Search results for 'Ron Sun Todd Peterson' (try it on Scholar)

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  1. Ron Sun & Todd Peterson, EMAIL: Rsun@Cs.Ua.Edu.score: 5100.0
    In developing autonomous agents, one usually emphasizes only (situated) procedural knowledge, ignoring more explicit declarative knowledge. On the other hand, in developing symbolic reasoning models, one usually emphasizes only declarative knowledge, ignoring procedural knowledge. In contrast, we have developed a learning model Clarion, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in Sun (1995). learns and utilizes both procedural and declarative knowledge, tapping into the synergy of..
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  2. Ron Sun, Todd Peterson & Edward Merrill, A Bottom-Up Model of Skill Learning.score: 4800.0
    We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLAR- ION is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and (...)
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  3. Ron Sun, Todd Peterson & Edward Merrill, Bottom-Up Skill Learning in Reactive Sequential Decision Tasks.score: 4800.0
    This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent’s experience interacting with the world. Computational experiments with the model in two domains are reported.
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  4. Ron Sun & Todd Peterson, Multi-Agent Reinforcement Learning: Weighting and Partitioning.score: 4800.0
    This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in reinforcement learning (...)
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  5. Todd Peterson, Ron Sun & Edward Merrill, Tuscaloosa, AL 35487.score: 4800.0
    This paper introduces a hybrid model that combines connectionist, symbolic, and reinforcement learning for tackling reactive sequential decision tasks by a situated agent. Both procedural skills and high-level symbolic representations are acquired through an agent's experience interacting with the world, in a bottom-up direction. It deals with on-line learning, that is, learning continuously from on-going experience in the world, without the use of preconstructed data sets or preconceived concepts. The model is a connectionist one based on a two-level approach proposed (...)
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  6. Ron Sun & Todd Peterson, Some Experiments with a Hybrid Model for Learning Sequential Decision Making.score: 4800.0
    To deal with reactive sequential decision tasks we present a learning model which is a hybrid connectionist model consisting of both localist and distributed representations based on the two level approach proposed in..
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  7. Ron Sun Todd Peterson, A Subsymbolic Symbolic Model for Learning Sequential Navigation.score: 532.5
    To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on (...)
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  8. Robert Mathews & Ron Sun, The Symposium on the Synergy Between Implicit and Explicit Learning Processes.score: 150.0
    Implicit processes are thought to be relatively fast, inaccessible, holistic, and imprecise, while explicit processes are slow, accessible and precise (e.g., Reber, 1989, Sun 2002). This dichotomy is closely related to some other well-known dichotomies including symbolic versus subsymbolic processing (Rumelhart et al., 1986), conceptual versus subconceptual processing (Smolensky, 1988), and conscious versus unconscious processing (Jacoby et al., 1994). This dichotomy has been justified by extensive studies of implicit and explicit learning, implicit and explicit memory, and implicit versus explicit metacognition (...)
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  9. Ron Sun, Cognitive Architectures and Multi-Agent Social Simulation.score: 150.0
    As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application to social simulation will be sketched.
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  10. Ron Sun, The Symposium on the Synergy Between Implicit and Explicit Learning Processes.score: 150.0
    Implicit processes are thought to be relatively fast, inaccessible, holistic, and imprecise, while explicit processes are slow, accessible and precise (e.g., Reber, 1989, Sun 2002). This dichotomy is closely related to some other wellknown dichotomies including symbolic versus subsymbolic processing (Rumelhart et al., 1986), conceptual versus subconceptual processing (Smolensky, 1988), and conscious versus unconscious processing (Jacoby et al., 1994). This dichotomy has been justified by extensive studies of implicit and explicit learning, implicit and explicit memory, and implicit versus explicit metacognition (...)
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  11. Todd Peterson, Autonomous Learning of Sequential Tasks: Experiments and Analyses.score: 150.0
    This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...)
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  12. Todd Peterson, Some Experiments with a Hybrid Model for Learning Sequential Decision Making.score: 150.0
    To deal with sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and distributed represen tations based on the two level approach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on the working (...)
