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4 - Symbolic and Hybrid Models of Cognition

from Part II - Cognitive Modeling Paradigms

Published online by Cambridge University Press:  21 April 2023

By
Tarek R. Besold  and
Kai-Uwe Kühnberger
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

For decades, symbolic models of cognition were the dominant computational approaches of cognition. Today they coexist with subsymbolic, statistical, and hybrid models, but they are still the de facto standard for modeling human reasoning processes. This chapter summarizes important aspects of symbolic and hybrid models of cognition, approaching the topic from different perspectives. After some discussion on historical aspects and the theoretical basis of symbolic models of cognition, cognitive architectures as models for intelligent agents are examined. Subsequently, the role of symbolic computational approaches towards processing natural language, representation of human knowledge, and commonsense reasoning are considered. Then the focus is put on the crucial question of learning new representations and theories, before finally looking at hybrid and neural-symbolic systems combining reasoning and learning and bridging between symbolic and subsymbolic elements.

Keywords

knowledge representationknowledge-based systemssymbolic learningnatural language processingcommon sense reasoningcognitive architectureshybrid architecturesneural-symbolic systems
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 139 - 172
Publisher: Cambridge University Press
Print publication year: 2023

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References

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1), 39–52.Google Scholar
Agre, P., & Chapman, D. (1990). What are plans for? In Maes, P. (Ed.), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. Cambridge, MA: MIT Press.Google Scholar
Anderson, J., & Lebiere, C. (1998). The Atomic Concepts of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Aristotle, (1989). Prior Analytics, translated by Robin Smith. Indianapolis, IN: Hackett.Google Scholar
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.Google Scholar
Avni, A., Bar-Eli, M., & Tenenbaum, G. (1990). Assessment and calculation in top chess players’ decision-making during competition: a theoretical model. Psychological Reports, 67(3), 899–906.Google Scholar
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. (2003). The Description Logic Handbook: Theory, Implementation, Applications. Cambridge: Cambridge University Press.Google Scholar
Baader, F., Horrocks, I., & Sattler, U. (2007). Description logics. In Van Harmelen, F., Lifschitz, V., & Porter, B. (Eds.), Handbook of Knowledge Representation. Abingdon: Elsevier.Google Scholar
Baader, F., & Nipkow, T. (1998). Term Rewriting and All That. Cambridge: Cambridge University Press.Google Scholar
Baader, F., & Nutt, W. (2003). Basic description logic. In Baader, F. et al. (Eds.), The Handbook of Description Logic: Theory, Implementation, and Applications. Cambridge: Cambridge University Press.Google Scholar
Bach, J. (2009). Principles of Synthetic Intelligence. An Architecture for Motivated Cognition. New York, NY: Oxford University Press.CrossRefGoogle Scholar
Baker, C., Saxe, R., & Tenenbaum, J. (2011). Bayesian theory of mind: modeling joint belief-desire attribution. In Proceedings of the Thirty-third Annual Meeting of the Cognitive Science Society, Boston, MA.Google Scholar
Bechtel, W., Abrahamsen, A., & Graham, G. (2001). Cognitive science: history. In Smelser, N. & Baltes, P. (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 2154–2158). Abingdon: Elsevier.Google Scholar
Berners-Lee, T., Hendler, J., & Lassila, Ora (2001). The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, 284(5), 34–43.CrossRefGoogle Scholar
