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Paul Smolensky

 

  
Courses
Research
Students
Education
Positions
Professional Awards and Service
Presentations
Publications
    Books
    Papers
      Grammar
    Computation
    Foundations
 


Krieger-Eisenhower Professor of
Cognitive Science
smolensky@jhu.edu
Phone:410-516-5114
Office: Krieger 241B

     Integration
    Others

 

 

Principles of Dave's Philosophy. From celebration honoring the career of David Rumelhart. Carnegie-Mellon University, October, 1999. [Photos]


Courses (at Johns Hopkins)

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Research

Primary Area: Universal grammar -- Optimality Theory: phonology, syntax, acquisition, learnability, processing.
Secondary Areas: Integration of connectionist ('neural') and symbolic computation: computational, linguistic, and philosophical issues.

Precise theories of higher cognitive domains like language and reasoning rely crucially on complex symbolic rule systems like those of grammar and logic. According to traditional cognitive science and artificial intelligence, such symbolic systems are the very essence of higher intelligence. Yet intelligence resides in the brain, where computation appears to be numerical, not symbolic; parallel, not serial; quite distributed, not as highly localized as in symbolic systems. Furthermore, when observed carefully, much of human behavior is remarkably sensitive to the detailed statistical properties of experience; hard-edged rule systems seem ill-equipped to handle these subtleties. My research attempts to identify the proper roles within a unified theory of cognition for symbolic computation, numerical neural computation, and statistical computation.

More specifically, the basic questions driving this research include: What are the central general principles of computation in connectionist -- abstract neural -- networks? How can these principles be reconciled with those of symbolic computation? Addressing these questions over the past two decades, my work has led to a new computational architecture for cognition which integrates connectionist and symbolic computation. Can this framework further the theory of higher cognition, by connecting it with lower-level principles derived from neural computation?

The connectionist conception of intuitive knowledge as a collection of conflicting soft constraints, interacting via optimization of well-formedness or Harmony, led in joint research with Géraldine Legendre to the connectionist-based formalism of Harmonic Grammar. Incorporating the richly structured representations and universal well-formedness constraints of symbolic linguistic theory, Alan Prince and I developed a grammar formalism called Optimality Theory which brings general connectionist computational principles of optimization into the heart of the symbolic theory of universal grammar. The optimization that emerges is no longer inherently numerical: constraint strengths are encoded in a hierarchy of constraints, ranked from strongest to weakest; each constraint is stronger than all weaker constraints combined.

According to Optimality Theory (OT), possible human languages share a common set of universal constraints on well-formedness. These constraints are highly general, and hence conflict; thus some must be violated in optimal, i.e., grammatical, structures. The different surface patterns of the world's languages emerge via different priority rankings of the fixed set of universal constraints: each ranking is a language-particular grammar, a means of resolving the inherent conflicts among the universal constraints.

My current research addresses multiple aspects of OT. These include superadditive constraint interaction ('local conjunction' of constraints), especially in phonology (vowel harmony; Obligatory Contour Principle; sonority and syllable structure), as well as numerical and connectionist implementation of OT constraint interaction.

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Ph.D. Students (since 1995)

 

Current position

Ph.D. Dissertation, Cognitive Science, Johns Hopkins or Research Topic

 Primary Advisor

 

 

 

Oren Schwartz

Ph.D. students,

Computational modeling of human speech perception

Sara Finley

Cognitive Science,

Morphological and phonological vowel harmony systems

Rebecca Morley

JHU

Grammar induction: computational and experimental studies

Gaja Jarosz

Visiting Assistant Professor of Linguistics, UMass

Rich Lexicons and Restrictive Grammars – Maximum Likelihood Learning in Optimality Theory. 2006.

Lisa Davidson

Assistant Professor of Linguistics,
NYU

The atoms of phonological representation: Gestures, coordination and perceptual features in consonant cluster phonotactics. 2003.

John Hale

Assistant Professor of Linguistics,
Michigan State

Grammar, uncertainty, and sentence processing. 2003.

Bruce Tesar

Associate Professor of Linguistics,
Rutgers

Computational Optimality Theory. 1995. Computer Science, U. of Colorado

 Secondary Advisor

 

 

 

Adam Wayment

(Primary: L. Burzio)

 

Representational entailment theory in neural networks

Adam Buchwald

(Co-advisor: B. Rapp)

Postdoc, Speech Research Laboratory, Indiana U.

