gradually
• Knowledge Integration in Creative Problem Solving Sbastien Hlie (helies@rpi.edu)
• Accounting for Similarity-Based Reasoning within a Cognitive Architecture
• For decades, Cognitive Science has been grappling with the issue of how to best understand the interactions between symbolic and subsymbolic cognition. In this ambitious and
• Properties of CLARION-H Sbastien Hlie & Ron Sun
• Questions and Answers In this chapter, I address a number of commonly raised questions about
• Moody JE (1992) The effective number of parameters: an analysis of generalization and regularization in
• Journal of Cognitive Systems Research 2 (2001) 3954 www.elsevier.com/locate/cogsys
• Journal of Cognitive Systems Research 2 (2001) 6779 www.elsevier.com/locate/cogsys
• Journal of Cognitive Systems Research 2 (2001) 8193 www.elsevier.com/locate/cogsys
• How to Make a Camera-Ready Proceedings Contribution Morton Ann Gernsbacher (MAG@Macc.Wisc.Edu)
• Arti cial Intelligence November 4, 1996
• necessary, that is, performing a search over the space of all possible ways of removing nodes, until a test shows that all similarity matching measures are implemented correctly.
• A Hybrid Learning Model for Reactive Sequential Decision Making Department of Computer Science
• Accounting for the Computational Basis of Consciousness: A Connectionist Approach
• AN INTRODUCTION: ON SYMBOLIC PROCESSING IN
• From Implicit Skills to Explicit Knowledge: A Bottom-Up Model of Skill Learning
• The CLARION Cognitive Architecture: A Tutorial Sbastien Hlie (helies@rpi.edu)
• META-LEARNING PROCESSES IN MULTI-AGENT SYSTEMS CECS, University of Missouri, Columbia, MO 65211, USA
• Some Experiments with a Hybrid Model for Learning Sequential Decision Making
• Journal of Cognitive Systems Research 2 (2001) 538 www.elsevier.com/locate/cogsys
• T u t o r i a l 2 1094-7167/01/$10.00 2001 IEEE IEEE INTELLIGENT SYSTEMS
• Some Experiments with a Hybrid Model for Learning Sequential Decision Making
• Knowledge Extraction from Reinforcement Learning NEC Research Institute and University of Alabama
• Incubation, Insight, and Creative Problem Solving: A Unified Theory and a Connectionist Model
• Top-Down versus Bottom-Up Learning in Skill Acquisition Ron Sun (rsun@cecs.missouri.edu)
• Integrating Reinforcement Learning, Bidding and Genetic Algorithms Lamar University
• 1997 SPECIAL ISSUE Learning, Action and Consciousness: A Hybrid
• SelfSegmentation of Sequences Ron Sun 1;2 , Chad Sessions 2
• Automatic Partitioning for MultiAgent Reinforcement Ron Sun 1;2 , Todd Peterson 2
• A New Approach towards Modeling Causality in Commonsense Reasoning
• Skill Learning Using A BottomUp Hybrid Model Ron Sun, Edward Merrill, Todd Peterson
• Hybrid Systems and Connectionist Implementationalism Department of CECS
• Hybrid ConnectionistSymbolic Models: a report from the IJCAI'95 workshop on
• Supplementing Neural Reinforcement Learning with Symbolic Methods
• A BottomUp Model of Skill Learning Ron Sun (rsun@cs.ua.edu)
• A Neural Network Model of Causality Department of Computer Science
• A Cognitively Based Simulation of Academic Science Isaac Naveh
• Introduction to Sequence Learning CECS Department, University of Missouri
• Individual Action and Collective Function: from Sociology to Multi-Agent Learning
• Learning Plans without a priori Knowledge Ron Sun 1 , Chad Sessions 2
• A Microfeature Based Approach Towards Metaphor Interpretation Department of Computer Science
• The Importance of Cognitive Architectures: An Analysis Based on Cognitive Science Department
• On Variable Binding in Connectionist Department of Computer Science
• Arti cial Intelligence: Connectionist and Symbolic CECS, University of Missouri-Columbia, Columbia, MO 65211, USA
• A HYBRID AGENT ARCHITECTURE FOR REACTIVE
• Schemas, Logics, and Neural Assemblies Department of Computer Science
• Bidding in Reinforcement Learning: A Paradigm for MultiAgent Systems Ron Sun 1;2 , Chad Sessions 2
• MultiAgent Reinforcement Learning: Weighting and Partitioning
• Self-Segmentation of Sequences: Automatic Formation of Hierarchies of Sequential Behaviors