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  13. Ron Sun, The Interaction of Explicit and Implicit Learning: An Integrated Model.score: 120.0
    This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, contrary to the common tendency in the literature of studying each type of learning in isolation. It highlights the interaction between the two types of processes and its various effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learning implicit knowledge and then (...)
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  14. Ron Sun (2000). Symbol Grounding: A New Look at an Old Idea. Philosophical Psychology 13 (2):149-172.score: 120.0
    Symbols should be grounded, as has been argued before. But we insist that they should be grounded not only in subsymbolic activities, but also in the interaction between the agent and the world. The point is that concepts are not formed in isolation (from the world), in abstraction, or "objectively." They are formed in relation to the experience of agents, through their perceptual/motor apparatuses, in their world and linked to their goals and actions. This paper takes a detailed look at (...)
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  15. Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.score: 120.0
    The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high (...)
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  16. Ron Sun, Top-Down Versus Bottom-Up Learning in Cognitive Skill Acquisition.score: 120.0
    This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...)
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  17. Ron Sun, Incubation, Insight, and Creative Problem Solving: A Unified Theory and a Connectionist Model.score: 120.0
    This article proposes a unified framework for understanding creative problem solving, namely, the explicit–implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit–implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most (...)
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  18. Ron Sun, The Importance of Cognitive Architectures: An Analysis Based on CLARION.score: 120.0
    Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domaingeneric computational cognitive model that may be used for a broad, multiple-level, multipledomain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will (...)
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  19. Ron Sun & Robert C. Mathews (2012). Implicit Cognition, Emotion, and Meta-Cognitive Control. Mind and Society 11 (1):107-119.score: 120.0
    The goal of this research is to understand the interaction of implicit and explicit psychological processes in dealing with emotional distractions and meta-cognitive control of such distractions. The questions are how emotional and meta-cognitive processes can be separated into implicit and explicit components, and how such a separation can be utilized to improve self-regulation of emotion, which can have significant theoretical and practical implications.
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  20. L. Andrew Coward & Ron Sun (2004). Criteria for an Effective Theory of Consciousness and Some Preliminary Attempts. Consciousness and Cognition 13 (2):268-301.score: 120.0
    In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory (...)
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  21. Ron Sun (1999). Accounting for the Computational Basis of Consciousness: A Connectionist Approach. Consciousness and Cognition 8 (4):529-565.score: 120.0
    This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the (...)
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  22. Ron Sun (2004). Desiderata for Cognitive Architectures. Philosophical Psychology 17 (3):341-373.score: 120.0
    This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking (...)
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  23. Ron Sun, Knowledge Integration in Creative Problem Solving.score: 120.0
    Most psychological theories of problem solving have focused on modeling explicit processes that gradually bring the solver closer to the solution in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. In the present paper, we propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative problem solving that involves integrating (...)
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  24. Ron Sun & Xi Zhang, Accessibility Versus Action-Centeredness in the Representation of Cognitive Skills.score: 120.0
    We believe that the distinction between procedural and declarative knowledge unnecessarily confounds two issues: action-centeredness and accessibility, and can be made clearer through separating the two aspects. The work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both action-centered and non-action-centered knowledge. We examine and simulate human data in the Letter Counting task. The work shows how the data may be captured using either the action-centered knowledge alone or the combined action-centered (...)
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  25. Ron Sun, Xi Zhang & Robert Mathews, Modeling Meta-Cognition in a Cognitive Architecture.score: 120.0
    This paper describes how meta-cognitive processes (i.e., the self monitoring and regulating of cognitive processes) may be captured within a cognitive architecture Clarion. Some currently popular cognitive architectures lack sufficiently complex built-in meta-cognitive mechanisms. However, a sufficiently complex meta-cognitive mechanism is important, in that it is an essential part of cognition and without it, human cognition may not function properly. We contend that such a meta-cognitive mechanism should be an integral part of a cognitive architecture. Thus such a mechanism has (...)
