References

Aamodt, A. and Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 7(1): 39-59.

Abelson, H. and DiSessa, A. (1981). Turtle Geometry: The Computer as a Medium for Exploring Mathematics. MIT Press, Cambridge, MA.

Abramson, H. and Rogers, M.H. (Eds.) (1989). Meta-Programming in Logic Programming. MIT Press, Cambridge, MA.

Agre, P.E. (1995). Computational research on interaction and agency. Artificial Intelligence, 72: 1-52.

Aha, D.W., Marling, C., and Watson, I. (Eds.) (2005). The Knowledge Engineering Review, special edition on case-based reasoning, volume 20 (3). Cambridge University Press. http://journals.cambridge.org/action/displayIssue?jid=KER&volumeId=20&issueId=03.

Albus, J.S. (1981). Brains, Behavior and Robotics. BYTE Publications, Peterborough, NH.

Allais, M. and Hagen, O. (Eds.) (1979). Expected Utility Hypothesis and the Allais Paradox. Reidel, Boston, MA.

Allen, J., Hendler, J., and Tate, A. (Eds.) (1990). Readings in Planning. Morgan Kaufmann, San Mateo, CA.

Anderson, M. and Leigh Anderson, S.L. (2007). Machine ethics: Creating an ethical intelligent agent. AI Magazine, 28(4): 15-26.

Andrieu, C., de Freitas, N., Doucet, A., and Jordan, M.I. (2003). An introduction to MCMC for machine learning. Machine Learning, 50(1-2): 5-43.

Antoniou, G. and van Harmelen, F. (2008). A Semantic Web Primer. MIT Pres, Cambridge, MA, 2nd edition.

Apt, K. and Bol, R. (1994). Logic programming and negation: A survey. Journal of Logic Programming, 19,20: 9-71.

Aristotle (350 B.C.). Categories. Translated by E. M. Edghill, http://www.classicallibrary.org/Aristotle/categories/.

Asimov, I. (1950). I, Robot. Doubleday, Garden City, NY.

Bacchus, F. and Grove, A. (1995). Graphical models for preference and utility. In Uncertainty in Artificial Intelligence (UAI-95), pp. 3-10.

Bacchus, F., Grove, A.J., Halpern, J.Y., and Koller, D. (1996). From statistical knowledge bases to degrees of belief. Artificial Intelligence, 87(1-2): 75-143. http://www.cs.toronto.edu/ fbacchus/Papers/BGHKAIJ96.ps.

Bacchus, F. and Kabanza, F. (1996). Using temporal logic to control search in a forward chaining planner. In M. Ghallab and A. Milani (Eds.), New Directions in AI Planning, pp. 141-153. ISO Press, Amsterdam.

Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, NY.

Ballard, B.W. (1983). The *-minimax search procedure for trees containing chance nodes. Artificial Intelligence, 21(3): 327-350.

Baum, E.B. (2004). What is Thought? MIT Press.

Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53: 370-418. Reprinted in Biometrika 45, 298-315, 1958. Reprinted in S. J. Press, Bayesian Statistics, 189-217, Wiley, New York, 1989.

Beckett, D. and Berners-Lee, T. (2008). Turtle - terse RDF triple language. http://www.w3.org/TeamSubmission/turtle/.

Bell, J.L. and Machover, M. (1977). A Course in Mathematical Logic. North-Holland, Amsterdam.

Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.

Bernardo, J.M. and Smith, A.F.M. (1994). Bayesian Theory. Wiley.

Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web: A new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May: 28-37. http://www.sciam.com/article.cfm?id=the-semantic-web.

Bertelè, U. and Brioschi, F. (1972). Nonserial dynamic programming, volume 91 of Mathematics in Science and Engineering. Academic Press.

Bertsekas, D.P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, Massachusetts. Two volumes.

Bertsekas, D.P. and Tsitsiklis, J.N. (1996). Neuro-Dynamic Programming. Athena Scientific, Belmont, Massachusetts.

Besnard, P. and Hunter, A. (2008). Elements Of Argumentation. MIT Press, Cambridge, MA.

Bishop, C.M. (1995). Neural Networks for Pattern Recognition. Oxford University Press, Oxford, England.

Bishop, C.M. (2008). Pattern Recognition and Machine Learning. Springer-Verlag, New York.

Blum, A. and Furst, M. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90: 281-300.

