Commonsense Bibliography

 

Collected by Push Singh and Erik T. Mueller

Contributions from: Barbara Barry, Leiguang Gong, Hugo Liu, and Stefan Marti

 

Last updated 2002-02-11

 

Table of contents

 

1      Recent overviews of the problem of common sense

2      Classics

3      Critiques and essays

4      Cyc

4.1        Cyc overviews

4.2        Cyc criticisms and evaluations

5      Architectures for common sense

5.1        The society of mind

5.2        The cognition and affect project

5.3        Blackboard systems

5.4        Other heterogeneous architectures

5.5        Soar

5.6        Other unified architectures

6      Logical formalisms for commonsense reasoning

6.1        Overviews

6.2        Situation calculus

6.3        Event calculus

6.4        Language of the causal calculator

6.5        Features and fluents

6.6        Default reasoning

6.7        Circumscription

7      Other mechanisms for common sense

7.1        Analogy and metaphor

7.2        Case-based reasoning

7.3        Marker passing

7.4        Reformulation

7.5        Lexical semantics

7.6        Connectionism

7.7        Situated action

8      Realms of common sense

8.1        Time

8.2        Space

8.3        Physics

8.4        Egg cracking

8.5        Frames and scripts

8.6        Plans and Goals

8.7        Beliefs, desires, and intentions

8.8        Interpersonal relations

8.9        Emotions

8.10      Personality

8.11      Plot structures

8.12      Economics

8.13      Vision

8.14      Gesture

8.15      Metaplanning and reflection

8.16      Context

8.17      Causality

8.18      Creativity and invention

9      Acquisition of common sense

9.1        Distributed human projects

9.2        Sketching

9.3        Learning structural representations

9.4        Sensory grounded learning

10     Applications of common sense

10.1      Context-aware agents

10.2      The Semantic Web

10.3      Story understanding

10.4      Robots

11     Results from psychology and neuroscience

12     Popular books

13     Web resources

 

1           Recent overviews of the problem of common sense

 

Minsky, Marvin (2000). Commonsense-based interfaces. Communications of the ACM, 43(8), 67-73.

http://www.acm.org/pubs/citations/journals/cacm/2000-43-8/p66-minsky/

http://commonsense.media.mit.edu/minsky.pdf

 

Minsky, Marvin (forthcoming). The Emotion Machine (Part 5).

http://web.media.mit.edu/~minsky/E5/eb5.html

 

Singh, Push (2002). The Open Mind Common Sense project.

http://openmind.media.mit.edu/Kurzweil.htm

http://www.kurzweilai.net/meme/frame.html?main=/articles/art0371.html

 

Davis, Ernest (1998). The naive physics perplex. AI Magazine, 19(4), 51-79.

http://csdocs.cs.nyu.edu/Dienst/UI/2.0/Describe/ncstrl.nyu_cs%2FTR1997-738

2           Classics

 

McCarthy, John (1959). Programs with common sense.

http://www-formal.stanford.edu/jmc/mcc59.html

 

McCarthy, John (1990). Formalizing common sense. Norwood, NJ: Ablex.

http://www-formal.stanford.edu/jmc/

 

Minsky, Marvin (1968). Introduction. In Marvin L. Minsky (Ed.), Semantic information processing (pp. 1-32). Cambridge, MA: MIT Press.

 

Minsky, Marvin (1974). A framework for representing knowledge (AI Laboratory Memo 306). Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

ftp://publications.ai.mit.edu/ai-publications/0-499/AIM-306.ps

http://www.media.mit.edu/~minsky/papers/Frames/frames.html

 

Minsky, Marvin (1986). The society of mind. New York: Simon and Schuster.

3           Critiques and essays

 

Birnbaum, Lawrence (1991). Rigor mortis: a response to Nilsson's "Logic and artificial intelligence." Artificial Intelligence, 47, 57-77.

 

Clancey, W. J., Smoliar, S. W., and Stefik, M. J. (Eds.) (1994). Contemplating minds: A forum for artificial intelligence. Cambridge, MA: MIT Press.

 

Davis, R., Shrobe, H., & and Szolovits, P. (1993). What is a Knowledge Representation? AI Magazine, 17-33.

http://medg.lcs.mit.edu/ftp/psz/k-rep.html

 

Giunchiglia, Fausto (1995). An epistemological science of common sense. Book review of John McCarthy's Formalizing Common Sense. Artificial Intelligence, 77, 371-392.

http://citeseer.nj.nec.com/giunchiglia96epistemological.html

 

Hayes, P. J. (1977). In defence of logic. Proceedings of the Fifth International Joint Conference on Artificial Intelligence.

 

McCarthy, John. The well-designed child.

http://www-formal.stanford.edu/jmc/child1.html

 

McCarthy, John (1996). From here to human-level AI.

http://www-formal.stanford.edu/jmc/human.html

 

McDermott, D. (1987). A critique of pure reason. Computational Intelligence, 3, 151-160.

Mueller, Erik T. (1999). Prospects for in-depth story understanding by computer. CogPrints cog00000554.

http://www.media.mit.edu/~mueller/papers/storyund.html


Nilsson, Nils. J. (1991). Logic and artificial intelligence. Artificial Intelligence, 47, 31-56.

4           Cyc

 

4.1         Cyc overviews

 

Guha, Ramanathan, & Lenat, Douglas (1990). Cyc: A midterm report. AI Magazine, 11(3), 32-59.

 

Guha, Ramanathan, & Lenat, Douglas (1994). Enabling agents to work together. Communications of the ACM, 37(7),127-142.

http://www.acm.org/pubs/citations/journals/cacm/1994-37-7/p126-guha/

 

Lenat, Douglas (1995). CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11).