Berov, L. (2017). Steering plot through personality and affect: an extended BDI model of fictional characters. In Kern-Isberner, G., Fürnkranz, J., & Thimm, M. (Eds.), KI 2017. Lecture Notes in Computer Science, Volume 10505. London: Springer. https://doi.org/10.1007/978-3-319-67190-1_23Google Scholar
Besold, T., Garcez, A., Bader, S., et al. (2022). Neural-symbolic learning and reasoning: a survey and interpretation. In Hitzler, P. & Sarker, K. (Eds.), Neuro-Symbolic Artificial Intelligence: The State of the Art. Amsterdam: IOS Press.Google Scholar
Besold, T., Kühnberger, K.-U., & Plaza, E. (2017). Towards a computational and algorithmic-level account of concept blending using analogies and amalgams. Connection Science, 29(4), 387–413. https://doi.org/10.1080/09540091.2017.1326463CrossRefGoogle Scholar
Bibel, W. (1993). Wissensrepräsentation und Inferenz: Eine grundlegende Einführung. Braunschweig, Wiesbaden: Vieweg Verlagsgesellschaft.Google Scholar
Bordini, R., Hubner, J., & Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak Using Jason. Oxford: John Wiley & Sons.Google Scholar
Brachman, R., & Schmolze, J. (1985). An overview of the KL-ONE Knowledge Representation System. Cognitive Science, 9(2), 171–216.Google Scholar
Bratko, I. (2012). Prolog Programming for Artificial Intelligence (4th ed.). Harlow: Addison-Wesley.Google Scholar
Bredeweg, B., & Struss, P. (2004). Current topics in qualitative reasoning. AI Magazine, 24(4).Google Scholar
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.Google Scholar
Brooks, R. (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, MA: MIT Press.Google Scholar
Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. ArXiv200514165 CsGoogle Scholar
Byrne, J. (2020). Learning and memory. In Neuroscience Online, the Open-Access Neuroscience Electronic Textbook. Available from: https://nba.uth.tmc.edu/neuroscience/m/index.htm [last accessed June 8, 2022].Google Scholar
Chater, N., Oaksford, M., Hahn, U., & Heit, E. (2010). Bayesian models of cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 1(6), 811–823.Google Scholar
Chomsky, N. (1957). Syntactic Structures. The Hague/Paris: Mouton.Google Scholar
Chomsky, N. (1980a). On cognitive structures and their development: a reply to Piaget. In Piattelli-Palmarini, M. (Ed.), Language and Learning: The Debate between Jean Piaget and Noam Chomsky. Cambridge, MA: Harvard University Press.Google Scholar
Chomsky, N. (1980b). Rules and Representations. New York, NY: Blackwell.Google Scholar
Chomsky, N. (1981). Lectures on Government and Binding. Bonn: Foris Publications.Google Scholar
Confalonieri, R., Weyde, T., Besold, T. R., & del Prado Martín, F. M. (2021). Using ontologies to enhance human understandability of global post-hoc explanations of black-box models. Artificial Intelligence, 296, 103471.Google Scholar
Cropper, A., Dumancic, S., & Muggleton, S. (2020). Turning 30: new ideas in inductive logic programming. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan.Google Scholar
Davis, E., & Marcus, G. (2015). Commonsense reasoning and commonsense knowledge in artificial intelligence. Communications of the ACM, 58(9), 92–103.Google Scholar
De Raedt, L., & Kersting, K. (2008). Probabilistic inductive logic programming. In De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. (Eds.), Probabilistic Logic Programming – Theory and Applications (pp. 1–27). Berlin: Springer.Google Scholar
Donadello, I., Serafini, L., & Garcez, A. (2017). Logic tensor networks for semantic image interpretation. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI (2017) (pp. 1596–1602).Google Scholar
Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41, 1–63.Google Scholar
Fauconnier, G., & Turner, M. (2003). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books.Google Scholar
Fensel, D., van Harmelen, F., Andersson, B., et al. (2008 ). Towards LarKC: a platform for web-scale reasoning. In Proceedings of the 2008 IEEE International Conference on Semantic Computing ICSC, Santa Monica, CA.Google Scholar
Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., & Mueller, E. (2013). Watson: beyond jeopardy!. Artificial Intelligence, 199, 93–105.CrossRefGoogle Scholar
Fillmore, C. J. (1976). Frame semantics and the nature of language. In Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, Vol. 280, pp. 20–32.CrossRefGoogle Scholar
Fincham, J. M., Anderson, H. S., & Anderson, J. R. (2020). Spatiotemporal analysis of event-related fMRI to reveal cognitive states. Human Brain Mapping, 41, 666–683. https://doi.org/10.1002/hbm.24831.Google Scholar
Flener, P., & Schmid, U. (2010). Inductive programming. In Sammut, C. & Webb, G. (Eds.), Encyclopedia of Machine Learning (pp. 537–544). Berlin: Springer.Google Scholar
Fodor, J. (1981). Representations. Cambridge, MA: MIT Press.Google Scholar
Frege, G. (1879). Begriffsschrift. Eine der arithmetischen nachgebildete Formelsprache des reinen Denkens. Halle.Google Scholar
Garcez, A. S. D. A., Lamb, L. C., & Gabbay, D. M. (2007). Connectionist modal logic: representing modalities in neural networks. Theoretical Computer Science, 371(1–2), 34–53.CrossRefGoogle Scholar
Getoor, L., & Taskar, B. (2007). Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press.Google Scholar
Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In Staab, S. and R.Studer, (Eds.), Handbook on Ontologies (pp. 1–17). Berlin: Springer. https://doi.org/10.1007/978-3-540-92673-3_0Google Scholar
Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U., & Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM, 58(11), 90–99.Google Scholar
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI: explainable artificial intelligence. Science Robotics, 4(37), eaay7120.Google Scholar
Halstead, D. T. (2011). Statistical relational learning through structural analogy and probabilistic generalization. Doctoral dissertation, Northwestern University.Google Scholar
Heim, I., & Kratzer, A. (1998). Semantics in Generative Grammar. Oxford: Wiley-Blackwell.Google Scholar
Heim, S. (2007). The Resonant Interface. HCI Foundations for Interaction Design. London: Addison Wesley Publishing Company.Google Scholar
Hitzler, P., Krötzsch, M., & Rudolph, S. (2009). Foundations of Semantic Web Technologies. London: Chapman & Hall/CRC.Google Scholar
Hofmann, J., Kitzelmann, E., & Schmid, U. (2014). Applying inductive program synthesis to induction of number series a case study with IGOR2. In Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 25–36). Cham: Springer.Google Scholar
Horrocks, I., & Patel-Schneider, P. (2004). Reducing OWL entailment to description logic satisfiability. Journal of Web Semantics, 1(4). http://dx.doi.org/10.2139/ssrn.3199027Google Scholar
Howard, R. (1960). Dynamic Programming and Markov Processes. Cambridge, MA: MIT Press.Google Scholar
Jara-Ettinger, J., Gweon, H., Tenenbaum, J. B., & Schulz, L. E. (2015). Children’s understanding of the costs and rewards underlying rational action. Cognition, 140, 14–23.Google Scholar
Kamp, H., & Reyle, U. (1993). From Discourse to Logic: Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory. Dordrecht: Kluwer Academic Publishers.Google Scholar
Kaplan, R., & Bresnan, J. (1982). Lexical-functional grammar: a formal system for grammatical representation. In Bresnan, J. (Ed.), The Mental Representation of Grammatical Relations (pp. 173–281). Cambridge, MA: MIT Press.Google Scholar
Kifer, M., & Lausen, G. (1989). F-logic: a higher-order language for reasoning about objects, inheritance, and scheme. ACM SIGMOD, 18(2), 134–146. https://doi.org/10.1145/66926.66939CrossRefGoogle Scholar
Kindermann, R., & Snell, J. (1980). Markov random fields and their applications. In Meserve, B. E. (Ed.), Contemporary Mathematics. Providence, RI: American Mathematical Society.Google Scholar
Kitzelmann, E., & Schmid, U. (2006). Inductive synthesis of functional programs: an explanation-based generalization approach. Journal of Machine Learning Research, 7(2), 429–454.Google Scholar