Sound structure representation, repair and well-formedness: Grammar in spoken language production. 2005.

Matt Goldrick

(Primary: B. Rapp)

Assistant Professor of Linguistics,
Northwestern

Patterns in sound, patterns in mind: Phonological regularities in speech production. 2002.

Colin Wilson

(Primary: L. Burzio)

Assistant Professor of Linguistics,
UCLA

Targeted Constraints: An Approach to Positional Neutralization in Optimality Theory. 2000.

Adamantios Gafos

(Primary: L. Burzio)

Assistant Professor of Linguistics,
NYU

The Articulatory Basis of Locality in Phonology. 1996.

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Education

  • Ph.D. in mathematical physics, Indiana University, 1981.
  • M.S. in physics, Indiana University, 1977.
  • A.B. summa cum laude in physics, Harvard University, 1976.

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Positions

  • Full Professor, Department of Cognitive Science, Johns Hopkins University, 1994-present.
  • Chair, Department of Cognitive Science, Johns Hopkins University, Jan. 1997 - June 1998 (Acting), July 1998 - June 2000.
  • Adjunct Professor, Department of Linguistics, University of Maryland at College Park, 1994-present.
  • Assistant Director, Center for Language and Speech Processing, Johns Hopkins University, 1995-present.
  • Director, NSF IGERT Training Program in the Cognitive Science of Language, 1999-2004.
  • Professor, Department of Computer Science, University of Colorado at Boulder

Full Professor, 1994-95 (on leave, 1994-95).

Associate Professor, 1990-94.

Assistant Professor, 1985-90.

  • Assistant Research Cognitive Scientist (Assistant Professor - Research), Institute for Cognitive Science, University of California at San Diego, 1982-85.
  • Visiting Scholar, Program in Cognitive Science, University of California at San Diego, 1981-82.
  • Faculty, Linguistic Institute, MIT/Harvard, 2005.
  • Faculty, First International Summer Institute in Cognitive Science, SUNY Buffalo, 1994.
  • Faculty, Linguistic Institute, University of California at Santa Cruz, 1991.
  • Faculty, Connectionist Models Summer School; Carnegie-Mellon University, 1986, 1988; University of California, San Diego, 1990; University of Colorado, Boulder, 1993.
  • National Science Foundation, John H. Edwards, and Indiana University Graduate Fellow, 1976-81.

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Professional Awards and Service

  • David E. Rumelhart Prize for Theoretical Contributions to Cognitive Science, 2005
  • Guggenheim Foundation Fellowship, 1995–96.
  • President, Society for Philosophy and Psychology. 2000 – 01.
  • President, Cognitive Science Society. 1995 – 96, 1996 – 97.
  • Governing Board, Cognitive Science Society. 1992 – 98.
  • Executive Board, Society for Philosophy and Psychology. 1988 – 91, 1994 – 97.
  • International Advisory Board, Cognitive Science Department, New Bulgarian University. 1993 – present.

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Recent Presentations

  • An Integrated Connectionist/Symbolic (ICS) Cognitive Architecture. Seoul National University. November, 2002. [4.4M ppt file]

  • Jakobson's Grand Unified Theory of Linguistic Cognition. Seoul National University. November, 2002. [.5M ppt file]
  • Constraint Conjunction and Strong Harmonic Completeness. Korean Phonological Society. November, 2002.[0.6M ppt file]
  • The Harmonic Mind. Cognition Workshop. North American Summer School for Logic, Language, and Information. Stanford University. July, 2002. [2.4M ppt file]
  • Markedness Optimization in Grammar and Cognition. Plenary Lecture, Annual Meeting of the Linguistic Society of America. San Francisco. January, 2002. [1M ppt file]
  • Formal Typology: Explanation in Optimality Theory. Phonology Forum. Tokyo, Japan. August, 2001. [0.5M ppt file]
  • The Harmonic Mind. International Cognitive Science Conference. Beijing, China. August, 2001.[2M ppt file]
  • The Harmonic Mind. Presidential Address, Annual Meeting of the Society for Philosophy and Psychology. Cincinnati, OH. June, 2001. [4M ppt file]