 

Lenat, Douglas (1997). Cyc Upper Ontology.

http://www.cyc.com/cyc-2-1/index.html

 

Lenat, Douglas, & Guha, Ramanathan (1990). Building large knowledge-based systems. Reading, MA: Addison-Wesley.

 

Lenat, Douglas, & Guha, Ramanathan (1991). The evolution of CycL, the Cyc representation language. SIGART Bulletin, 2(3), 84-87.

 

4.2         Cyc criticisms and evaluations

 

Guha, Ramanathan, & Lenat, Douglas (1993). Re: CycLing paper reviews, Artificial Intelligence, 61(1), 149-174.

 

Locke, Christopher (1990). Common knowledge or superior Ignorance?

http://www.panix.com/~clocke/ieee.html

 

Mahesh, Kavi, Nirenburg, Sergei, Cowie, Jim, & Farwell, David (1996). An assessment of Cyc for natural language processing (Technical Report MCCS 96-302). Computing Research Laboratory, New Mexico State University, Las Cruces, New Mexico.

http://crl.nmsu.edu/Research/Pubs/MCCS/Postscript/mccs-96-302.ps

 

Pratt, Vaughan (1994). Cyc report.

http://boole.stanford.edu/cyc.html

http://www.cs.umbc.edu/~narayan/proj/cyc-critic.html

 

Stefik, Mark J., & Smoliar, Stephen W. (1993). The commonsense reviews. Artificial Intelligence, 61, 37-179.

http://www1.elsevier.nl/inca/publications/store/5/0/5/6/0/1/

http://www.cs.brandeis.edu/~brendy/CYC_report.txt

5           Architectures for common sense

 

5.1         The society of mind

 

Minsky, Marvin (1986). The society of mind. New York: Simon and Schuster.

 

Minsky, Marvin (forthcoming). The Emotion Machine.

http://web.media.mit.edu/~minsky/E1/eb1.html

http://web.media.mit.edu/~minsky/E2/eb2.html

http://web.media.mit.edu/~minsky/E3/eb3.html

http://web.media.mit.edu/~minsky/E4/eb4.html

http://web.media.mit.edu/~minsky/E5/eb5.html

 

Minsky, Marvin (1981). Jokes and their relation to the cognitive unconscious. In Vaina and Hintikka (Eds.), Cognitive Constraints on Communication. Reidel.

http://web.media.mit.edu/~minsky/papers/jokes.cognitive.txt

 

Minsky, Marvin (1991). Logical vs. analogical or symbolic vs. connectionist or neat vs. scruffy. AI Magazine, Summer 1991.

http://web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html

 

Minsky, Marvin (1994). Negative expertise. International Journal of Expert Systems, 7(1), 13-19.

http://web.media.mit.edu/~minsky/papers/NegExp.mss.txt

 

5.2         The cognition and affect project

 

Beaudoin, Luc P. (1994). Goal processing in autonomous agents.

http://www.cs.bham.ac.uk/research/cogaff/0-INDEX81-95.html#38

 

Sloman, Aaron (1981). Why robots will have emotions. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.

http://www.cs.bham.ac.uk/research/cogaff/0-INDEX81-95.html#36

 

Sloman, Aaron (1998). Damasio, Descartes, alarms and meta-management.

http://www.cs.bham.ac.uk/research/cogaff/0-INDEX96-99.html#36

 

Sloman, Aaron (1998). What’s an AI toolkit for?

http://www.cs.bham.ac.uk/research/cogaff/0-INDEX96-99.html#34

 

5.3         Blackboard systems

 

Carver, N., & Lesser, V. (1994). Evolution of blackboard control architectures. Expert Systems with Applications 7, 1-30.

http://citeseer.nj.nec.com/carver92evolution.html

 

Engelmore, R. and Morgan, T. (1988). Blackboard systems. Reading, MA: Addison-Wesley.

 

Hayes-Roth, B. (1985). A blackboard architecture for control. Artificial Intelligence, 26, 251-321.

 

Nii, H. P. (1986). Blackboard Systems: The blackboard model of problem solving and the evolution of blackboard architectures. AI Magazine, 7(2), 38-53.

 

5.4         Other heterogeneous architectures

 

Mueller, Erik T. (1990). Daydreaming in humans and machines: A computer model of the stream of thought. Norwood, NJ: Ablex/Intellect.
ftp://ftp.cs.ucla.edu/tech-report/198_-reports/870017.pdf

 

Mueller, Erik T. (1998). Natural language processing with ThoughtTreasure. New York: Signiform.

http://www.signiform.com/tt/book/

 

Riecken, Doug (1994). M: An architecture of integrated agents. Communications of the ACM, 37(7), 107-116.

 

Singh, Push (1999). Big list of mental agents for common sense thinking.

http://www.media.mit.edu/people/push/agencies.html

 

5.5         Soar

 

Laird, J.E., & Rosenbloom, P.S. (1996). The evolution of the Soar cognitive architecture. In T. Mitchell (Ed.) Mind Matters.

http://citeseer.nj.nec.com/laird94evolution.html

 

Lehman, J.F., Laird, J.E., & Rosenbloom, P.S. (1996). A gentle introduction to Soar, an architecture for human cognition. In S. Sternberg & D. Scarborough (Eds.) Invitation to Cognitive Science (Volume 4).

http://www.cse.msu.edu/~cse841/papers/Soar.pdf

 

Newell A., & Simon, H. A. (1963). GPS, a program that simulates human thought. In E. A. Feigenbaum and J. Feldman, editors, Computers and Thought, pages 279-293. New York: McGraw-Hill.

