Results for 'Cognitive Architectures'

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  1. 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 (...)
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  2. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and (...)
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  3.  17
    Cognitive Architectures.Paul Thagard - 2012 - In Keith Frankish & William Ramsey (eds.), The Cambridge Handbook of Cognitive Science. Cambridge University Press. pp. 50--70.
  4.  61
    Cognitive Architectures as Lakatosian Research Programs: Two Case Studies.Richard P. Cooper - 2006 - Philosophical Psychology 19 (2):199-220.
    Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is (...)
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  5. Augmenting Cognitive Architectures to Support Diagrammatic Imagination.Balakrishnan Chandrasekaran, Bonny Banerjee, Unmesh Kurup & Omkar Lele - 2011 - Topics in Cognitive Science 3 (4):760-777.
    Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes (...)
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  6. Desiderata for Cognitive Architectures.Ron Sun - 2004 - Philosophical Psychology 17 (3):341-373.
    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 (...)
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  7. The Importance of Cognitive Architectures: An Analysis Based on CLARION.Ron Sun - unknown
    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 (...)
     
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  8.  10
    Cognitive Architectures Have Limited Explanatory Power.Prasad Tadepalli - 2003 - Behavioral and Brain Sciences 26 (5):622-623.
    Cognitive architectures, like programming languages, make commitments only at the implementation level and have limited explanatory power. Their universality implies that it is hard, if not impossible, to justify them in detail from finite quantities of data. It is more fruitful to focus on particular tasks such as language understanding and propose testable theories at the computational and algorithmic levels.
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  9. Beyond Subgoaling: A Dynamic Knowledge Generation Framework for Creative Problem Solving in Cognitive Architectures.Antonio Lieto - 2019 - Cognitive Systems Research 58:305-316.
    In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism (...)
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  10.  20
    Cognitive Architectures Need Compliancy, Not Universality.Richard M. Young - 2003 - Behavioral and Brain Sciences 26 (5):628-628.
    The criterion of computational universality for an architecture should be replaced by the notion of compliancy, where a model built within an architecture is compliant to the extent that the model allows the architecture to determine the processing. The test should be that the architecture does easily – that is, enables a compliant model to do – what people do easily.
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  11.  27
    A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models.Andrea Stocco - 2018 - Cognitive Science 42 (2):457-490.
    Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's (...)
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  12. Constraints in Cognitive Architectures.N. A. Taatgen & J. R. Anderson - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 170--185.
  13.  69
    The Past, Present, and Future of Cognitive Architectures.Niels Taatgen & John R. Anderson - 2010 - Topics in Cognitive Science 2 (4):693-704.
    Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive (...)
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  14. Levels of Explanation and Cognitive Architectures* By.Robert N. McCauley - unknown
    Some controversies in cognitive science, such as arguments about whether classical or distributed connectionist architectures best model the human cognitive system, reenact long-standing debates in the philosophy of science. For millennia philosophers have pondered whether mentality can submit to scientific explanation generally and to physical explanation particularly. Recently, positive answers have gained popularity. The question remains, though, as to the analytical level at which mentality is best explained. Is there a level of analysis that is peculiarly appropriate (...)
     
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  15. Cognitive Architectures and Multi-Agent Social Simulation.Ron Sun - unknown
    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 (...)
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  16.  70
    Criteria for the Design and Evaluation of Cognitive Architectures.Sashank Varma - 2011 - Cognitive Science 35 (7):1329-1351.
    Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition (...)
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  17.  70
    Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation.Richard P. Cooper & David Peebles - 2015 - Topics in Cognitive Science 7 (2):243-258.
    We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system (...)
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  18.  9
    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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  19. Some Epistemological Problems with the Knowledge Level in Cognitive Architectures.Antonio Lieto - 2015 - In Proceedings of AISC 2015, 12th Italian Conference on Cognitive Science, Genoa, 10-12 December 2015, Italy. NeaScience.
    This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it according to the needs emerging in different situational scenarios, is (...)
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  20. Cognitive Architectures in Artificial Intelligence: The Evolution of Research Programs.Andy Clark (ed.) - 1998 - Routledge.
    First published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.
     
