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