 

Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

 

Rosenbloom, P.S., Laird, J.E. & Newell, A. (1993). The Soar Papers: Readings on Integrated Intelligence. Cambridge, MA: MIT Press.

 

5.6         Other unified architectures

 

Anderson, John R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

6           Logical formalisms for commonsense reasoning

 

6.1         Overviews

 

Davis, Ernest (1990). Representations of Commonsense Knowledge. San Mateo, CA: Morgan Kaufmann.

http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-033-7

 

Hobbs, Jerry R., & Moore, Robert C. (Eds.). (1985). Formal theories of the commonsense world. Norwood, NJ: Ablex.

 

McCarthy, John (1990). Formalizing common sense. Norwood, NJ: Ablex.

http://www-formal.stanford.edu/jmc/

 

6.2         Situation calculus

 

McCarthy, John, & Hayes, Patrick J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In D. Michie & B. Meltzer (Eds.), Machine intelligence 4. Edinburgh, Scotland: Edinburgh University Press.

http://www-formal.stanford.edu/jmc/mcchay69/mcchay69.html

 

Reiter, Raymond (2001). Knowledge in action: Logical foundations for specifying and implementing dynamical systems. Cambridge, MA: MIT Press.

 

6.3         Event calculus

 

Kowalski, R. & Sergot, M. J. (1986). A logic-based calculus of events. New Generation Computing, 4, 67-95.

 

Shanahan, Murray (1997). Solving the frame problem. Cambridge, MA: MIT Press.

 

Shanahan, Murray (1999). The Event Calculus explained. In M. J. Wooldridge & M. Veloso (Eds.), Artificial intelligence today (pp. 409-430). Heidelberg: Springer-Verlag.

http://www-ics.ee.ic.ac.uk/~mpsha/ECExplained.ps.Z

 

6.4         Language of the causal calculator

 

Akman, Varol, Erdogan, Selim, & Lee, Joohyung, & Lifschitz, Vladimir (2001). A representation of the traffic world in the language of the causal Calculator. Fifth Symposium on Logical Formalizations of Commonsense Reasoning.

http://www.cs.nyu.edu/faculty/davise/commonsense01/final/akman.ps

 

Lee, Joohyung, Lifschitz, Vladimir, & Turner, Hudson (2001). A representation of the zoo world in the language of the causal calculator. Fifth Symposium on Logical Formalizations of Commonsense Reasoning.

http://www.cs.nyu.edu/faculty/davise/commonsense01/final/lee.ps

 

McCain, N., & Turner, H. (1997). Causal theories of action and change. Proceedings of the Fourteenth National Conference on Artificial Intelligence.

http://citeseer.nj.nec.com/mccain97causal.html

 

McCain, N., & Turner, H. (1995). A causal theory of ramifications and qualifications. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence.

http://citeseer.nj.nec.com/mccain95causal.html

 

6.5         Features and fluents

 

Sandewall, Erik (1994). Features and fluents: The representation of knowledge about dynamical systems (Volume I). Oxford University Press.

 

Doherty, Patrick, Gustafsson, Joakim, Karlsson, Lars, & Kvarnstrom, Jonas (1998). TAL: Temporal Action Logics language specification and tutorial.

http://www.ep.liu.se/ea/cis/1998/015/

 

6.6         Default reasoning

 

de Kleer, J. (1986). An assumption based truth maintenance system. Artificial Intelligence, 28, 127-162.

 

Doyle, J. (1979). A truth maintenance system. Artificial Intelligence, 12, 231-272.

 

McDermott, D., & Doyle. J. (1980). Non-monotonic logic I. Artificial Intelligence 13, 41-72.

 

Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence 13, 81-132.

 

6.7         Circumscription

 

Lifschitz, Vladimir (1994). Circumscription. In Handbook of logic in AI and logic programming (Volume 3). Oxford University Press.

http://www.cs.utexas.edu/users/vl/mypapers/circumscription.ps

 

McCarthy, John (1980). Circumscription—A form of non-monotonic reasoning. Journal of Artificial Intelligence, 13, 27-39.

http://www-formal.stanford.edu/jmc/circumscription.html

 

7           Other mechanisms for common sense

 

7.1         Analogy and metaphor

 

Falkenhainer, B., Forbus, K.D., & Gentner, D. (1990). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

http://citeseer.nj.nec.com/falkenhainer89structuremapping.html

 

Forbus, K. D., Gentner, D., Markman, A. B., & Ferguson, R. W. (1998). Analogy just looks like high-level perception: Why a domain-general approach to analogical mapping is right. Journal of Experimental and Theoretical Artificial Intelligence, 10(2), 231-257.

http://www.psych.nwu.edu/psych/people/faculty/gentner/pdfs%20papers/forbus-gentner-98.pdf

 

Gentner, D. (2001). Spatial metaphors in temporal reasoning. In M. Gattis (Ed.), Spatial schemas in abstract thought (pp. 203-222). Cambridge, MA: MIT Press.

http://www.psych.nwu.edu/psych/people/faculty/gentner/pdfs%20papers/spatial%20schemas.2001.pdf

 

Gentner, D., Bowdle, B., Wolff, P., & Boronat, C. (2001). Metaphor is like analogy. In D. Gentner, K. J. Holyoak, & B. N. Kokinov (Eds.), The analogical mind: Perspectives from cognitive science (pp. 199-253). Cambridge, MA: MIT Press.

http://www.psych.nwu.edu/psych/people/faculty/gentner/pdfs%20papers/gentner-a2k-01.pdf

 

Lakoff G. & Johnson M. (1990) Metaphors we live by. University of Chicago Press.