• Simulating organizational decision-making using a cognitively realistic agent model Isaac Naveh
• Skill Learning Using A BottomUp Hybrid Model Ron Sun, Edward Merrill, Todd Peterson
• Supplementing Neural Reinforcement Learning with Symbolic Methods: Possibilities and Challenges
• Journal of Cognitive Systems Research 1 (2001) 241--249 www.elsevier.com / locate / cogsys
• AN INTRODUCTION TO HYBRID CONNECTIONISTSYMBOLIC
• Accessibility versus Action-Centeredness in the Representation of Cognitive Skills
• Hierarchical Approaches to Understanding Consciousness L. Andrew Coward
• This article was originally published in a journal published by Elsevier, and the attached copy is provided by Elsevier for the
• Bottomup Skill Learning in Reactive Sequential Decision Tasks Ron Sun and Todd Peterson and Edward Merrill
• Learning, Action, and Consciousness: A Hybrid Approach toward Modeling Consciousness
• Symbol Grounding: A New Look At An Old Idea CECS Department
• Multi-Agent Reinforcement Learning with Bidding for Segmenting Action Sequences
• Philosophical Psychology Vol. 18, No. 5, October 2005, pp. 613637
• Intro to CLARION 1 An Introduction to CLARION
• Journal of Cognitive Systems Research 2 (2001) 5566 www.elsevier.com/locate/cogsys
• The Motivational and Metacognitive Control in August 28, 2005
• A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making
• A Subsymbolic+Symbolic Model for Learning Sequential Navigation Ron Sun, Todd Peterson
• Cognitive Science Meets Multi-Agent Systems: A Prolegomenon CECS Department, University of Missouri -Columbia
• Modeling Meta-Cognition in a Cognitive Architecture Cognitive Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
• Accessibility versus ActionCenteredness in the Representation of Cognitive Skills
• Computational Cognitive Modeling, the Source of Power and Other Related Issues
• Rationality Assumptions and Optimality of Co-learning
• Journal of Cognitive Systems Research 2 (2001) 13 www.elsevier.com/locate/cogsys
• Criteria for an effective theory of consciousness and some preliminary attempts
• Modeling meta-cognition in a cognitive architecture Action editor: Vasant Honavar
• Computational Models of Consciousness: An Evaluation CECS Department, University of Missouri -Columbia
• Extracting Plans from Reinforcement Learners Ron Sun 1;2 and Chad Sessions 2
• Learning to Plan Probabilistically From Neural Ron Sun, The University of Alabama, Tuscaloosa, AL
• A Hybrid Architecture for Learning Reactive Sequential Decision Ron Sun, Todd Peterson, Edward Merrill
• Partitioning in Multi-Agent Reinforcement Learning Ron Sun Todd Peterson
• An Agent Architecture for OnLine Learning of Procedural and Declarative Knowledge
• An RBF Network Alternative for a Hybrid Architecture
• Beyond Associative Memories: Logics and Variables in Connectionist Models
• [51] R. Sun, L. Bookman, and S. Shekhar, (eds.) The Working Notes of the AAAI Workshop on Integrating Neural and Symbolic Processes, American Association for Artificial Intelligence, Menlo Park, CA. 1992
• Beyond Simple Rule Extraction: Acquiring Planning Knowledge from
• Bibliography of Connectionist Models with Symbolic Processing This bibliog raphy was compiled by Ron Sun from contributions solicited from the research
• Computation, Reduction, and Teleology of Consciousness CECS Department, University of Missouri -Columbia
• Journal of Cognitive Systems Research 1 (1999) 13 www.elsevier.com/locate/cogsys
• Social Institution, Cognition, and Survival: A Cognitive-Social Simulation
• Introduction to Computational Cognitive Instead going straight into dealing with specific approaches, issues, and do-
• An Efficient Featurebased Connectionist Inheritance Scheme
• A Hybrid Model for Learning Sequential Navigation Ron Sun, Todd Peterson
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• Mixed Effects of Distractor Tasks on Incubation Sbastien Hlie (helies@rpi.edu)
• Motivational Representations within a Computational Cognitive Architecture
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• The Interaction of Explicit and Implicit Learning: An Integrated Model Paul Slusarz (kmicic@ideaworks.com)
• Hybrid Neural Systems Stefan Wermter