Bobrow, D.G. (1993). Artificial intelligence in perspective: a retrospective on fifty volumes of Artificial Intelligence. Artificial Intelligence, 59: 5-20.

Bobrow, D.G. (1967). Natural language input for a computer problem solving system. In M. Minsky (Ed.), Semantic Information Processing, pp. 133-215. MIT Press, Cambridge MA.

Boddy, M. and Dean, T.L. (1994). Deliberation scheduling for problem solving in time-constrained environments. Artificial Intelligence, 67(2): 245-285.

Bodlaender, H.L. (1993). A tourist guide through treewidth. Acta Cybernetica, 11(1-2): 1-21.

Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., and Poole, D. (2004). Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research, 21: 135-191. http://www.jair.org/abstracts/boutilier04a.html.

Boutilier, C., Dean, T., and Hanks, S. (1999). Decision-theoretic planning: Structual assumptions and computational leverage. Journal of Artificial Intelligence Research, 11: 1-94.

Bowen, K.A. (1985). Meta-level programming and knowledge representation. New Generation Computing, 3(4): 359-383.

Bowling, M. and Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2): 215-250.

Brachman, R.J. and Levesque, H.J. (Eds.) (1985). Readings in Knowledge Representation. Morgan Kaufmann, San Mateo, CA.

Brachman, R. and Levesque, H. (2004). Knowledge Representation and Reasoning. Morgan Kaufmann.

Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA.

Briscoe, G. and Caelli, T. (1996). A Compendium of Machine Learning, Volume 1: Symbolic Machine Learning. Ablex, Norwood, NJ.

Brooks, R.A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1): 14-23. Reprinted in [Shafer and Pearl (1990)].

Brooks, R.A. (1991). Intelligence without representation. Artificial Intelligence, 47: 139-159.

Brooks, R. (1990). Elephants don't play chess. Robotics and Autonomous Systems, 6: 3-15. http://people.csail.mit.edu/brooks/papers/elephants.pdf.

Bryce, D. and Kambhampati, S. (2007). A tutorial on planning graph based reachability heuristics. AI Magazine, 28(47-83): 1.

Buchanan, B. and Shortliffe, E. (Eds.) (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, MA.

Buchanan, B.G. and Feigenbaum, E.A. (1978). Dendral and meta-dendral: Their applications dimension. Artificial Intelligence, 11: 5-24.

Buchanan, B.G. (2005). A (very) brief history of artificial intelligence. AI Magazine, 26(4): 53-60.

Buntine, W. (1992). Learning classification trees. Statistics and Computing, 2: 63-73.

Burch, R. (2008). Charles Sanders Peirce. The Stanford Encyclopedia of Philosophy. http://plato.stanford.edu/archives/spr2008/entries/peirce/.

Campbell, M., Hoane Jr., A.J., and Hse, F.h. (2002). Deep blue. Artificial Intelligence, 134(1-2): 57-83. http://www.sciencedirect.com/science/article/B6TYF-43PHC49-2/1/325879d3cbf078187ea49a232e421ea9.

Castillo, E., Gutiérrez, J.M., and Hadi, A.S. (1996). Expert Systems and Probabilistic Network Models. Springer Verlag, New York.

Chapman, D. (1987). Planning for conjunctive goals. Artificial Intelligence, 32(3): 333-377.

Cheeseman, P. (1990). On finding the most probable model. In J. Shranger and P. Langley (Eds.), Computational Models of Scientific Discovery and Theory Formation, chapter 3, pp. 73-95. Morgan Kaufmann, San Mateo, CA.

Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., and Freeman, D. (1988). Autoclass: A Bayesian classification system. In Proc. Fifth International Conference on Machine Learning, pp. 54-64. Ann Arbor, MI. Reprinted in [Shavlik and Dietterich (1990)].

Cheng, J. and Druzdzel, M. (2000). AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research, 13: 155-188. http://www.jair.org/papers/paper764.html.

Chesnevar, C., Maguitman, A., and Loui, R. (2000). Logical models of argument. ACM Comput. Surv., 32(4): 337-383.

Chomsky, N. (1957). Syntactic Structures. Mouton and Co., The Hague.

Chrisley, R. and Begeer, S. (2000). Artificial intelligence: Critical Concepts in Cognitive Science. Routledge, London and New York.