 

7.2         Case-based reasoning

 

Carbonell, J. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R.S. Michalski et al. (Eds.), Machine intelligence: An AI approach.

 

Hammond, C. (1989). Case-based planning: Viewing planning as a memory task. San Diego: Academic Press.

 

Hammond, K. J. (1990). Explaining and repairing plans that fail. Artificial Intelligence, 45(3), 173-228.

 

Kolodner, J. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6, 3-34.

 

Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufman.

 

Veloso, M. M., & Carbonell, J. G. (1993). Derivational analogy in Prodigy: Automating case acquisition, storage, and utilization. Machine Learning, 10 , 249-278.

 

7.3         Marker passing

 

Norvig, Peter (1987). Unified theory of inference for text understanding (Report No. UCB/CSD 87/339). Berkeley, CA: University of California, Computer Science Division.
http://sunsite.berkeley.edu/Dienst/UI/2.0/Describe/ncstrl.ucb/CSD-87-339


Norvig, Peter (1989). Marker passing as a weak method for text inferencing. Cognitive Science, 13, 569-620.

 

Hendler, J. (1988). Integrating marker-passing and problem-solving. Hillsdale, NJ: Erlbaum.

 

7.4         Reformulation

 

Amarel, Saul (1968). On representations of problems of reasoning about actions. In Michie (Ed.), Machine Intelligence 3. Edinburgh University Press.

 

Singh, Push (1998). Failure-directed reformulation (M.Eng. thesis).

http://web.media.mit.edu/~push/reformulation.ps

 

7.5         Lexical semantics

 

Fellbaum, Christiane (Ed.). (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press.

http://www.cogsci.princeton.edu/~wn/papers/

 

Jackendoff, R. (1983). Semantics and cognition. Cambridge, MA, MIT Press.

 

Lyons, J. (1977). Semantics (Volumes I and II). Cambridge: Cambridge University Press.

 

Mel'cuk, Igor & Polguere, Alain (1987). A formal lexicon in the meaning-text theory (or How to do lexica with words). Computational Linguistics, 13(3-4), 261-275.

 

Pustejovsky, J. (1995). The generative lexicon. Cambridge, MA: MIT Press.

 

7.6         Connectionism

 

Minsky, Marvin, & Papert, Seymour (1988). Perceptrons (Expanded edition). Cambridge, MA: MIT Press.

Miikkulainen, R. (1993). Subsymbolic natural language processing: An integrated model of scripts, lexicon, and memory. Cambridge, MA: MIT Press.

Sun, Ron (1994). Integrating rules and connectionism for robust commonsense reasoning. New York: Wiley.

 

7.7         Situated action

 

Agre, Philip E. (1997). Computation and human experience. Cambridge University Press.

 

Suchman, Lucy A. (1987). Plans and situated action. Cambridge University Press.

8           Realms of common sense

 

8.1         Time

 

Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.

 

Allen, J. F. (1984). Towards a general theory of action and time, Artificial Intelligence 23, 123-154.

 

Allen, J. F., & Hayes, P. J. (1985). A common-sense theory of time. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 528-531.

 

Allen, J. F. (1991). Time and time again: The many ways to represent time. International Journal of Intelligent Systems 6(4), 341-356.

http://www.cs.rochester.edu/u/james/

(download ps)

 

Allen, J. F. (1991). Planning as temporal reasoning. Proceedings of 2nd Principles of Knowledge Representation and Reasoning, San Mateo, CA: Morgan Kaufmann.

http://www.cs.rochester.edu/u/james/kr91.pdf

 

ter Meulen, A. G. B. (1995). Representing time in natural language. Cambridge, MA: MIT Press.

 

8.2         Space

 

Davis, Ernest (1986). Representing and acquiring geographic knowledge. San Mateo, CA: Morgan Kaufman.

 

Davis, Ernest (1991). Lucid representations (Technical Report 565). Computer Science Department, New York University.

http://citeseer.nj.nec.com/davis94lucid.html

 

Davis, Ernest (1995). A highly expressive language of spatial constraints (Technical Report 714). Computer Science Department, New York University.

ftp://cs.nyu.edu/pub/tech-reports/tr714.ps.gz

 

Kuipers, B. J. (1978). Modeling spatial knowledge. Cognitive Science, 2, 129-153.

http://citeseer.nj.nec.com/kuipers78modeling.html

 

Kuipers, B. J. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119, 191-233.

http://citeseer.nj.nec.com/kuipers00spatial.html

 

Mukerjee, Amitabha (1998). Neat vs. scruffy: A survey of computational models for spatial expressions. In Representation and processing of spatial expressions.
http://citeseer.nj.nec.com/250615.html

 

8.3         Physics

 

Hayes, P. J. (1979). Naive physics manifesto. Expert Systems in the Microelectronic Age. Edinburgh: Edinburgh University Press.

 

Hayes, P. J. (1985). The second naive physics manifesto. In J. R. Hobbs & R. C. Moore (Eds.), Formal theories of the commonsense world. Norwood, NJ: Ablex.

 

Hayes, P. J. (1985). Naive physics I: Ontology for liquids. In J. R. Hobbs & R. C. Moore (Eds.), Formal theories of the commonsense world. Norwood, NJ: Ablex.

 

Rieger, C., & Grinberg, M. (1977). The causal representation and simulation of physical mechanisms. Technical Report TR-495, Dept. of Computer Science, University of Maryland.