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  26. Ron Sun, R. Mathews & and S. Lane, Implicit and Explicit Processes in the Development of Cognitive Skills: A Theoretical Interpretation with Some Practical Implications for Science Education.score: 120.0
    In: E. Vargios (ed.), Educational Psychology Research Focus, pp.1-26. Nova Science Publishers, Hauppauge, NY. 2007.
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  27. Ron Sun (2008). Introduction to Computational Cognitive Modeling. In , The Cambridge Handbook of Computational Psychology. Cambridge University Press. 3--19.score: 120.0
  28. Ron Sun (2013). Autonomous Generation of Symbolic Representations Through Subsymbolic Activities. Philosophical Psychology 26 (6):888 - 912.score: 120.0
    This paper explores an approach for autonomous generation of symbolic representations from an agent's subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of (...)
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  29. Ron Sun (2001). Computation, Reduction, and Teleology of Consciousness. Cognitive Systems Research 1 (1):241-249.score: 120.0
    This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the (...)
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  30. Ron Sun, Cognitive Social Simulation Incorporating Cognitive Architectures.score: 120.0
    Agent-based social simulation (with multi-agent systems), which is an important aspect of social computing, can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents and therefore their social interactions. A cognitive architecture is a domain-generic computational cognitive model that may be used for a broad multiple-domain analysis of individual behavior. In this article, an example of a cognitive architecture is given, and its applications to social simulation described. Some challenging issues in this regard are (...)
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  31. Ron Sun (1997). Learning, Action, and Consciousness: A Hybrid Approach Toward Modeling Consciousness. Neural Networks 10:1317-33.score: 120.0
    _role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...)
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  32. Ron Sun (2004). Criteria for an Effective Theory of Consciousness and Some Preliminary Attempts. Consciousness and Cognition 13 (2):268-301.score: 120.0
    In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory (...)
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  33. Ron Sun, Introduction to Sequence Learning.score: 120.0
    Sequential behavior is essential to intelligence, and it is a fundamental part of human activities ranging from reasoning to language, and from everyday skills to complex problem solving. In particular, sequence learning is an important component of learning in many task domains — planning, reasoning, robotics, natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. Naturally, there are many different approaches towards sequence learning, resulting from different perspectives taken in different task domains. (...)
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  34. Ron Sun (2005). The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach. Psychological Review 112:159-192.score: 120.0
    This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...)
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  35. Ron Sun & Gregg C. Oden, Integration of Cognitive Systems Across Disciplinary Boundaries.score: 120.0
    The present issue is the beginning of a new journal from various sub-disciplines and paradigms in order – Cognitive Systems Research – which we have to construct a coherent picture of how the various developed in response to what we perceive to be an pieces fit together overall. Such a synthesis is unfilled niche in the current literature in the areas of essential to the discovery of designs for general Cognitive Science and Artificial Intelligence.
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  36. Ron Sun & Isaac Naveh (2007). Social Institution, Cognition, and Survival: A Cognitive–Social Simulation. Mind and Society 6 (2):115-142.score: 120.0
    Although computational models of cognitive agents that incorporate a wide range of cognitive functionalities have been developed in cognitive science, most of the work in social simulation still assumes rudimentary cognition on the part of the agents. In contrast, in this work, the interaction of cognition and social structures/processes is explored, through simulating survival strategies of tribal societies. The results of the simulation demonstrate interactions between cognitive and social factors. For example, we show that cognitive capabilities and tendencies may be (...)
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  37. Ron Sun (ed.) (2008). The Cambridge Handbook of Computational Psychology. Cambridge University Press.score: 120.0
    This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Computational models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many (...)
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  38. Ron Sun & Isaac Naveh, A Cognitively Based Simulation of Simple Organizations.score: 120.0
    This paper explores cognitively realistic social simulations by deploying the CLARION cognitive architecture in a simple organizational simulation, which involves the interaction of multiple cognitive agents. It argues for an integration of the two separate strands of research: cognitive modeling and social simulation. Such an integration could, on the one hand, enhance the accuracy of social simulation models by taking into full account the effects of individual cognitive factors, and on the other hand, it could lead to greater explanatory, predictive, (...)