Clark, K.L. (1978). Negation as failure. In H. Gallaire and J. Minker (Eds.), Logic and Databases, pp. 293-322. Plenum Press, New York.

Cohen, P.R. (2005). If not Turing's test, then what? AI Magazine, 26(4): 61-67.

Colmerauer, A., Kanoui, H., Roussel, P., and Pasero, R. (1973). Un système de communication homme-machine en français. Technical report, Groupe de Researche en Intelligence Artificielle, Université d'Aix-Marseille.

Colmerauer, A. and Roussel, P. (1996). The birth of Prolog. In T.J. Bergin and R.G. Gibson (Eds.), History of Programming Languages. ACM Press/Addison-Wesley. http://alain.colmerauer.free.fr/ArchivesPublications/HistoireProlog/19november92.pdf.

Copi, I.M. (1982). Introduction to Logic. Macmillan, New York, sixth edition.

Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2001). Introduction to Algorithms. MIT Press and McGraw-Hill, second edition.

Cover, T.M. and Thomas, J.A. (1991). Elements of information theory. Wiley, New York.

Culberson, J. and Schaeffer, J. (1998). Pattern databases. Computational Intelligence, 14(3): 318-334.

Dahl, V. (1994). Natural language processing and logic programming. Journal of Logic Programming, 19,20: 681-714.

Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks. Cambridge University Press.

Dasarathy, B.V. (1991). NN concepts and techniques. In B.V. Dasarathy (Ed.), Nearest Neighbour (NN) Norms: NN Pattern Classification Techniques, pp. 1-30. IEEE Computer Society Press, New York.

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

Davis, J. and Goadrich, M. (2006). The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine Learning, pp. 233 - 240. http://www.icml2006.org/icml_documents/camera-ready/030_The_Relationship_Bet.pdf.

Davis, M., Logemann, G., and Loveland, D. (1962). A machine program for theorem proving. Communications of the ACM, 5(7): 394-397.

Davis, M. and Putnam, H. (1960). A computing procedure for quantification theory. Journal of the ACM, 7(3): 201-215.

de Kleer, J. (1986). An assumption-based TMS. Artificial Intelligence, 28(2): 127-162.

de Kleer, J., Mackworth, A.K., and Reiter, R. (1992). Characterizing diagnoses and systems. Artificial Intelligence, 56: 197-222.

De Raedt, L., Frasconi, P., Kersting, K., and Muggleton, S.H. (Eds.) (2008). Probabilistic Inductive Logic Programming. Springer.

Dean, T. and Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3): 142-150.

Dean, T.L. and Wellman, M.P. (1991). Planning and Control. Morgan Kaufmann, San Mateo, CA.

Dechter, R. (1996). Bucket elimination: A unifying framework for probabilistic inference. In E. Horvitz and F. Jensen (Eds.), Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI-96), pp. 211-219. Portland, OR.

Dechter, R. (2003). Constraint Processing. Morgan Kaufmann.

Dellaert, F., Fox, D., Burgard, W., and Thrun, S. (1999). Monte Carlo localization for mobile robots. In IEEE International Conference on Robotics and Automation (ICRA99). http://www.ri.cmu.edu/pub_files/pub1/dellaert_frank_1999_2/dellaert_frank_1999_2.pdf.

Dietterich, T.G. (2000). Hierarchical reinforcement learning with the maxq value function decomposition. Journal of Artificial Intelligence Research, 13: 227-303. http://jair.org/papers/paper639.html.

Dietterich, T.G. (2002). Ensemble learning. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks,, pp. 405-408. MIT Press, Cambridge, MA, second edition. http://web.engr.oregonstate.edu/ tgd/publications/hbtnn-ensemble-learning.ps.gz.

Dijkstra, E.W. (1976). A discipline of programming. Prentice-Hall, Englewood Cliffs, NJ.

Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1: 269-271. http://gdzdoc.sub.uni-goettingen.de/sub/digbib/loader?did=D196313.

Doucet, A., de Freitas, N., and Gordon, N. (Eds.) (2001). Sequential Monte Carlo in Practice. Springer-Verlag.

Doyle, J. (1979). A truth maintenance system. AI Memo 521, MIT Artificial Intelligence Laboratory.

Dresner, K. and Stone, P. (2008). A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 31: 591-656.

Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification. Wiley-Interscience, 2nd edition.

Dung, P. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2): 321-357.