 

8.4         Egg cracking

 

Lifschitz, Vladimir (1998). Cracking an egg: An exercise in formalizing commonsense reasoning.

http://www.cs.utexas.edu/users/vl/mypapers/egg.ps

 

Morgenstern, Leora (2001). Mid-sized axiomatizations of commonsense problems: A case study in egg cracking. Studia Logica, 67, 333-384.

http://www.kluweronline.com/issn/0039-3215

 

Shanahan, Murray (1998). A logical formalisation of Ernie Davis's egg cracking problem.

http://www.dcs.qmw.ac.uk/~mps/egg_murray.ps.Z

 

8.5         Frames and scripts

 

Fillmore, C. (1968). The case for case. In E. Bach and R. Harms (Eds.), Universals in linguistic theory. New York: Holt, Reinhart and Winston.

 

Minsky, Marvin (1974). A framework for representing knowledge (AI Laboratory Memo 306). Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

ftp://publications.ai.mit.edu/ai-publications/0-499/AIM-306.ps

http://www.media.mit.edu/~minsky/papers/Frames/frames.html

 

Mueller, Erik T. (1999). A database and lexicon of scripts for ThoughtTreasure.CogPrints cog00000555.

http://www.signiform.com/tt/htm/script.htm

 

Schank, R. C., and Abelson, R. P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum.

 

Wilks, Yorick (1975). A preferential, pattern-seeking, semantics for natural language inference. Artificial Intelligence. 6(1), 53-74.

 

8.6         Plans and Goals

 

Allen, James F., Kautz, Henry A., Pelavin, Richard N., & Tenenberg, Josh D. (1991). Reasoning about plans. San Mateo, CA: Morgan Kaufmann.

 

Schank, R. C., and Abelson, R. P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum.

 

Schank, Roger C., & Riesbeck, Christopher K. (1981). Inside computer understanding. Hillsdale, NJ: Erlbaum.

 

8.7         Beliefs, desires, and intentions

 

Bratman, M. E., Israel, D. J., and Pollack, M. E. (1988). Plans and resource-bounded practical reasoning. Computational Intelligence, 4(4).

http://citeseer.nj.nec.com/bratman88plans.html

 

Cohen, Philip R., and Levesque, Hector J. (1990). Intention is choice with commitment. Artificial Intelligence, 42, 213-261.

 

Fagin, Ronald, Halpern, Joseph Y., Moses, Yoram, & Vardi, Moshe Y. (1995). Reasoning About Knowledge. Cambridge, MA: MIT Press.

 

Halpern, J. and Moses, Y. (1984). Knowledge and common knowledge in a distributed environment, Proceedings of the Third ACM Symposium on Principles of Distributed Computing, 50-61. New York: ACM.

http://citeseer.nj.nec.com/halpern84knowledge.html

 

Lakemeyer, G. and Levesque, H. J. (1998). AOL: a logic of acting, sensing, knowing, and only knowing, Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning. San Mateo, CA: Morgan Kaufmann.

 

Rao, A.S., & Georgeff, M. P. (1991). Modeling rational agents within a BDI-architecture. In J. Allen, R. Fikes, and E. Sandewall (Eds.), Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning (pp. 473-484). San Mateo, CA: Morgan Kaufmann.

http://citeseer.nj.nec.com/rao91modeling.html

 

Smedslund, Jan (1997). The structure of psychological common sense. Mahwah, NJ: Erlbaum.

 

8.8         Interpersonal relations

 

Heider, Fritz (1958). The psychology of interpersonal relations. Hillsdale, NJ: Erlbaum.

 

Schank, R. C., and Abelson, R. P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum.

 

8.9         Emotions

 

Dyer, Michael G. (1987). Emotions and their computations: Three computer models. Cognition and Emotion, 1(3), 323-347.

 

Minsky, Marvin (forthcoming). The Emotion Machine.

http://web.media.mit.edu/~minsky/E1/eb1.html

http://web.media.mit.edu/~minsky/E2/eb2.html

http://web.media.mit.edu/~minsky/E3/eb3.html

http://web.media.mit.edu/~minsky/E4/eb4.html

http://web.media.mit.edu/~minsky/E5/eb5.html

 

O'Rorke, P., and Ortony, A. (1994). Explaining emotions. Cognitive science, 18(2), 283-323.

Ortony, A., Clore, G. L., and Collins, A. (1988). The cognitive structure of emotions. New York: Cambridge University Press.

 

Sloman, Aaron (2001). Beyond shallow models of emotion. Cognitive Processing, 1(1).

http://www.cs.bham.ac.uk/research/cogaff/0-INDEX00-05.html#74

 

8.10     Personality

 

Carbonell, J. (1980). Towards a process model of human personality traits. Artificial Intelligence, 15, 49-74.

 

8.11     Plot structures

 

Lehnert, W. G. (1981). Plot units and narrative summarization. Cognitive Science, 4, 293-331.

 

Rumelhart D. E. (1975) Notes on a schema for stories. In D. G. Bobrow & A. M. Collins (Eds.) Representation and understanding: Studies in cognitive science, pp. 211-236. New York: Academic Press.

 

Wilensky R. (1982) Points: A theory of the structure of stories in memory. In W.G. Lehnert & M. H. Ringle (Eds.) Strategies for natural language processing, pp. 345-374. Hillsdale, NJ: Erlbaum.

 

8.12     Economics

 

Riesbeck, Christopher, & Martin, Charles (1984). Direct memory access parsing (Technical Report 354). Computer Science Department, Yale University.

 

8.13     Vision

 

Aloimonos, J. (1989) Integration of visual modules. San Diego: Academic Press.

 

Arnheim, Rudolf (1969). Visual Thinking. Berkeley, CA: University of California.


