Commonsense bibliography
Push Singh and Erik T. Mueller
Contributions from: Barbara Barry, Leiguang Gong, Hugo Liu, Stefan Marti, Dustin Smith,
Commonsense Overviews
Minsky, Marvin (2000). Commonsense-based interfaces. Communications of the ACM, 43(8), 67-73. [1] [2]
Minsky, Marvin (2006). The Emotion Machine. New York: Simon & Schuster [3] [4]
Mueller, Erik T. (2006). Commonsense reasoning. San Francisco: Morgan Kaufmann/Elsevier. [5] [6]
Singh, Push (2002). The Open Mind Common Sense project. [7] [8]
Davis, Ernest (1998). The naive physics perplex. AI Magazine, 19(4), 51-79. [9]
Classics
McCarthy, John (1959). Programs with common sense. [10]
McCarthy, John (1990). Formalizing common sense. Norwood, NJ: Ablex. [11]
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. [12] [13]
Minsky, Marvin (1986). The society of mind. New York: Simon and Schuster.
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. [14]
Giunchiglia, Fausto (1995). An epistemological science of common sense. Book review of John McCarthy's Formalizing Common Sense. Artificial Intelligence, 77, 371-392. [15]
Hayes, P. J. (1977). In defence of logic. Proceedings of the Fifth International Joint Conference on Artificial Intelligence.
McCarthy, John. The well-designed child. [16]
McCarthy, John (1996). From here to human-level AI. [17]
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. [18]
Nilsson, Nils. J. (1991). Logic and artificial intelligence. Artificial Intelligence, 47, 31-56.
Cyc
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. [19]
Lenat, Douglas (1995). CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11).
Lenat, Douglas (1997). Cyc Upper Ontology. [20]
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.
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? [21]
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. [22]
Pratt, Vaughan (1994). Cyc report. [23] [24]
Stefik, Mark J., & Smoliar, Stephen W. (1993). The commonsense reviews. Artificial Intelligence, 61, 37-179. [25] [26]
Architectures for common sense
The society of mind / Emotion Machine
Minsky, Marvin (1986). The society of mind. New York: Simon and Schuster.
Minsky, Marvin (forthcoming). The Emotion Machine. [27] [28] [29] [30] [31] [32] [33] [34]
Minsky, Marvin (1981). Jokes and their relation to the cognitive unconscious. In Vaina and Hintikka (Eds.), Cognitive Constraints on Communication. Reidel. [35]
Minsky, Marvin (1991). Logical vs. analogical or symbolic vs. connectionist or neat vs. scruffy. AI Magazine, Summer 1991. [36]
Minsky, Marvin (1994). Negative expertise. International Journal of Expert Systems, 7(1), 13-19. [37]
Singh, Push (2005). The EM-One. PhD Thesis. [38]
The cognition and affect project
Beaudoin, Luc P. (1994). Goal processing in autonomous agents. [39]
Sloman, Aaron (1981). Why robots will have emotions. Proceedings of the Seventh International Joint Conference on Artificial Intelligence. [40]
Sloman, Aaron (1998). Damasio, Descartes, alarms and meta-management. [41]
Sloman, Aaron (1998). What?s an AI toolkit for? [42]
Blackboard systems
Carver, N., & Lesser, V. (1994). Evolution of blackboard control architectures. Expert Systems with Applications 7, 1-30. [43]
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.
Other heterogeneous architectures
Mueller, Erik T. (1990). Daydreaming in humans and machines: A computer model of the stream of thought. Norwood, NJ: Ablex/Intellect. [44]
Mueller, Erik T. (1998). Natural language processing with ThoughtTreasure. New York: Signiform. [45]
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. [46]
Soar
Laird, J.E., & Rosenbloom, P.S. (1996). The evolution of the Soar cognitive architecture. In T. Mitchell (Ed.) Mind Matters. [47]
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). [48]
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.
Other unified architectures
Anderson, John R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Logical formalisms for commonsense reasoning
Overviews
Davis, Ernest (1990). Representations of Commonsense Knowledge. San Mateo, CA: Morgan Kaufmann. [49]
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. [50]
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. [51]
Reiter, Raymond (2001). Knowledge in action: Logical foundations for specifying and implementing dynamical systems. Cambridge, MA: MIT Press.
Event calculus
Kowalski, R. & Sergot, M. J. (1986). A logic-based calculus of events. New Generation Computing, 4, 67-95.
Mueller, Erik T. (2006). Commonsense reasoning. San Francisco: Morgan Kaufmann/Elsevier. [52] [53]
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. [54]
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. [55]
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. [56]
McCain, N., & Turner, H. (1997). Causal theories of action and change. Proceedings of the Fourteenth National Conference on Artificial Intelligence. [57]
McCain, N., & Turner, H. (1995). A causal theory of ramifications and qualifications. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. [58]
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. [59]
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.
Circumscription
Lifschitz, Vladimir (1994). Circumscription. In Handbook of logic in AI and logic programming (Volume 3). Oxford University Press. [60]
McCarthy, John (1980). Circumscription?A form of non-monotonic reasoning. Journal of Artificial Intelligence, 13, 27-39. [61]
Other mechanisms for common sense
Analogy and metaphor
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1990). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1-63. [62]
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. [63]
Gentner, D. (2001). Spatial metaphors in temporal reasoning. In M. Gattis (Ed.), Spatial schemas in abstract thought (pp. 203-222). Cambridge, MA: MIT Press. [64]
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. [65]
Lakoff G. & Johnson M. (1990) Metaphors we live by. University of Chicago Press.
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.
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. [66]
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.