Klahr, D., Langley, P., & Neches, R. (Eds.). (1987). Production System Models of Learning and Development. Cambridge, MA: MIT Press.Google Scholar
Kleene, S. (1952). Introduction to Metamathematics. Amsterdam: North-Holland.Google Scholar
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Kripke, S. (1959). A completeness theorem for modal logic. Journal of Symbolic Logic, 24(1), 1–14.Google Scholar
Laird, J. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.Google Scholar
Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55(1), 1–7.Google Scholar
Lehmann, J., Chan, M., & Bundy, A. (2013). A higher-order approach to ontology evolution in physics. Journal on Data Semantics, 2(4), 163–187. https://doi.org/10.1007/s13740-012-0016-7Google Scholar
Leibniz, G. (1677). Preface to the general science. In: Wiener, P., (Ed.), Leibniz Selections. Oxford: Macmillan.Google Scholar
Leitgeb, H. (2005). Interpreted dynamical systems and qualitative laws: from neural networks to evolutionary systems. Synthese, 146(1), 189–202.CrossRefGoogle Scholar
Lenat, D., & Guha, R. (1989). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, MA: Addison-Wesley.Google Scholar
Lenat, D., Prakash, M., & Shepherd, M. (1986). CYC: using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine, 6(4), 65–85.Google Scholar
Magee, D., Needham, C. J., Santos, P., Cohn, A. G., & Hogg, D. C. (2004). Autonomous learning for a cognitive agent using continuous models and inductive logic programming from audio-visual input. In Proceedings of the AAAI workshop on Anchoring Symbols to Sensor Data (pp. 17–24).Google Scholar
Martin, C. (1989). Pragmatic interpretation and ambiguity. In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, Ann Arbor, MI.Google Scholar
Matuszek, C., Cabral, J., Witbrock, M., & DeOliveira, J. (2006). An introduction to the syntax and content of Cyc. In Papers from the 2006 AAAI Spring Symposium “Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering,” Technical Report SS-06-05, Stanford, CA.Google Scholar
McCarthy, J. (1988). Review of the question of artificial intelligence. Annals of the History of Computing, 10(3), 224–229.Google Scholar
Mehta, D., & Raghavan, V. (2002). Decision tree approximations of Boolean functions. Theoretical Computer Science, 270(1–2), 609–623.Google Scholar
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1990). WordNet: an online lexical database. International Journal of Lexicography, 3(4), 235–244.Google Scholar
Minsky, M. (1975). A framework for representing knowledge. In Winston, P., (Ed.), The Psychology of Computer Vision. New York, NY: McGraw-Hill.Google Scholar
Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18(2), 203–226. https://doi.org/10.1016/0004-3702(82)90040-6Google Scholar
Mizoguchi, F., Ohwada, H., Nishiyama, H., & Iwasaki, H. (2012). Identifying driver’s cognitive load using inductive logic programming. In International Conference on Inductive Logic Programming, pp. 166–177. Berlin/Heidelberg: Springer.Google Scholar
Möller, R., & Haarslev, V. (2003). Tableau-based reasoning. In Baader, F. et al. (Eds.), The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge: Cambridge University Press.Google Scholar
Montague, R. (1973). The proper treatment of quantification in ordinary English. In Suppes, P., Moravcsik, J., & Hintikka, J. (Eds.), Approaches to Natural Language (pp. 221–242). Amsterdam: Dordrecht.Google Scholar
Montague, R. (1974). Formal Philosophy: Selected Papers of Richard Montague, edited and with an introduction by Richmond H. Thomason. New Haven, CT: Yale University Press.Google Scholar
Mota, T., & Sridharan, M. (2019). Commonsense reasoning and knowledge acquisition to guide deep learning on robots. In Bicchi, A. et al. (Eds.), Robotics: Science and Systems Proceedings, Volume 15.Google Scholar
Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8(4), 295–318.Google Scholar