Bobick, Aaron, & Pinhanez, Claudio (1995). Using approximate models as source of contextual Information for vision processing. Proceedings of the Workshop on Context-Based Vision, 13-21.

http://www.research.ibm.com/people/p/pinhanez/publications/357.pdf

Bobick, Aaron, & Intille, S. (1995). Exploiting contextual information for tracking by using closed worlds. Proceedings of the Workshop on Context-Based Vision, 87-98.

 

Buxton, H., & Howarth, R. (1996). Watching behaviour: The role of context and learning. In International Conference on Image Processing, Lausanne, Switzerland.

 

Buxton, H., & Gong, S. (1995). Visual surveillance in a dynamic and uncertain world. Artificial Intelligence, 78, 371-405.

 

Casati, Roberto, & and Varzi, Achille C. (1994). Holes and Other Superficialities. Cambridge, MA: MIT Press.

 

Crowley, J. L., & Christensen, H. (1993). Vision as process. Berlin: Springer-Verlag.

 

Garvey, D. (1976). Perceptual strategies for purposive vision (Technical note 117). Artificial Intelligence Center, SRI International.

 

Gong, Leiguang (2001). Image analysis as context-based reasoning. Proceedings of the ISCA Tenth International Conference on Intelligent Systems, 130-134.

 

Gong, L., & Kulikowski, C. (1995). Composition of Image Analysis Processes through Object-Centered Hierarchical Planning. IEEE Transactions on Pattern Recognition and Machine Intelligence, 17, 997-1009.

 

Ibrahim, Ahmed E. (2001). An intelligent framework for image understanding.
http://www.engr.uconn.edu/%7Eibrahim/publications/image.html

 

Marr, David (1982). Vision. San Francisco: W.H. Freeman.

 

Minsky, Marvin (1974). A framework for representing knowledge (AI Laboratory Memo 306). Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

ftp://publications.ai.mit.edu/ai-publications/0-499/AIM-306.ps

http://www.media.mit.edu/~minsky/papers/Frames/frames.html

 

Oliver, Nuria (2000). Towards perceptual intelligence: Statistical modeling of human individual and interactive hehaviors (PhD thesis).

http://whitechapel.media.mit.edu/people/nuria/thesis/thesis.pdf

 

Pinker, Steven (Ed.) (1988). Visual cognition. Cambridge, MA: MIT Press.


 

Rosenthal, D., & and Bajscy, R. (1984). Visual and conceptual hierarchy: a paradigm for studies of automated generation of recognition strategies. IEEE Transactions on Pattern Recognition and Machine Intelligence, 5, 319-324.

 

Schank, Roger C., & Fano, Andrew E. (995). Memory and expectations in learning, language, and visual understanding. Artificial Intelligence Review, 9, 261-271.

 

Selfridge, P. (1981). Reasoning about success and failure in aerial image understanding (PhD thesis). University of Rochester.

 

Socher, G., Sagerer, G., Kummert, F., & and Fuhr, T. (1996). Talking about 3D scenes: Integration of image and speech understanding in a hybrid distributed system. In International Conference on Image Processing, Lausanne, Switzerland.

 

Srihari, R. K. (1995). Linguistic context in vision. In Workshop on Context-based Vision. IEEE Press.

Stark, L., & Bowyer, K. (1995). Functional context in vision. In Workshop on Context-Based Vision. IEEE Press.

Strat, T. M., & Fischler, M. A. (1991). Context-based vision: Recognising objects using both 2D and 3D imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 1050-1065.


Strat, T. M., & and Fischler, M. A. (1995). The role of context in computer vision. In Workshop on Context-based Vision. IEEE Press.

Waltz, David L., & Boggess, Lois (1979). Visual analog representations for natural language understanding. Proceedings of the Sixth International Joint Conference on Artificial Intelligence.

 

8.14     Gesture


Cassell, Justine (1995). Speech, action and gestures as context for ongoing task-oriented talk. Proceedings of AAAI Fall Symposium on Embodied Language and Action, 20-25.

 

8.15     Metaplanning and reflection

 

Craig, Iain D. (1998). Programs that model themselves.

 

Doyle, J. (1980). A model for deliberation, action, and introspection (Technical Report 581). Cambridge, MA: Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

 

Gordon, Andrew (2001). The representational requirements of strategic planning.

http://www.cs.nyu.edu/faculty/davise/commonsense01/final/Gordon.pdf

 

McCarthy, John (1995). Making robots conscious of their mental states. In AAAI Spring Symposium on Representing Mental States and Mechanisms.

http://www-formal.stanford.edu/jmc/consciousness.html

 

Smith, B. (1982). Reflection and semantics in a procedural language (Technical Report 272). Cambridge, MA: Laboratory for Computer Science, Massachusetts Institute of Technology.

 

Stroulia, E., & and Goel, A. (1995). Functional Representation and Reasoning in Reflective Systems. Journal of Applied Intelligence, Special Issue on Functional Reasoning, 9(1).

 

Voss, Angi, & Karbach, Werner (1998). Building competent reflective systems.

http://citeseer.nj.nec.com/342258.html

 

Wilensky, R. (1983). Planning and understanding: A computational approach to human reasoning. Reading, MA: Addison-Wesley.

 

8.16     Context

 

Guha, Ramanathan (1995). Contexts: A formalization and some applications (PhD thesis).

http://www.guha.com/guha-thesis.ps

 

Lenat, D. (1998). The dimensions of context-space.

http://www.cyc.com/context-space.doc

 

McCarthy, John (1993). Notes on formalizing context. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence.

http://citeseer.nj.nec.com/318177.html

 

8.17     Causality

 

Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge University Press.

 

8.18     Creativity and invention

 

Gelernter, David (1994). The muse in the machine: Computerizing the poetry of human thought. New York: Free Press.

