20 found
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  1.  39
    An Integrated Theory of the Mind.John R. Anderson, Daniel Bothell, Michael D. Byrne, Scott Douglass, Christian Lebiere & Yulin Qin - 2004 - Psychological Review 111 (4):1036-1060.
  2. The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments.Antonio Lieto, Christian Lebiere & Alessandro Oltramari - 2018 - Cognitive Systems Research:1-42.
    In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...)
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  3.  53
    Instance‐based learning in dynamic decision making.Cleotilde Gonzalez, Javier F. Lerch & Christian Lebiere - 2003 - Cognitive Science 27 (4):591-635.
    This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, (...)
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  4. The Newell test for a theory of cognition.John R. Anderson & Christian Lebiere - 2003 - Behavioral and Brain Sciences 26 (5):587-601.
    Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...)
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  5.  12
    Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Sarah Cooney, Milind Tambe & Christian Lebiere - 2020 - Topics in Cognitive Science 12 (3):992-1011.
    The purpose of cognitive models is to make predictive simulations of human behaviour, but this is often done at the aggregate level. Cranford, Gonzalez, Aggarwal, Cooney, Tambe, and Lebiere show that they can automatically customize a model to a particular individual on‐the‐fly, and use it to make specific predictions about their next actions, in the context of a particular cybersecurity game.
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  6.  22
    Conditional routing of information to the cortex: A model of the basal ganglia’s role in cognitive coordination.Andrea Stocco, Christian Lebiere & John R. Anderson - 2010 - Psychological Review 117 (2):541-574.
  7.  24
    Learning rapid and precise skills.John R. Anderson, Shawn Betts, Daniel Bothell, Ryan Hope & Christian Lebiere - 2019 - Psychological Review 126 (5):727-760.
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  8.  15
    Cognitive Models in Cybersecurity: Learning From Expert Analysts and Predicting Attacker Behavior.Vladislav D. Veksler, Norbou Buchler, Claire G. LaFleur, Michael S. Yu, Christian Lebiere & Cleotilde Gonzalez - 2020 - Frontiers in Psychology 11.
  9. Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind.Antonio Lieto, William G. Kennedy, Christian Lebiere, Oscar Romero, Niels Taatgen & Robert West - forthcoming - Procedia Computer Science.
    In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive arti cial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, (...)
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  10.  45
    Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.Norbou Buchler, Sean M. Fitzhugh, Laura R. Marusich, Diane M. Ungvarsky, Christian Lebiere & Cleotilde Gonzalez - 2016 - Frontiers in Psychology 7.
  11. Balancing long-term reinforcement and short-term inhibition.Christian Lebiere & Bradley J. Best - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  12. ACT-R: A higher-level account of processing capacity.John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder - 1998 - Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  13.  46
    Social Networks through the Prism of Cognition.Radosław Michalski, Boleslaw K. Szymanski, Przemysław Kazienko, Christian Lebiere, Omar Lizardo & Marcin Kulisiewicz - 2021 - Complexity 2021:1-13.
    Human relations are driven by social events—people interact, exchange information, share knowledge and emotions, and gather news from mass media. These events leave traces in human memory, the strength of which depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel cognition-driven social network model that accounts for cognitive aspects of social perception. The model explicitly represents each social interaction as a trace (...)
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  14.  41
    Optimism for the future of unified theories.John R. Anderson & Christian Lebiere - 2003 - Behavioral and Brain Sciences 26 (5):628-633.
    The commentaries on our article encourage us to believe that researchers are beginning to take seriously the goal of achieving the broad adequacy that Newell aspired to. The commentators offer useful elaborations to the criteria we suggested for the Newell Test. We agree with many of the commentators that classical connectionism is too restrictive to achieve this broad adequacy, and that other connectionist approaches are not so limited and can deal with the symbolic components of thought. All these approaches, including (...)
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  15.  49
    The b-I-c-a of biologically inspired cognitive architectures.Andrea Stocco, Christian Lebiere & Alexei V. Samsonovich - 2010 - International Journal of Machine Consciousness 2 (2):171-192.
    Recent years have seen a gradual convergence of seemingly distant research fields over a single goal: understanding and replicating biological intelligence in artifacts. This work presents a general overview on the origin, the state-of-the-art, scientific challenges and the future of Biologically Inspired Cognitive Architecture (BICA) research. Our perspective decomposes the field into the four principal semantic components associated with the BICA challenge that together call for an integration of efforts of researchers across disciplines. Areas and directions of study where new (...)
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  16.  13
    Towards a Cognitive Theory of Cyber Deception.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Milind Tambe, Sarah Cooney & Christian Lebiere - 2021 - Cognitive Science 45 (7):e13013.
    This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present‐day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive (...)
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  17.  12
    Cognitive architectures combine formal and heuristic approaches.Cleotilde Gonzalez & Christian Lebiere - 2013 - Behavioral and Brain Sciences 36 (3):285 - 286.
    Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.
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  18. Error modeling in the ACT-R production system.Christian Lebière, John R. Anderson & Lynne M. Reder - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 555--559.
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  19.  44
    Implicit and explicit learning in a hybrid architecture of cognition.Christian Lebiere & Dieter Wallach - 1999 - Behavioral and Brain Sciences 22 (5):772-773.
    We present a theoretical account of implicit and explicit learning in terms of ACT-R, an integrated architecture of human cognition as a computational supplement to Dienes & Perner's conceptual analysis of knowledge. Explicit learning is explained in ACT-R by the acquisition of new symbolic knowledge, whereas implicit learning amounts to statistically adjusting subsymbolic quantities associated with that knowledge. We discuss the common foundation of a set of models that are able to explain data gathered in several signature paradigms of implicit (...)
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  20.  17
    Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture.Konstantinos Mitsopoulos, Sterling Somers, Joel Schooler, Christian Lebiere, Peter Pirolli & Robert Thomson - 2022 - Topics in Cognitive Science 14 (4):756-779.
    We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well (...)
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