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Publications

ROA = http://roa.rutgers.edu/, the Rutgers Optimality Archive

Books

  1. Smolensky, Paul & Legendre, Géraldine. 2006. The Harmonic Mind: From Neural Computation To Optimality-Theoretic Grammar Vol. 1: Cognitive Architecture; vol. 2: Linguistic and Philosophical Implications. MIT Press.
  2. Prince, Alan & Smolensky, Paul. 2004. Optimality Theory: Constraint interaction in generative grammar. Blackwell. as Technical Report CU-CS-696-93, Department of Computer Science, University of Colorado at Boulder, and Technical Report TR-2, Rutgers Center for Cognitive Science, Rutgers University, New Brunswick, NJ, April 1993. Rutgers Optimality Archive 537 version, 2002.
  3. Tesar, Bruce & Smolensky, Paul. 2000. Learnability in Optimality Theory. MIT Press.
  4. Smolensky, Paul, Mozer, Michael C., & Rumelhart, David E. (eds.). 1996. Mathematical perspectives on neural networks. Erlbaum.
  5. Macdonald, Cynthia & Macdonald, Graham. (eds.). 1995. Connectionism: Debates on psychological explanation, Volume 2. Basil Blackwell. [4 chapters, 183 pp.]
  6. Mozer, Michael C., Smolensky, Paul, Touretzky, David, Elman, Jeffrey, & Weigend, Andreas. (eds.). 1993. Proceedings of the Connectionist Models Summer School 1993.Lawrence Erlbaum Publishers.
  7. Smolensky, Paul. 1992. Il Connessionismo: Tra simboli e neuroni. Italian translation of the entire treatment, including peer commentary: On the proper treatment of connectionism, Behavioral and Brain Sciences, 11, 1-74; with introduction by Marcello Frixione. Genova: Marietti/Cambridge University Press.