 

Dyer, Michael G., Flowers, Margot, & Hodges, Jack (1986). Edison: An engineering design invention system operating naively.

ftp://ftp.cs.ucla.edu/tech-report/198_-reports/860087.pdf

 

Mueller, Erik T. and Dyer, Michael G. (1985). Towards a computational theory of human daydreaming. Proceedings of the Seventh Annual Conference of the Cognitive Science Society, 120-129.

http://www.panix.com/~erik/pubs/ddcogsci.htm

 

Turner, Scott (1994). The creative process. Hillsdale, NJ: Erlbaum.

9           Acquisition of common sense

 

9.1         Distributed human projects

 

Stork, David (1999). The OpenMind initiative. IEEE Intelligent Systems & their applications, 14(3), 19-20.

 

Singh, Push, et al. (in submission). Open Mind Common Sense: Knowledge acquisition from the general public.

 

9.2         Sketching

 

Forbus, K. D., Ferguson, R. W., & Usher, J. M. (2000). Towards a computational model of sketching, Proceedings of the International Conference on Intelligent User Interfaces. Sante Fe, NM.

 

9.3         Learning structural representations

 

Pazzani, M., & Kibler, D. (1992). The Utility of Knowledge in Inductive Learning. Machine Learning, 9, 57-94.

 

Quinlan, J. R., & Cameron-Jones, R. M. (1993). FOIL: A midterm report. In Pavel B. Brazdil, editor, Machine Learning: ECML-93. Vienna, Austria.

 

Quinlan, J. R., & Cameron-Jones, R. M. (1995). Induction of logic programs: FOIL and related systems. New Generation Computing, 13, 287-312.

 

9.4         Sensory grounded learning

 

Cohen, Paul R, Atkin, Marc S, Oates, Tim, & Beal, Carole R. (1997). Neo: Learning conceptual knowledge by sensorimotor interaction with an environment. Proceedings of the First International Conference on Autonomous Agents, 170-177.

http://citeseer.nj.nec.com/cohen97neo.html

 

Finney, Sarah, Hernandez, Natalia, Oates, Tim, & Kaelbling, Leslie Pack (2001). Learning in worlds with objects. Working Notes of the AAAI Stanford Spring Symposium on Learning Grounded Representations.

http://citeseer.nj.nec.com/419524.html

 

Narayanan, S. (1997). Talking the talk is like walking the walk.

http://www.icsi.berkeley.edu/~snarayan/sub.ps

 

Roy, Deb (in press). Learning visually grounded words and syntax of natural spoken language. Evolution of Communication.

 

Schmill, Matthew D., Oates, Tim, & Cohen, Paul R. (2000). Learning planning operators in real-world, partially observable environments. Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling, 246-253.

http://www-eksl.cs.umass.edu/papers/schmill-aips00.ps

 

Siskind, Jeffrey M. (1994). Grounding language in perception. Artificial Intelligence Review, 8, 371-391.

 

Siskind, Jeffrey M. (2001). Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic. Journal of Artificial Intelligence Research, 15, 31-90.

10       Applications of common sense

 

10.1     Context-aware agents

 

Lenat, Douglas, & Guha, Ramanathan (1994). Ideas for Applying CYC.

http://www.cyc.com/tech-reports/act-cyc-407-91/act-cyc-407-91.html

 

Lieberman, Henry, & Selker, Ted (2000). Out of context: Computer systems that adapt to, and learn from, context. IBM Systems Journal, 39(3,4), 617-632.

http://www.research.ibm.com/journal/sj/393/part1/lieberman.pdf

 

McCarthy, John (1990). Some expert systems need common sense.

http://www-formal.stanford.edu/jmc/someneed.html

 

Mueller, Erik T. (2000). A calendar with common sense. Proceedings of the 2000 International Conference on Intelligent User Interfaces, 198-201. New York: ACM.

http://lieber.www.media.mit.edu/people/lieber/IUI/Mueller/Mueller.html

 

Mueller, Erik T. (2001). Machine-understandable news for e-commerce and  web applications. Proceedings of the 2001 International Conference on Artificial Intelligence, 1113-1119. CSREA Press.

http://www.panix.com/~erik/pubs/newsund1.htm

 

Picard, Rosalind (1997). Affective Computing. Cambridge, MA: MIT Press.

 

Singh, Push (2002). The public acquisition of commonsense knowledge.  Proceedings of AAAI Spring Symposium on Acquiring (and Using) Linguistic (and World) Knowledge for Information Access.  Palo Alto, CA: AAAI.

http://openmind.media.mit.edu/pack.html

 

10.2      The Semantic Web

 

Fensel, Dieter, & Musen, Mark A. (Eds.). (2001). The semantic web. IEEE Intelligent Systems, 16(2), 24-79.
http://computer.org/intelligent/Is.zip

 

Berners-Lee, Tim, Hendler, James, & Lassila, Ora (2001). The Semantic Web. Scientific American, 284(5), 34-43.

http://www.scientificamerican.com/2001/0501issue/0501berners-lee.html

 

Berners-Lee, Tim (1998). Semantic Web Road map.

http://www.w3.org/DesignIssues/Semantic.html

 

Berners-Lee, Tim (1998). What the Semantic Web can represent.

http://www.w3.org/DesignIssues/RDFnot.html

 

10.3     Story understanding

 

Charniak, Eugene (1972) Toward a model of children's story comprehension (AI Laboratory Technical Report 266). Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

ftp://publications.ai.mit.edu/ai-publications/0-499/AITR-266.ps

ftp://publications.ai.mit.edu/ai-publications/pdf/AITR-266.pdf

 

Duchan, Judith F., Bruder, Gail A., & Hewitt, Lynne E. (1995). Deixis in narrative. Hillsdale, NJ: Erlbaum.