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  21.  21
    Extending Cognitive Architectures with Mental Imagery.Scott D. Lathrop & John E. Laird - 2009 - In B. Goertzel, P. Hitzler & M. Hutter (eds.), Proceedings of the Second Conference on Artificial General Intelligence. Atlantis Press.
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  22.  69
    Maps, Languages, and Manguages: Rival Cognitive Architectures?Kent Johnson - 2015 - Philosophical Psychology 28 (6):815-836.
    Provided we agree about the thing, it is needless to dispute about the terms. —David Hume, A treatise of human nature, Book 1, section VIIMap-like representations are frequently invoked as an alternative type of representational vehicle to a language of thought. This view presupposes that map-systems and languages form legitimate natural kinds of cognitive representational systems. I argue that they do not, because the collections of features that might be taken as characteristic of maps or languages do not themselves (...)
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  23.  13
    Representational Redescription and Cognitive Architectures.Antonella Carassa & Maurizio Tirassa - 1994 - Behavioral and Brain Sciences 17 (4):711-712.
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  24. Cognitive Social Simulation Incorporating Cognitive Architectures.Ron Sun - unknown
    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 (...)
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  25.  19
    The Role of Falsification in the Development of Cognitive Architectures: Insights From a Lakatosian Analysis.Richard P. Cooper - 2007 - Cognitive Science 31 (3):509-533.
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  26.  44
    Processes Models, Environmental Analyses, and Cognitive Architectures: Quo Vadis Quantum Probability Theory?Julian N. Marewski & Ulrich Hoffrage - 2013 - Behavioral and Brain Sciences 36 (3):297 - 298.
    A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.
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  27. Design for Cognitive Architectures.R. Sun - 2004 - Philosophical Psychology 17 (3).
  28.  17
    A Conceptual Linkage Between Cognitive Architectures and Social Interaction.Kees Zoethout & Wander Jager - 2009 - Semiotica 2009 (175):317-333.
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    Consciosusness in Cognitive Architectures. A Principled Analysis of Rcs, Soar and Act-R.Carlos Hernandez, Ricardo Sanz & Ignacio Lopez - 2008
    This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness. To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST). A general qualitative (...)
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  30.  12
    A 3rd Person Knowledge Level Analysis of Cognitive Architectures: Problems, Challenges, and Future Directions.Antonio Lieto - 2021 - Unipa Invited Seminars.
    A 3rd person Knowledge Level analysis of cognitive architectures -/- Abstract I provide a knowledge level analysis of the main representational and reasoning problems affecting the cognitive architectures for what concerns this issue. In providing this analysis I will show, by considering some of the main cognitive architectures currently available (e.g. SOAR, ACT-R, CLARION), how one of the main problems of such architectures is represented by the fact that their knowledge representation and processing (...)
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  31.  65
    Representational Redescription and Cognitive Architectures.Antonella Carassa & Maurizio Tirassa - 1994 - Carassa, Antonella and Tirassa, Maurizio (1994) Representational Redescription and Cognitive Architectures. [Journal (Paginated)] 17 (4):711-712.
    We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.
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  32.  11
    Implicit Learning and Cognitive Architectures.R. S. Lockhart - 1994 - Journal of Intelligent Systems 4 (3-4):329-344.
  33.  17
    Embodied Anticipation in Neurocomputational Cognitive Architectures for Robotic Agents.Alberto Montebelli, Robert Lowe & Tom Ziemke - forthcoming - The Swedish Ai Society Workshop May 27-28, 2009 Ida, Linköping University.
  34.  12
    Functional and Structural Models of Commonsense Reasoning in Cognitive Architectures.Antonio Lieto - 2021 - VISCA 2021 - 2nd Virtual International Symposium on Cognitive Architecture.
    I will present two different applications - Dual PECCS and the TCL reasoning framework - addressing some crucial aspects of commonsense reasoning (namely: dealing with typicality effects and with the problem of commonsense compositionality) in a way that is integrated or compliant with different cognitive architectures. In doing so I will show how such aspects are better dealt with at different levels of representation and will discuss the adopted solution to integrate such representational layers.
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  35.  17
    Meeting Newell's Other Challenge: Cognitive Architectures as the Basis for Cognitive Engineering.Wayne D. Gray, Michael J. Schoelles & Christopher W. Myers - 2003 - Behavioral and Brain Sciences 26 (5):609-610.
    We use the Newell Test as a basis for evaluating ACT-R as an effective architecture for cognitive engineering. Of the 12 functional criteria discussed by Anderson & Lebiere (A&L), we discuss the strengths and weaknesses of ACT-R on the six that we postulate are the most relevant to cognitive engineering.
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  36. A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes.Antonio Lieto - 2014 - Proceedings of 5th International Conference on Biologically Inspired Cognitive Architectures, Boston, MIT, Pocedia Computer Science, Elsevier:1-9.
    In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in (...) sciences by providing unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)
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  37.  7
    The Challenges of Building Computational Cognitive Architectures.Ron Sun - 2007 - In Wlodzislaw Duch & Jacek Mandziuk (eds.), Challenges for Computational Intelligence. Springer. pp. 37--60.
  38.  29
    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.
  39.  16
    Processes Models, Environmental Analyses, and Cognitive Architectures: Quo Vadis Quantum Probability Theory?—ERRATUM.Julian N. Marewski & Ulrich Hoffrage - 2013 - Behavioral and Brain Sciences 36 (4):463-463.
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    On Theory Integration: Toward Developing Affective Components Within Cognitive Architectures.Justin M. Olds & Julian N. Marewski - 2015 - Behavioral and Brain Sciences 38.
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  41. Modular Architectures and Informational Encapsulation: A Dilemma.Dustin Stokes & Vincent Bergeron - 2015 - European Journal for Philosophy of Science 5 (3):315-38.
    Amongst philosophers and cognitive scientists, modularity remains a popular choice for an architecture of the human mind, primarily because of the supposed explanatory value of this approach. Modular architectures can vary both with respect to the strength of the notion of modularity and the scope of the modularity of mind. We propose a dilemma for modular architectures, no matter how these architectures vary along these two dimensions. First, if a modular architecture commits to the informational encapsulation (...)
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  42. Cognitive Robot Architectures: Proceedings of EUCognition 2016.Ron Chrisley, Vincent C. Müller, Yulia Sandamirskaya & Markus Vincze (eds.) - 2017 - Hamburg: CEUR-WS.
    The European Association for Cognitive Systems is the association resulting from the EUCog network, which has been active since 2006. It has ca. 1000 members and is currently chaired by Vincent C. Müller. We ran our annual conference on December 08-09 2016, kindly hosted by the Technical University of Vienna with Markus Vincze as local chair. The invited speakers were David Vernon and Paul F.M.J. Verschure. Out of the 49 submissions for the meeting, we accepted 18 a papers and (...)
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  43.  44
    Parallel Architectures and Mental Computation.Andrew Wells - 1993 - British Journal for the Philosophy of Science 44 (3):531-542.
    In a recent paper, Lyngzeidetson [1990] has claimed that a type of parallel computer called the ‘Connection Machine’ instantiates architectural principles which will ‘revolutionize which "functions" of the human mind can and cannot be modelled by (non-human) computational automata.’ In particular, he claims that the Connection Machine architecture shows the anti-mechanist argument from Gödel's theorem to be false for at least one kind of parallel computer. In the first part of this paper, I argue that Lyngzeidetson's claims are not supported (...)
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  44.  14
    Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.Elisabeth A. Karuza, Sharon L. Thompson-Schill & Danielle S. Bassett - 2016 - Trends in Cognitive Sciences 20 (8):629-640.
  45.  25
    Self-Awareness in Real-Time Cognitive Control Architectures.Ricardo Sanz, Ignacio López & Carlos Hernández - 2007 - In Anthony Chella & Ricardo Manzotti (eds.), Ai and Consciousness: Theoretical Foundations and Current Approaches. Aaai Press, Merlo Park, Ca.
  46. The Algebraic Mind: Integrating Connectionism and Cognitive Science.Gary Marcus - 2001 - MIT Press.
    1 Cognitive Architectures 2 Multilayer Perceptrons 3 Relations between Variables 4 Structured Representations 5 Individuals 6 Where does the Machinery of Symbol Manipulation Come From? 7 Conclusions.
     