Dung, P., Mancarella, P., and Toni, F. (2007). Computing ideal sceptical argumentation. Artificial Intelligence, 171(10-15): 642-674.

Edwards, P. (Ed.) (1967). The Encyclopedia of Philosophy. Macmillan, New York.

Enderton, H.B. (1972). A Mathematical Introduction to Logic. Academic Press, Orlando, FL.

Felner, A., Korf, R.E., and Hanan, S. (2004). Additive pattern database heuristics. Journal of Artificial Intelligence Research (JAIR), 22: 279-318.

Fikes, R.E. and Nilsson, N.J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4): 189-208.

Fisher, D. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2: 139-172. Reprinted in [Shavlik and Dietterich (1990)].

Forbus, K.D. (1996). Qualitative reasoning. In CRC Hand-book of Computer Science and Engineering. CRC Press. http://www.qrg.northwestern.edu/papers/Files/crc7.pdf.

Freuder, E.C. and Mackworth, A.K. (2006). Constraint satisfaction: An emerging paradigm. In P.V.B. F. Rossi and T. Walsh (Eds.), Handbook of Constraint Programming, pp. 13-28. Elsevier.

Friedman, N. and Goldszmidt, M. (1996a). Building classifiers using Bayesian networks. In Proc. 13th National Conference on Artificial Intelligence, pp. 1277-1284. Portland, OR.

Friedman, N. and Goldszmidt, M. (1996b). Learning Bayesian networks with local structure. In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI-96), pp. 252-262. http://www2.sis.pitt.edu/ dsl/UAI/UAI96/Friedman1.UAI96.html.

Friedman, N., Greiger, D., and Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29: 103-130.

Gabbay, D.M., Hogger, C.J., and Robinson, J.A. (Eds.) (1993). Handbook of Logic in Artificial Intelligence and Logic Programming. Clarendon Press, Oxford, England. 5 volumes.

Gangemi, A., Guarino, N., Masolo, C., and Oltramari, A. (2003). Sweetening wordnet with dolce. AI Magazine, 24(3): 13-24.

Garcia-Molina, H., Ullman, J.D., and Widom, J. (2009). Database Systems: The Complete Book. Prentice Hall, 2nd edition.

Gardner, H. (1985). The Mind's New Science. Basic Books, New York.

Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2004). Bayesian Data Analysis. Chapman and Hall/CRC, 2nd edition.

Getoor, L. and Taskar, B. (Eds.) (2007). Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA.

Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.

Goldberg, D.E. (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Addison-Wesley, Reading, MA.

Green, C. (1969). Application of theorem proving to problem solving. In Proc. 1st International Joint Conf. on Artificial Intelligence, pp. 219-237. Washington, DC. Reprinted in [Webber and Nilsson (1981)].

Grenon, P. and Smith, B. (2004). Snap and span: Towards dynamic spatial ontology. Spatial Cognition and Computation, 4(1): 69-103. http://ontology.buffalo.edu/smith/articles/SNAP_SPAN.pdf.

Grünwald, P.D. (2007). The Minimum Description Length Principle. The MIT Press, Cambridge, MA.

Halpern, J. (1997). A logical approach to reasoning about uncertainty: A tutorial. In X. Arrazola, K. Kortha, and F. Pelletier (Eds.), Discourse, Interaction and Communication. Kluwer.

Hart, P.E., Nilsson, N.J., and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2): 100-107.

Hart, T.P. and Edwards, D.J. (1961). The tree prune (TP) algorithm. Memo 30, MIT Artificial Intelligence Project, Cambridge MA.

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, second edition.

Haugeland, J. (1985). Artificial Intelligence: The Very Idea. MIT Press, Cambridge, MA.

Haugeland, J. (Ed.) (1997). Mind Design II: Philosohpy, Psycholgy, Artificial Intelligence. MIT Press, Cambridge, MA, revised and enlarged edition.

Haussler, D. (1988). Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence, 36(2): 177-221. Reprinted in [Shavlik and Dietterich (1990)].

Hayes, P.J. (1973). Computation and deduction. In Proc. 2nd Symposium on Mathematical Foundations of Computer Science, pp. 105-118. Czechoslovak Academy of Sciences.

Heckerman, D. (1999). A tutorial on learning with Bayesian networks. In M. Jordan (Ed.), Learning in Graphical Models. MIT press.