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). [67]
Smith, Dustin (2006). Problem Reformation: Taxonomy (draft, tech notes) [68]
Lexical semantics
Fellbaum, Christiane (Ed.). (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press. [69]
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.
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.
Situated action
Agre, Philip E. (1997). Computation and human experience. Cambridge University Press.
Suchman, Lucy A. (1987). Plans and situated action. Cambridge University Press.
Realms of common sense
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. [70]
Allen, J. F. (1991). Planning as temporal reasoning. Proceedings of 2nd Principles of Knowledge Representation and Reasoning, San Mateo, CA: Morgan Kaufmann. [71]
ter Meulen, A. G. B. (1995). Representing time in natural language. Cambridge, MA: MIT Press.
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. [72]
Davis, Ernest (1995). A highly expressive language of spatial constraints (Technical Report 714). Computer Science Department, New York University. [73]
Jackendoff, Jay and Landau, Barbara (1993). `What' and `Where' in Spatial Language and Spatial Cognition Behavioral and Brain Sciences, 16(2), pp. 217-265.
Kuipers, B. J. (1978). Modeling spatial knowledge. Cognitive Science, 2, 129-153. [74]
Kuipers, B. J. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119, 191-233. [75]
Mukerjee, Amitabha (1998). Neat vs. scruffy: A survey of computational models for spatial expressions. In Representation and processing of spatial expressions. [76]
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.
Egg cracking
Lifschitz, Vladimir (1998). Cracking an egg: An exercise in formalizing commonsense reasoning. [77]
Morgenstern, Leora (2001). Mid-sized axiomatizations of commonsense problems: A case study in egg cracking. Studia Logica, 67, 333-384. [78]
Shanahan, Murray (1998). A logical formalisation of Ernie Davis's egg cracking problem. [79]
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. [80] [81]
Mueller, Erik T. (1999). A database and lexicon of scripts for ThoughtTreasure.CogPrints cog00000555. [82]
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.
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.
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). [83]
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. [84]
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. [85]
Smedslund, Jan (1997). The structure of psychological common sense. Mahwah, NJ: Erlbaum.
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.
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 [86] [87] [88] [89] [90]
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). [91]
Personality
Carbonell, J. (1980). Towards a process model of human personality traits. Artificial Intelligence, 15, 49-74.
Plot structures & Stories
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.
Schank, RC. (1995) Tell Me a Story: Narrative and Intelligence. Northwestern University 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.
Economics
Riesbeck, Christopher, & Martin, Charles (1984). Direct memory access parsing (Technical Report 354). Computer Science Department, Yale University.
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. [92]
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. [93]
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. [94] [95]
Oliver, Nuria (2000). Towards perceptual intelligence: Statistical modeling of human individual and interactive hehaviors (PhD thesis). [96]
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.
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.
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. [97]
McCarthy, John (1995). Making robots conscious of their mental states. In AAAI Spring Symposium on Representing Mental States and Mechanisms. [98]
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. [99]
Wilensky, R. (1983). Planning and understanding: A computational approach to human reasoning. Reading, MA: Addison-Wesley.
Context
Guha, Ramanathan (1995). Contexts: A formalization and some applications (PhD thesis). [100]
Lenat, D. (1998). The dimensions of context-space. [101]
McCarthy, John (1993). Notes on formalizing context. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. [102]
Causality
Borchardt, Gary C (1993). Causal Reconstruction. AIM-1403. [103]
Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge University Press.
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. [104]
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. [105]
Turner, Scott (1994). The creative process. Hillsdale, NJ: Erlbaum.
Acquisition of common sense
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.
von Ahn, Luis et al (2006). Verbosity: A Game for Collecting Common-Sense Facts. CHI. [106]
von Ahn, Luis et al (2004). Labeling images with a computer game. CHI. [107]
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.
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.
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. [108]
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. [109]
Narayanan, S. (1997). Talking the talk is like walking the walk. [110]
Roy, Deb. 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. [111]
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.
Applications of common sense
Context-aware agents
Lenat, Douglas, & Guha, Ramanathan (1994). Ideas for Applying CYC. [112]
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. [113]
McCarthy, John (1990). Some expert systems need common sense. [114]
Mueller, Erik T. (2000). A calendar with common sense. Proceedings of the 2000 International Conference on Intelligent User Interfaces, 198-201. New York: ACM. [115]
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. [116]
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. [117]
The Semantic Web
Fensel, Dieter, & Musen, Mark A. (Eds.). (2001). The semantic web. IEEE Intelligent Systems, 16(2), 24-79. [118]
Berners-Lee, Tim, Hendler, James, & Lassila, Ora (2001). The Semantic Web. Scientific American, 284(5), 34-43. [119]
Berners-Lee, Tim (1998). Semantic Web Road map. [120]
Berners-Lee, Tim (1998). What the Semantic Web can represent. [121]
Story understanding
See Erik Muller's Story Understanding Resources
Charniak, Eugene (1972) Toward a model of children's story comprehension (AI Laboratory Technical Report 266). Artificial Intelligence Laboratory, Massachusetts Institute of Technology. [122] [123]
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.
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. [124]
Amir, Eyal, & Maynard-Reid, Pedrito II. (2001). LiSA: A robot driven by logical subsumption. Fifth Symposium on Logical Formalizations of Commonsense Reasoning. [125]
Stopp, Eva, Gapp, Klaus-Peter, Herzog, Gerd, L?ngle , Thomas, & L?th , 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
L'ngle, Thomas, L?th, 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. [126] [127]
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. [128]
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.
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. (partial ebook)
Web resources
Commonsense problem page [129]
OpenCyc [130]
Open Mind Common Sense [131]
ThoughtTreasure [132]
WordNet [133]