Papers

Grammar

  1. Hale, John & Smolensky, Paul. 2006. Harmonic Grammars and harmonic parsers for formal languages. In [1]. Chapter 10.
  2. Legendre, Géraldine, Smolensky, Paul, and Miyata, Yoshiro. 2006. Harmonic Grammar and its subsymbolic foundations. In [1]. Chapter 11.
  3. Smolensky, Paul & Tesar, Bruce. 2006. Principles of Optimality Theory. In [1]. Chapter 12.
  4. Smolensky, Paul. 2006. Optimality in phonology II: Markedness, feature domains, and Local Constraint Conjunction. In [1]. Chapter 14.
  5. Smolensky, Paul & Stevenson, Suzanne. 2006. Optimality in sentence processing. In [1]. Chapter 19.
  6. Legendre, Géraldine, Sorace, Antonella & Smolensky, Paul. 2006. The Optimality Theory -- Harmonic Grammar connection. In [1]. Chapter 20.
  7. Davidson, Lisa, Smolensky, Paul, and Jusczyk, Peter W. 2004. The initial and final states: Theoretical implications and experimental explorations of richness of the base. In René Kager, Joe Pater and Wim Zonneveld, eds. Constraints in Phonological Acquisition. Cambridge University Press. Reprinted in [1], Chapter 17. ROA 428.
  8. Smolensky, Paul. 2003. Markedness, Harmony, and phonological invisibility. Journal of Cognitive Science. 4:1-41.
  9. Prince, Alan, and Smolensky, Paul. 2003. Optimality Theory in phonology. In International Encyclopedia of Linguistics, ed. William John Frawley. Oxford, England: Oxford University Press.
  10. Buchwald, Adam, Schwartz, Oren, Seidl, Amanda, & Smolensky, Paul. 2002. Recoverability Optimality Theory: Discourse Anaphora in a Bi-directional framework. Proceedings of the EDILOG Conference, Edinburgh. 8 pages.
  11. Moreton, Elliott, and Smolensky, Paul. 2002. Typological consequences of Local Constraint Conjunction. Proceedings of the 21st West Coast Conference on Formal Linguistics.
  12. Jusczyk, Peter W., Smolensky, Paul, and Allocco, Theresa. 2002. How English-learning infants respond to markedness and faithfulness constraints. Language Acquisition 10: 31-73.
  13. Smolensky, Paul. 2002. Optimality Theory: Frequently Asked ‘Questions’. Phonological Studies.
  14. Smolensky, Paul. 2002. Why OT now? Phonological Studies.
  15. Smolensky, Paul. 2001. Optimality Theory. In MIT Encyclopedia of the Cognitive Sciences, eds. Robert A. Wilson and Frank C. Keil. Cambridge, MA: MIT Press/Bradford Books.
  16. Smolensky, Paul. 2001. Optimality Theory: Frequently Asked ‘Questions’. In Japanese translation: Gengo, September, Tokyo: Taishukan; Haruka Fukazawa and Mafuyu Kitahara, translators.
  17. Smolensky, Paul. 2001. Why OT now? In Japanese translation: Gengo, September, Tokyo: Taishukan; Haruka Fukazawa and Mafuyu Kitahara, translators.
  18. Soderstrom, Melanie, Mathis, Donald W., and Smolensky, Paul. 2001. Toward computational empirical testing of linguistic innateness: Abstract genomic encoding of an Optimality-Theoretic grammar. Proceedings of the Third International Conference on Cognitive Science, Beijing, China, 14-25. University of Science and Technology of China Press.
  19. Hale, John and Smolensky, Paul. 2001. A parser for harmonic context-free grammars. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. Johanna D. Moore and Keith Stenning, editors. pages 427-432.
  20. Smolensky, P. 2000. Grammar-based connectionist approaches to language. Cognitive Science 23: 589-613. Reprinted in M. Christiansen and N. Chater. 2000. Connectionist Psycholinguistics. Ablex.
  21. Tesar, B. & Smolensky, P. 1998. Learning Optimality-Theoretic grammars. Lingua, 106: 161-196. Reprinted in Sorace, A., Heycock, C. and Shillcock, R. (eds.) Language Acquisition: Knowledge Representation and Processing. Elsevier.
  22. Legendre, G., Smolensky, P., & Wilson, C. 1998. When is less more? Faithfulness and minimal links in wh-chains. In Pilar Barbosa, Danny Fox, Paul Hagstrom, Martha McGinnis, and David Pesetsky, eds., Is the Best Good Enough? Optimality and Competition in Syntax. MIT Press. 249-289
  23. Tesar, B. & Smolensky, P. 1998. Learnability in Optimality Theory. Linguistic Inquiry, 29: 229-268
  24. Prince, A. & Smolensky, P. 1997. Optimality: From neural networks to universal grammar. Science 275: 1604-1610.
  25. Smolensky, P. 1996. On the comprehension/production dilemma in child language. Linguistic Inquiry 27: 720-731. ROA-118.
  26. Smolensky, P. 1996. The initial state and 'richness of the base' in Optimality Theory. Accepted for publication in Linguistic Inquiry in 1997. Technical Report JHU-CogSci-96-4, Cognitive Science Department, Johns Hopkins University. ROA-154.
  27. Legendre, G., Wilson, C., Smolensky, P., Homer, K., & Raymond, W. 1995. Optimality and wh-extraction. University of Massachusetts Occasional Papers in Linguistics 18: Papers in Optimality Theory, J. Beckman, S. Urbanczyk, & L. Walsh, eds. Amherst, MA: GLSA, University of Massachusetts. 607-636. ROA-85.
  28. Smolensky, P. 1995. On the structure of Con, the constraint component of UG. Handout of talk at UCLA, April 7. ROA-86
  29. Tesar, B. & Smolensky, P. 1994. The learnability of Optimality Theory. Proceedings of the West Coast Conference on Formal Linguistics XIII. 122-137.
  30. Tesar, B. & Smolensky, P. 1993. The learnability of Optimality Theory: An algorithm and some basic complexity results. Handout, Rutgers Optimality Workshop—1.
  31. Tesar, B. & Smolensky, P. 1993. The learnability of Optimality Theory: An algorithm and some basic complexity results. ROA–2.
  32. Smolensky, P. 1993. Harmony, markedness, and phonological activity. Handout of keynote address, Rutgers Optimality Workshop—1, October 23. ROA-87.
  33. Legendre, G., Raymond, W., & Smolensky, P. 1993. An Optimality-Theoretic typology of case and grammatical voice systems. Proceedings of the Nineteenth Annual Meeting of the Berkeley Linguistics Society. Berkeley, CA. February. 464-478. ROA-3.
  34. Legendre, G., Miyata, Y., & Smolensky, P. 1991. Unifying syntactic and semantic approaches to unaccusativity: A connectionist approach. In L. Sutton & C. Johnson (with Ruth Shields) (Eds.), Proceedings of the Seventeenth Annual Meeting of the Berkeley Linguistics Society. Berkeley, CA. February. 156-167.
  35. Prince, A. & Smolensky, P. 1991. Notes on Connectionism and Harmony Theory in Linguistics. Technical Report CU-CS-533-91, Department of Computer Science, University of Colorado at Boulder. July. [Notes from the course, 'Connectionism and Harmony Theory in Linguistics,' LSA Linguistic Institute, University of California, Santa Cruz; July, 1991.]
  36. Smolensky, P. 1991. Connectionism. In W. Bright (Ed.) The International Encyclopedia of Linguistics. Oxford University Press. 294-297.
  37. Legendre, G., Miyata, Y., & Smolensky, P. 1990. Can connectionism contribute to syntax? Harmonic Grammar, with an application. Proceedings of the 26th Meeting of the Chicago Linguistic Society. Chicago, IL. April.
  38. Legendre, G., Miyata, Y., & Smolensky, P. 1990. Harmonic Grammar -- A formal multi-level connectionist theory of linguistic well-formedness: An application. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, Cambridge, MA. July. 884-891.
  39. Legendre, G., Miyata, Y., & Smolensky, P. 1990. Harmonic Grammar -- A formal multi-level connectionist theory of linguistic well-formedness: Theoretical foundations. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, Cambridge, MA. July. 388-395.