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  39. Ron Sun, Connectionist Inference Models.score: 120.0
    The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule- based reasoning and whether they involve distributed or localist representations. The bene®ts and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used (...)
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  40. Ron Sun (2001). Cognitive Science Meets Multi-Agent Systems: A Prolegomenon. Philosophical Psychology 14 (1):5 – 28.score: 120.0
    In the current research on multi-agent systems (MAS), many theoretical issues related to sociocultural processes have been touched upon. These issues are in fact intellectually profound and should prove to be significant for MAS. Moreover, these issues should have equally significant impact on cognitive science, if we ever try to understand cognition in the broad context of sociocultural environments in which cognitive agents exist. Furthermore, cognitive models as studied in cognitive science can help us in a substantial way to better (...)
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  41. Ron Sun, Hybrid Connectionist -Symbolic Mo Dels.score: 120.0
    During the two days of the workshop, various presentations and discussions brought to light many new ideas, controv ersies, and syntheses. The fo cus was on learning and architecture s that feature hybrid representations and supp ort hybrid learning. It was a general consensus among the workshop participants that hybrid connectionist-symb olic mo dels constitute a promising aven ue toward developing more robust, more p owerful, and more versatile architecture s b oth for cognitive mo deling and for intelligen t (...)
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  42. Ron Sun, Intelligence.score: 120.0
    The paper attempts to account for common pattems in commonsense reasoning through integrating rule-based reasoning and similarity-based reasoning as embodied in connectionist models.
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  43. Ron Sun, Integrating Reinforcement Learning, Bidding and Genetic Algorithms.score: 120.0
    This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by the Q module. While the (...)
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  44. Ron Sun, Supplementing Neural Reinforcement Learning with Symbolic Methods Possibilities and Challenges.score: 120.0
    methods to improve reinforcement learning are identi ed and discussed in some detail Each demonstrates to some extent the advantages of combining RL and symbolic meth ods These methods point to the potentials and the chal lenges of this line of research..
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  45. Phil Peterson & Noam Chomsky (1995). Correspondance Peterson-Chomsky (29 juin - 8 novembre 1993). Revue Philosophique de la France Et de L'Étranger 185 (1):83 - 96.score: 120.0
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  46. Ron Sun, Beyond Simple Rule Extraction: The Extraction of Planning Knowledge From Reinforcement Learners.score: 120.0
    Abstra,ct— This paper will discuss learning in hybrid models that goes beyond simple rule extraction from backpropagation networks. Although simple rule extraction has received a lot of research attention, to further develop hybrid learning models that include both symbolic and subsymbolic knowledge and that learn autonomously, it is necessary to study autonomous learning of both subsymbolic and symbolic knowledge in integrated architectures. This paper will describe knowledge extraction from neural reinforcement learning. It includes two approaches towards extracting plan knowledge: the (...)
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  47. Ron Sun (2003). Conceptions and Misconceptions of Connectionism. Behavioral and Brain Sciences 26 (5):621-621.score: 120.0
    This commentary examines one aspect of the target article – the comparison of ACT-R with connectionist models. It argues that conceptions of connectionist models should be broadened to cover the whole spectrum of work in this area, especially the so-called hybrid models. Doing so may change drastically ratings of connectionist models, and consequently shed more light on the developing field of cognitive architectures.
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  48. Ron Sun, Knowledge Extraction From Reinforcement Learning.score: 120.0
    traction from reinforcement learners It addresses two ap proaches towards knowledge extraction the extraction of ex plicit symbolic rules from neural reinforcement learners and the extraction of complete plans from such learners The advantages of such knowledge extraction include the improvement of learning especially with the rule extraction approach and the improvement of the usability of re sults of learning..
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  49. Ron Sun, Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model.score: 120.0
    Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance (...)
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  50. Isaac Naveh & Ron Sun, A Cognitively Based Simulation of Academic Science.score: 120.0
    The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous (...)
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