Muggleton, S., Schmid, U., Zeller, C., Tamaddoni-Nezhad, A., & Besold, T. (2018). Ultra-strong machine learning: comprehensibility of programs learned with ILP. Machine Learning, 107(7), 1119–1140.Google Scholar
Murray, W. R. (2011). Statistical relational learning in student modeling for intelligent tutoring systems. In International Conference on Artificial Intelligence in Education (pp. 516–518). Berlin/Heidelberg: Springer.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65(3), 151.Google Scholar
Olsson, R. (1995). Inductive functional programming using incremental program transformation. Artificial Intelligence, 74(1), 55–81.CrossRefGoogle Scholar
Ovchinnikova, E. (2012). Integration of World Knowledge for Natural Language Understanding. Berlin: Springer.Google Scholar
Paulheim, H. (2017). Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web, 8, 489–508.Google Scholar
Plotkin, G. (1969). A note on inductive generalization. Machine Intelligence, 5, 153–163.Google Scholar
Plunkett, K., & Elman, J. (1996). Rethinking Innateness: A Handbook for Connectionist Simulations. Cambridge, MA: MIT Press.Google Scholar
Pollard, C., & Sag, I. (1994). Head-Driven Phrase Structure Grammar. Chicago, IL: University of Chicago Press.Google Scholar
Quillian, M. (1968). Semantic memory. In Minsky, M. (Ed.), Semantic Information Processing (pp. 227–270). Cambridge, MA: MIT Press.Google Scholar
Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.Google Scholar
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Burlington, MA: Morgan Kaufmann Publishers.Google Scholar
Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In Michalski, R., Carbonell, J., & Mitchell, T. (Eds.), Machine Learning. An Artificial Intelligence Approach, Volume 1 (pp. 463–482). Berlin/Heidelberg: Springer.Google Scholar
Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies, 27(3), 221–234.Google Scholar
Rao, A., & Georgeff, M. (1991). Modeling rational agents within a BDI-architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (pp. 473–484).Google Scholar
Richardson, M., & Domingos, P. (2006). Markov logic networks. Machine Learning, 62(1–2), 107–136.Google Scholar
Robinson, J. (1965). A machine-oriented logic based on the resolution principle. Journal of the Association for Computing Machinery, 12(1), 23–41. https://doi.org/10.1145/321250.321253Google Scholar
Robinson, J. (1971). Computational logic: the unification computation. Machine Intelligence, 6, 63–72.Google Scholar
Ruppenhofer, J., Ellsworth, M., Petruck, M., Johnson, C., & Scheffczyk, J. (2010). FrameNet II: Extended Theory and Practice. Technical report, Berkeley, CA.Google Scholar
Schank, R. (1975). Conceptual Information Processing. New York, NY: Elsevier.Google Scholar
Schank, R., Abelsohn, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Earlbaum Associates.Google Scholar
Schank, R. C., Goldman, N. M., RiegerIII, C. J., & Riesbeck, C. (1973). MARGIE: memory analysis response generation, and inference on English. In Proceedings of the Second International Joint Conference on Artificial Intelligence, Stanford, CA.Google Scholar
Schmid, U., & Kitzelmann, E. (2011). Inductive rule learning on the knowledge level. Cognitive Systems Research, 12(3–4), 237–248.Google Scholar
Schmidt, M., Krumnack, U., Gust, H., & Kühnberger, K.-U. (2014). Heuristic-driven theory projection: an overview. In Prade, H. & Richard, G. (Eds.), Computational Approaches to Analogical Reasoning: Current Trends. Studies in Computational Intelligence (vol. 548). Berlin/Heidelberg: Springer. https://doi.org/10.1007/978-3-642-54516-0_7Google Scholar
Schöning, U. (1989). Logic for Computer Scientists. Boston, MA: Birkhäuser. https://doi.org/10.1007/978-0-8176-4763-6Google Scholar
Siegelmann, H. T. (1999). Neural Networks and Analog Computation: Beyond the Turing Limit. Berlin: Springer Science & Business Media.Google Scholar
Simmons, R. (1963). Synthetic language behavior. Data Processing Management, 5(12), 11–18.Google Scholar