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Computation

  1. Smolensky, Paul & Tesar, Bruce. 2006. Symbolic computation with activation patterns. In [1]. Chapter 7. 225-259.
  2. Smolensky, Paul. 2006. Tensor product representations: Formal foundations. In [1]. Chapter 8. 259-334.
  3. Smolensky, Paul. 2006. Constraints and optimization: Harmony maximization. In [1]. Chapter 9. 335-382.
  4. Smolensky, P. 2003. Connectionism. In International Encyclopedia of Linguistics, ed. William John Frawley. Oxford, England: Oxford University Press.
  5. Smolensky, Paul. 2001. Connectionist approaches to language. In MIT Encyclopedia of the Cognitive Sciences, eds. Robert A. Wilson and Frank C. Keil. Cambridge, MA: MIT Press/Bradford Books.
  6. Smolensky, P. 1996. Computational, dynamical, and statistical perspectives on the processing and learning problems in neural network theory. In [4]. 1-15.
  7. Smolensky, P. 1996. Computational perspectives on neural networks. In [4]. 17-40.
  8. Smolensky, P. 1996. Dynamical perspectives on neural networks. In [4]. 245-270.
  9. Smolensky, P. 1996. Statistical perspectives on neural networks. In [4]. 453-496.
    Ms. of [50]-[53]: [pdf]
  10. Tesar, B. & Smolensky, P. 1994. Synchronous-firing variable binding is spatio-temporal tensor product representation. Proceedings of the 16th Annual Conference of the Cognitive Science Society. Atlanta, GA. August.
  11. Smolensky, P. 1993. Harmonic Grammars for formal languages. In S. Hanson, J. D. Cowan, & C. L. Giles, (Eds.), Advances in Neural Information Processing Systems 5, San Mateo, CA: Morgan Kaufmann. [Collected papers of the IEEE Conference on Neural Information Processing Systems-Natural and Synthetic, Denver, Nov. 1992.] 847-854.
  12. Miyata, Y, Smolensky, P., & Legendre, G. 1993. Distributed representation and parallel processing of recursive structures. Proceedings of the 15th Annual Conference of the Cognitive Science Society, Boulder, CO. June. 759-764.
  13. Wagner, K., Mozer, M., Smolensky, P., Miyata, Y., Fellows, M. 1993. Optical neural networks using a new radial nonlinear neural layer. Proceedings of the SPIE (Society of Photo-Optical Instrumentation Engineers), 1773A-10.
  14. McMillan, C., Mozer, M., & Smolensky, P. 1993. Dynamic conflict resolution in a connectionist rule‑based system. Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1366–1371. San Mateo, CA: Morgan Kauffmann.
  15. McMillan, C., Mozer, M., & Smolensky, P. 1992. Rule induction through integrated symbolic and subsymbolic processing. In J. Moody, S. Hanson, & R. Lippman, (Eds.), Advances in Neural Information Processing Systems 4. San Mateo, CA: Morgan Kaufmann. [Collected papers of the IEEE Conference on Neural Information Processing Systems—Natural and Synthetic, Denver, Nov. 1991.] 969–976.
  16. Smolensky, P. 1992. Integrated connectionist/symbolic computation and formal languages. Proceedings of the International Symposia on Information Sciences. Iizuka, Kyushu, Japan. July. 42–49.
  17. Smolensky, P. 1991. Connectionism. In W. Bright (Ed.) The International Encyclopedia of Linguistics. Oxford University Press. 294–297.
  18. Legendre, G., Miyata, Y., & Smolensky, P. 1991. Distributed recursive structure processing. In Touretzky, D. S., Lippman, R. (Eds.), Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann. [Collected papers of the IEEE Conference on Neural Information Processing Systems-Natural and Synthetic, Denver, Nov. 1990.] 591-597.
  19. McMillan, C., Mozer, M. C., & Smolensky, P. 1991. The connectionist scientist game: Rule extraction and refinement in a neural network. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, Chicago, IL. July.
  20. McMillan, C., Mozer, M., & Smolensky, P. 1991. Learning explicit rules in a neural network. Proceedings of the International Joint Conference on Neural Networks. Seattle, WA. July.
  21. Smolensky, P. 1990. Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence, 46, 159-216. [Reprinted in G. Hinton, (Ed.), 1990), Connectionist symbol processing, Elsevier/MIT Press.]
  22. Brousse, O. & Smolensky, P. 1990. Connectionist generalization and incremental learning in combinatorial domains. In H. Haken (Ed.), Synergetics of Cognition. Springer-Verlag. 70-80.