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  47. Dual PECCS: A Cognitive System for Conceptual Representation and Categorization.Antonio Lieto, Daniele Radicioni & Valentina Rho - 2017 - Journal of Experimental and Theoretical Artificial Intelligence 29 (2):433-452.
    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual (...)
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  48.  33
    Cognitive Agents with Commonsense.Antonio Lieto - 2021 - I-Cog Talks.
    Commonsense reasoning is a crucial human ability employed in everyday tasks. In this talk I provide a knowledge level analysis of the main representational and reasoning problems affecting the cognitive architectures for what concerns this issue. In providing this analysis I will show, by considering some of the main cognitive architectures currently available (e.g. SOAR, ACT-R, CLARION), how one of the main problems of such architectures is represented by the fact that their knowledge representation and (...)
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  49. Neuro-Cognitive Systems Involved in Morality.James Blair, A. A. Marsh, E. Finger, K. S. Blair & J. Luo - 2006 - Philosophical Explorations 9 (1):13 – 27.
    In this paper, we will consider the neuro-cognitive systems involved in mediating morality. Five main claims will be made. First, that there are multiple, partially separable neuro-cognitive architectures that mediate specific aspects of morality: social convention, care-based morality, disgust-based morality and fairness/justice. Second, that all aspects of morality, including social convention, involve affect. Third, that the neural system particularly important for social convention, given its role in mediating anger and responding to angry expressions, is ventrolateral prefrontal cortex. (...)
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  50.  3
    Connectionist Architectures: Optimization.Marcus Frean - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
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