Skinner, B. (1957). Verbal Behavior. Acton: Copley Publishing Group.Google Scholar
Smolensky, P. (1988). On the proper treatment of connnectionism. Behavioral and Brain Sciences, 11(1), 1–74.Google Scholar
Solway, A., & Botvinick, M. M. (2015). Evidence integration in model-based tree search. Proceedings of the National Academy of Sciences, 112(37), 11708–11713.Google Scholar
Sowa, J. (1976). Conceptual graphs for a data base interface. IBM Journal of Research and Development, 20(4), 336–357. https://doi.org/10.1147/rd.204.0336Google Scholar
Sowa, J. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA: Brooks Cole Publishing Co.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 89–96.Google Scholar
Staab, S., & Studer, R. (Eds.) (2009). Handbook on Ontologies. Berlin: Springer.Google Scholar
Steedman, M. (1996). Surface Structure and Interpretation. Cambridge, MA: MIT Press.Google Scholar
Stumme, G., & Maedche, A. (2001). Ontology merging for federated ontologies for the semantic web. In Gruniger, M. (Ed.), Ontologies and Information Sharing. 17th International Joint Conference on Artificial Intelligence Workshop on Ontologies and Information Sharing, Seattle, WA.Google Scholar
Suchan, J., Bhatt, M., & Schultz, C. (2016). Deeply semantic inductive spatio-temporal learning. In the 26th International Conference on Inductive Logic Programming. London, UK.Google Scholar
Sun, R. (2002). Hybrid systems and connectionist implementationalism. In Encyclopedia of Cognitive Science (pp. 697–703). London: Nature Publishing Group (MacMillan).Google Scholar
Sun, R. (2016). Anatomy of the Mind. New York, NY: Oxford University Press.Google Scholar
Turing, A. (1936). On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2, Volume 42.Google Scholar
Turing, A. (1950). Computing machinery and intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433Google Scholar
Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., & Bal, H. (2010). OWL reasoning with WebPIE: calculating the closure of 100 billion triples. In Proceedings of the ESWC 2010, Heraklion, Greece.Google Scholar
Vanlehn, K., & Ball, W. (1987). A version space approach to learning context-free grammars. Machine Learning, 2(1), 39–74.Google Scholar
Van Opheusden, B., Galbiati, G., Bnaya, Z., Li, Y., & Ma, W. J. (2017). A computational model for decision tree search. In Proceedings of the Thirty-ninth Annual Conference of the Cognitive Science Society. London, UK.Google Scholar
Vernon, D. (2022). Cognitive architectures. In Cangelosi, A. & Asada, M. (Eds.), Cognitive Robotics. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/13780.003.0015.Google Scholar
Vu, M. H., Zehfroosh, A., Strother-Garcia, K., Sebok, M., Heinz, J., & Tanner, H. G. (2018). Statistical relational learning with unconventional string models. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00076Google Scholar
Wermter, S., & Sun, R. (2000). An overview of hybrid neural systems. In S. Wermter & R. Sun (Eds.), Hybrid Neural Systems (pp. 1–13). Berlin/Heidelberg: Springer.Google Scholar
Wooldridge, M. (2000). Reasoning about Rational Agents. Cambridge, MA: MIT Press.Google Scholar
Wooldridge, M. (2009). An Introduction to Multi-Agent Systems (2nd ed.). Oxford: John Wiley & Sons.Google Scholar
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019). From recognition to cognition: visual commonsense reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Amsterdam: IEEE Press.Google Scholar
Zhang, S., & Stone, P. (2015). CORPP: commonsense reasoning and probabilistic planning as applied to dialog with a mobile robot. In Proceedings of the 2015 AAAI Conference on Artificial Intelligence.Google Scholar
Zilke, J. R., Mencía, E. L., & Janssen, F. (2016). Deepred–rule extraction from deep neural networks. In International Conference on Discovery Science (pp. 457–473). Cham: Springer.Google Scholar

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