  23. Smolensky, P. 1990. Representation in connectionist networks. Intellectica: The Journal of the French Association for Cognitive Research, 9-10, 127-165.
  24. Brousse, O. & Smolensky, P. 1990. Interference and generalization in connectionist networks: Within-domain structure or between-domain correlation? — A response, Neural Network Review, 4, 29.
  25. Mozer, M. C., & Smolensky, P. 1989. Using relevance to reduce network size automatically. Connection Science, 1, 3-16.
  26. Dolan, C. & Smolensky, P. 1989. Tensor Product Production System: A modular architecture and representation. Connection Science, 1, 53-68.
  27. Mozer, M. C., & Smolensky, P. 1989. Skeletonization: Trimming the fat from a network via relevance assessment. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 1. San Mateo, CA: Morgan Kaufmann. [Collected papers of the IEEE Conference on Neural Information Processing Systems—Natural and Synthetic, Denver, Nov. 1988.] 107–115.
  28. Brousse, O. & Smolensky, P. 1989. Virtual memories and massive generalization in connectionist combinatorial learning. Proceedings of the Eleventh Annual Meeting of the Cognitive Science Society. Ann Arbor, MI. August. 380-387.
  29. Smolensky, P. 1988. Analysis of distributed representation of constituent structure in connectionist systems. Proceedings of Neural Information Processing Systems-87. Denver, CO. November. 730-739.
  30. Bein, J. & Smolensky, P. 1988. Application of the interactive activation model to document retrieval. Proceedings of Neuro‑Nîmes 1988: Neural networks and their applications. Nîmes, France. November. 295–308.
  31. McMillan, C. & Smolensky, P. 1988. Analyzing a connectionist model as a system of soft rules. Proceedings of the Tenth Annual Meeting of the Cognitive Science Society. Montreal, Canada. August. 62-68.
  32. Dolan, C. & Smolensky, P. 1988. Implementing a connectionist production system using tensor products. In D. Touretzky, G. E. Hinton, & T. J. Sejnowski (Eds.), Proceedings of the Connectionist Models Summer School, 1988. Morgan Kaufmann. 265–272.
  33. Smolensky, P. 1987. On variable binding and the representation of symbolic structures in connectionist systems. Technical Report CU-CS-355-87, Department of Computer Science, University of Colorado at Boulder. February.
  34. Smolensky, P. 1986. Formal modeling of subsymbolic processes: An introduction to harmony theory. In N. E. Sharkey (Ed.), Directions in the Science of Cognition. London: Horwoods. 204–235.
  35. Smolensky, P. 1986. Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. Cambridge, MA: MIT Press/Bradford Books. 194-281.
  36. Smolensky, P. 1986. Neural and conceptual interpretations of parallel distributed processing models. In J. L. McClelland, D. E. Rumelhart, & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models. Cambridge, MA: MIT Press/Bradford Books. 390-431.
  37. Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. 1986. Schemata and sequential thought processes in parallel distributed processing. J. L. McClelland, D. E. Rumelhart, & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models. Cambridge, MA: MIT Press/Bradford Books. 7-57. [Reprinted in A. Collins & E. Smith (Eds), 1988, Readings in Cognitive Science, San Mateo, CA: Morgan Kaufmann.]
  38. Smolensky, P. 1984. The mathematical role of self-consistency in parallel computation. Proceedings of the Sixth Annual Conference of the Cognitive Science Society. Boulder, CO. June. 319-325.
  39. Riley, M. S. & Smolensky, P. 1984. A parallel model of (sequential) problem solving. Proceedings of the Sixth Annual Conference of the Cognitive Science Society. Boulder, CO. June. 286-292.
  40. Smolensky, P. 1984. Harmony theory: thermal parallel models in a computational context. In P. Smolensky & M. S. Riley, Harmony theory: Problem solving, parallel cognitive models, and thermal physics, Technical Report 8404. Institute for Cognitive Science, University of California at San Diego. April.
  41. Hinton, G. E. & Smolensky, P. 1984. Parallel computation and the mass‑spring model of motor control. Report 123. Center for Human Information Processing, University of California at San Diego. June.
  42. Smolensky, P. 1983. Schema selection and stochastic inference in modular environments. Proceedings of the National Conference on Artificial Intelligence. Washington, DC. August. 378-382.