Hendler, J., Berners-Lee, T., and Miller, E. (2002). Integrating applications on the semantic web. Journal of the Institute of Electrical Engineers of Japan, 122(10): 676-680. http://www.w3.org/2002/07/swint.

Henrion, M. (1988). Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In J.F. Lemmer and L.N. Kanal (Eds.), Uncertainty in Artificial Intelligence 2, pp. 149-163. Elsevier Science Publishers B.V.

Hertz, J., Krogh, A., and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation. Lecture Notes, Volume I, Santa Fe Institute Studies in the Sciences of Complexity. Addison-Wesley, Reading, MA.

Hewitt, C. (1969). Planner: A language for proving theorems in robots. In Proc. 1st International Joint Conf. on Artificial Intelligence, pp. 295-301. Washington, DC.

Hillis, W.D. (2008). A forebrain for the world mind. Edge: World Question Center. www.edge.org/q2009/q09_12.html#hillis.

Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., and Rudolph, S. (2009). OWL 2 Web Ontology Language Primer. W3C. http://www.w3.org/TR/owl2-primer/.

Hobbs, J.R., Stickel, M.E., Appelt, D.E., and Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63(1-2): 69-142.

Holland, J.H. (1975). Adaption in Natural and Artificial Systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor, MI.

Hoos, H.H. and Stützle, T. (2004). Stochastic Local Search: Foundations and Applications. Morgan Kaufmann / Elsevier.

Horvitz, E.J. (1989). Reasoning about beliefs and actions under computational resource constraints. In L. Kanal, T. Levitt, and J. Lemmer (Eds.), Uncertainty in Artificial Intelligence 3, pp. 301-324. Elsevier, New York.

Horvitz, E. (2006). Eric Horvitz forecasts the future. New Scientist, 2578: 72. http://www.newscientist.com/article/mg19225780.121-eric-horvitz-forecasts-the-future.html.

Howard, R.A. and Matheson, J.E. (1984). Influence diagrams. In R.A. Howard and J.E. Matheson (Eds.), The Principles and Applications of Decision Analysis. Strategic Decisions Group, Menlo Park, CA.

Howson, C. and Urbach, P. (2006). Scientific Reasoning: the Bayesian Approach. Open Court, Chicago, Illinois, 3rd edition.

Jaynes, E.T. (2003). Probability Theory: The Logic of Science. Cambridge University Press. http://omega.albany.edu:8008/JaynesBook.html.

Jensen, F.V. (1996). An Introduction to Bayesian Networks. Springer Verlag, New York.

Jordan, M. and Bishop, C. (1996). Neural networks. Memo 1562, MIT Artificial Intelligence Lab, Cambridge, MA. ftp://psyche.mit.edu/pub/ jordan/crc.ps.Z.

Joy, B. (2000). Why the future doesn't need us. Wired. www.wired.com/wired/archive/8.04/joy.html.

Jurafsky, D. and Martin, J.H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, second edition.

Kaelbling, L.P., Littman, M.L., and Moore, A.W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4: 237-285.

Kakas, A. and Denecker, M. (2002). Abduction in logic programming. In A. Kakas and F. Sadri (Eds.), Computational Logic: Logic Programming and Beyond, number 2407 in LNAI, pp. 402-436. Springer Verlag. http://www2.cs.kuleuven.be/cgi-bin/dtai/publ_info.pl?id=39495.

Kakas, A.C., Kowalski, R.A., and Toni, F. (1993). Abductive logic programming. Journal of Logic and Computation, 2(6): 719-770.

Kambhampati, S., Knoblock, C.A., and Yang, Q. (1995). Planning as refinement search: a unified framework for evaluating design tradeoffs in partial order planning. Artificial Intelligence, 76: 167-238. Special issue on Planning and Scheduling.

Kautz, H. and Selman, B. (1996). Pushing the envelope: Planning, propositional logic and stochastic search. In Proc. 13th National Conference on Artificial Intelligence, pp. 1194-1201. Portland, OR.

Kearns, M. and Vazirani, U. (1994). An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA.

Keeney, R.L. and Raiffa, H. (1976). Decisions with Multiple Objectives. John Wiley and Sons.

Kelly, K. (2008). A new kind of mind. Edge: World Question Center. www.edge.org/q2009/q09_1.html#kelly.

Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220: 671-680.