 

Dyer, Michael G. (1983). In-depth understanding. Cambridge, MA: MIT Press.


Lehnert, Wendy (1978). The process of question answering. Hillsdale, NJ: Erlbaum.

 

Mueller, Erik T. (2002). Story understanding. In Encyclopedia of Cognitive Science. London: Nature Publishing Group.

 

Ram, Ashwin (1987). AQUA: asking questions and understanding answers. Proceedings of the Sixth Annual National Conference on Artificial Intelligence, 312-316.

 

Schank, Roger (1972). Conceptual dependency: A theory of natural language understanding. Cognitive Psychology, 3, 552-631.

 

Schank, R. C., and Abelson, R. P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum.

 

Schank, R. C., & Rieger, C. J. (1974). Inference and the computer understanding of natural language. Artificial Intelligence, 5, 373-412.

 

10.4     Robots

 

Shapiro, Stuart C., Amir, Eyal, Grosskreutz, Henrik, Randell, David, & Soutchanski, Mikhail (2001). Common sense and embodied agents: A panel discussion. Fifth Symposium on Logical Formalizations of Commonsense Reasoning.

http://www.cs.nyu.edu/faculty/davise/commonsense01/final/panel.pdf

 

Amir, Eyal, & Maynard-Reid, Pedrito II. (2001). LiSA: A robot driven by logical subsumption. Fifth Symposium on Logical Formalizations of Commonsense Reasoning.

http://www.cs.nyu.edu/faculty/davise/commonsense01/final/amir.pdf

 

Stopp, Eva, Gapp, Klaus-Peter, Herzog, Gerd, Längle , Thomas, & Lüth , Tim C. (1994). Utilizing spatial relations for natural language access to an autonomous mobile robot. http://wwwipr.ira.uka.de/internal/detailed_publication.php?id=974907079

 

Längle, Thomas, Lüth, Tim C., Stopp, Eva, Herzog, Gerd, & and Kamstrup, Gjertrud (1995). KANTRA – A natural language interface for intelligent robots. In Rembold et al. (Eds.), Intelligent Autonomous Systems (pp. 357-364). IOS Press.

http://wwwipr.ira.uka.de/internal/detailed_publication.php?id=975423485

http://wwwipr.ira.uka.de/internal/download.php?id=975423485&filetype=pdf

11       Results from psychology and neuroscience

 

Beeman, Mark (1998). Coarse semantic coding and discourse comprehension. In Right hemisphere language comprehension. Mahwah, NJ: Erlbaum.

 

Burgess, Curt, & Simpson, Greg B. (1988). Cerebral hemispheric mechanisms in the retrieval of ambiguous word meanings. Brain and Language, 33, 86-103.
http://locutus.ucr.edu/abstracts/88-bs-cere.html

 

Caramazza, Alfonso (1998). The interpretation of semantic category-specific deficits: What do they reveal about the organization of conceptual knowledge in the brain? Neurocase, 4, 265-272.

 

Clark, Herbert H. (1977). Bridging. In Thinking: Readings in Cognitive Science.

 

Goldman, Susan R., Graesser, Arthur C., & van den Broek, Paul (1999). Narrative comprehension, causality, and conherence. Mahwah, NJ: Erlbaum.


Graesser, Arthur C., Singer, Murray, and Trabasso, Tom (1994). Constructing inferences during narrative text comprehension. Psychological Review. 101(3):371-395.

 

Johnson-Laird, Philip N. (1993). Human and machine thinking. Hillsdale, NJ: Erlbaum.

 

Johnson-Laird, Philip N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.

 

Landauer, Thomas K. (1986). How much do people remember? Some estimates of the quantity of learned information in long-term memory. Cognitive Science, 10, 477-493.


McKoon, Gail, & Ratcliff, Roger (1992). Inference during reading. Psychological Review. 99(3), 440-466.


McKoon, Gail, & Ratcliff, Roger (1986). Inferences about predictable events. Journal of Experimental Psychology: Learning, Memory, and Cognition. 12(1), 82-91.

 

Pinker, Steven (1997). How the mind works. New York: Norton.


 

Rapaport, David (1951). Organization and pathology of thought. New York: Columbia University Press.

 

St. George, Marie, Mannes, Suzanne, and Hoffman, James E. (1997). Individual differences in inference generation: An ERP analysis. Journal of Cognitive Neuroscience, 9(6), 776-787.

 

Tanenhaus, Michael K., Spivey-Knowlton, Michael J., Eberhard, Kathleen M., & Sedivy, Julie C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632-1634.

Van Petten, Cyma, & Kutas, Marta (1990). Interactions between sentence context and word frequency in event-related brain potentials. Memory & Cognition, 18(4):380-393.

12       Popular books


Joseph, Lawrence E. (1994). Common sense. Reading, MA: Addison-Wesley.

McCorduck, P. (1979). Machines who think. San Francisco: W. H. Freeman.

Stork, David G. (Ed.) (1997). HAL’s legacy. Cambridge, MA: MIT Press.

http://mitpress.mit.edu/e-books/Hal/contents.html

(partial ebook)

13       Web resources

 

Commonsense problem page

http://www-formal.stanford.edu/leora/cs/

 

OpenCyc

http://www.opencyc.org

 

Open Mind Common Sense

http://www.openmind.org/commonsense

 

ThoughtTreasure

http://www.signiform.com/tt/htm/tt.htm

 

WordNet

http://www.cogsci.princeton.edu/~wn/