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Foundations

  1. Smolensky, Paul & Legendre, Géraldine. 2006. The unity of cognitive science: Methodological considerations. In [1]. Chapter 3. 93-113.
  2. Smolensky, Paul. 2006. Computational levels and integrated connectionist/symbolic explanation. In [1]. Chapter 23. 1035-1125.
  3. Smolensky, P. 1995. Constituent structure and explanation in an integrated connectionist/symbolic cognitive architecture. In [5]. 221-290.
  4. Smolensky, P. 1995. On the projectable predicates of connectionist psychology: A case for belief. In [5], 357-394. Oxford: Basil Blackwell.
  5. Smolensky, P. 1994. Computational theories of mind. In S. Guttenplan (Ed.), A Companion to the Philosophy of Mind. Blackwell Publishers. 176-185.
  6. Smolensky, P. 1991. Connectionism, constituency, and the language of thought. In B. Loewer & G. Rey (Eds.), Meaning in Mind: Fodor and his Critics. Oxford: Basil Blackwell. 201-227. Reprinted in [5].
  7. Smolensky, P. 1990. Connectionism and the foundations of AI. In D. Partridge & Y. Wilks (Eds.), The Foundations of Artificial Intelligence: A Sourcebook. Cambridge: Cambridge University Press. 306–326.
  8. Smolensky, P. 1989. Connectionism and constituent structure. In R. Pfeifer, Z. Schreter, F. Fogelman, & L. Steels (Eds.), Connectionism in Perspective. Amsterdam: Elsevier. 3–24.
  9. Smolensky, P. 1989. Connectionist modeling: Neural computation/mental connections. In L. Nadel (Ed.), P. Culicover, L. A. Cooper, R. M. Harnish (Assoc. Eds.), Neural connections, mental computation. Cambridge, MA: MIT Press/Bradford. 49-67. [Reprinted in J. Haugeland, (Ed.). 1997. Mind Design II: Philosophy, Psychology, Artificial Intelligence, MIT Press/Bradford Books.]
  10. Smolensky, P. 1987. On the connectionist reduction of conscious rule interpretation. Proceedings of the Ninth Conference of the Cognitive Science Society. Seattle, WA. July. 187–94.
  11. Smolensky, P. 1987. The constituent structure of connectionist mental states: A reply to Fodor and Pylyshyn. Southern Journal of Philosophy, 26 (Supplement), 137-63. [Reprinted in T. Horgan & J. Tienson (Eds.), 1991), Connectionism and the Philosophy of Mind, Dordrecht: Kluwer Academic. 281-308; Spanish translation in E. Rabossi (Ed.), Filosofía y Ciencia Cognitiva, Buenos Aires-Barcelona: Editorial Paidós.]

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Integration

98.         Smolensky, Paul & Legendre, Géraldine. 2006. Harmony optimization and the computational architecture of the mind/brain. In [1]. Chapter 1. 3-56.

99.         Smolensky, Paul & Legendre, Géraldine. 2006. Principles of the Integrated Connectionist/‌Symbolic cognitive architecture. In [1]. Chapter 2. 57-91.

100.     Smolensky, Paul & Legendre, Géraldine. 2006. Formalizing the principles I: Representation and processing in the mind/brain. In [1]. Chapter 5. 139-196.

101.     Smolensky, Paul & Legendre, Géraldine. 2006. Formalizing the principles II: Optimization and grammar. In [1]. Chapter 6. 197-224.

102.     Soderstrom, Melanie, Mathis, Donald W. & Smolensky, Paul. 2006. Abstract genomic encoding of Universal Grammar in Optimality Theory. In [1]. Chapter 21. 925-1002.

103.     Smolensky, P., Legendre, G., & Miyata, Y. 1993. Integrating connectionist and symbolic computation for the theory of language. Current Science 64, 381-391. Reprinted in: V. Honavar & L. Uhr, Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, 509-530. Academic Press.

104.     Smolensky, P., Legendre, G., & Miyata, Y. 1992. Principles for an Integrated Connectionist/Symbolic Theory of Higher Cognition. Technical Report CU-CS-600-92, Department of Computer Science and 92-8, Institute of Cognitive Science. University of Colorado at Boulder. (75 pages). Expanded to [1].

105.     McNaughton, B. L. & Smolensky, P. 1991. Connectionist and neural modeling: Converging in the hippocampus. In R. G. Lister & H. J. Weingartner (Eds.), Perspectives on Cognitive Neuroscience. Oxford University Press. 93-109.

106.     Smolensky, P. 1990. In defense of PTC: Reply to continuing commentary. Behavioral and Brain Sciences. 13, 407-411.

107.     Smolensky, P. 1988. Putting Together Connectionism — again. Behavioral and Brain Sciences, 11, 59-74.

108.     Smolensky, P. 1988. On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1-23. [Reprinted in D. Cole, J. Fetzer, & T. Rankin (Eds.), 1990), Philosophy, Mind, and Cognitive Inquiry, Dordrecht: Kluwer Academic; A. I. Goldman, 1994), Readings in Philosophy and Cognitive Science, Cambridge: MIT Press/Bradford Books; and [5]; Italian translation published as monograph [7]; Hungarian translation in A Cognitive Science Reader, Budapest: Osiris Publishing House. 1997)]

109.     Smolensky, P. 1987. Connectionist AI, symbolic AI, and the brain. Artificial Intelligence Review, 1, 95-109. [French translation with added post scriptum in D. Andler, (Ed.). 1992. Introduction aux sciences cognitives, Editions Gallimard.]

110.     Smolensky, P. 1987. Connectionism and implementation: Commentary on J. R. Anderson, Methodologies for studying human knowledge. The Behavioral and Brain Sciences, 10.

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Others

111.     Smolensky, P., Fox, B., King, R., Lewis, C. 1988. Computer-aided reasoned discourse, or, How to argue with a computer. In R. Guindon (Ed.), Cognitive Science and Its Applications For Human-Computer Interaction. Hillsdale, NJ: Erlbaum. 109-62.

112.     Smolensky, P., Bell, B., Fox, B., King, R., & Lewis, C. 1987. Constraint-based hypertext for argumentation. Proceedings of Hypertext-87. Chapel Hill, NC. November. 215-245.

113.     Smolensky, P., Monty, M. L. & Conway, E. 1984. Formalizing task descriptions for command specification and documentation. Proceedings of the International Federation of Information Processing Conference on Human-Computer Interaction. London, England. September. 603-609.

114.     Greenspan, S. & Smolensky, P. 1984. DESCRIBE: Environments for Specifying Commands and Retrieving Information By Elaboration. In User centered system design, Part II, Technical Report No. 8402. Institute for Cognitive Science, University of California at San Diego. March.

115.     O’Malley, C., Smolensky, P, Bannon, L., Conway, E., Graham, J., Sokolov, J., & Monty, M. L. 1983. A proposal for user centered system documentation. Proceedings of the CHI 1983 Conference on Human Factors in Computing Systems. Boston, MA. December.

116.     Freedman, B., Smolensky, P, & Weingarten, D. H. 1982. Monte Carlo evaluation of the continuum limit of (j4)4 and (j4)3 field theory. Physics Letters B, 113, 481-486.

117.     Smolensky, P. 1981. Lattice Renormalization of j4 Theory. Doctoral thesis in mathematical physics, Indiana University.

118.     Bradbury, K., Danziger, S., Smolensky, E., & Smolensky, P. 1979. Public assistance, female headship and economic well-being. Journal of Marriage and the Family, 519-535. [Reprinted in G. McDonald & F. Nye (Eds.), 1979), Family policy, National Council on Family Relations.]

119.     Cicchetti, C., Gillen, W., & Smolensky, P. 1977. The Marginal Cost and Pricing of Electricity: An Applied Approach. Ballinger.

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