Kirsh, D. (1991a). Foundations of AI: the big issues. Artificial Intelligence, 47: 3-30.

Kirsh, D. (1991b). Today the earwig, tomorrow man? Artificial Intelligence, 47: 161-184.

Knuth, D.E. and Moore, R.W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4): 293-326.

Koller, D. and Friedman, N. (2009). Probabilsitic Graphical Models: Principles and Techniques. MIT Press.

Koller, D. and Milch, B. (2003). Multi-agent influence diagrams for representing and solving games. Games and Economic Behavior, 45(1): 181-221. http://people.csail.mit.edu/milch/papers/geb-maid.pdf.

Kolodner, J. and Leake, D. (1996). A tutorial introduction to case-based reasoning. In D. Leake (Ed.), Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp. 31-65. AAAI Press/MIT Press.

Korf, K.E. (1985). Depth-first iterative deepening: An optimal admissible tree search. Artificial Intelligence, 27(1): 97-109.

Kowalski, R. (1979). Logic for Problem Solving. Artificial Intelligence Series. North-Holland, New York.

Kowalski, R. and Sergot, M. (1986). A logic-based calculus of events. New Generation Computing, 4(1): 67-95.

Kowalski, R.A. (1974). Predicate logic as a programming language. In Information Processing 74, pp. 569-574. North-Holland, Stockholm.

Kowalski, R.A. (1988). The early history of logic programming. CACM, 31(1): 38-43.

Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.

Kuipers, B. (2001). Qualitative simulation. In R.A. Meyers (Ed.), Encyclopedia of Physical Science and Technology, pp. 287-300. Academic Press, NY, third edition. http://www.cs.utexas.edu/users/qr/papers/Kuipers-epst-01.html.

Langley, P., Iba, W., and Thompson, K. (1992). An analysis of Bayesian classifiers. In Proc. 10th National Conference on Artificial Intelligence, pp. 223-228. San Jose, CA.

Laplace, P. (1812). Théorie Analytique de Probabilités. Courcier, Paris.

Latombe, J.C. (1991). Robot Motion Planning. Kluwer Academic Publishers, Boston.

Lawler, E.L. and Wood, D.E. (1966). Branch-and-bound methods: A survey. Operations Research, 14(4): 699-719.

Leibniz, G.W. (1677). The Method of Mathematics: Preface to the General Science. Selections reprinted by Chrisley and Begeer (2000).

Lenat, D.B. and Feigenbaum, E.A. (1991). On the thresholds of knowledge. Artificial Intelligence, 47: 185-250.

Levesque, H.J. (1984). Foundations of a functional approach to knowledge representation. Artificial Intelligence, 23(2): 155-212.

Liu, A.L., Hile, H., Kautz, H., Borriello, G., Brown, P.A., Harniss, M., and Johnson, K. (2006). Indoor wayfinding: Developing a functional interface for individuals with cognitive impairments. In Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 95-102. Association for Computing Machinery, New York.

Lloyd, J.W. (1987). Foundations of Logic Programming. Symbolic Computation Series. Springer-Verlag, Berlin, second edition.

Lopez, A. and Bacchus, F. (2003). Generalizing GraphPlan by formulating planning as a CSP. In IJCAI-03, pp. 954-960.

Lopez De Mantaras, R., Mcsherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.T., Forbus, K., Keane, M., Aamodt, A., and Watson, I. (2005). Retrieval, reuse, revision and retention in case-based reasoning. The Knowledge Engineering Review, 20(3): 215-240. doi: 10.1017/S0269888906000646. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=435263&fulltextType=RA&fileId=S0269888906000646.

Loredo, T. (1990). From Laplace to supernova SN 1987A: Bayesian inference in astrophysics. In P. Fougère (Ed.), Maximum Entropy and Bayesian Methods, pp. 81-142. Kluwer Academic Press, Dordrecht, The Netherlands. http://bayes.wustl.edu.

Lowe, D.G. (1995). Similarity metric learning for a variable-kernel classifier. Neural Computation, 7: 72-85.

Luenberger, D.G. (1979). Introduction to Dynamic Systems: Theory, Models and Applications. Wiley, New York.

Mackworth, A.K. (1993). On seeing robots. In A. Basu and X. Li (Eds.), Computer Vision: Systems, Theory, and Applications, pp. 1-13. World Scientific Press, Singapore.

Mackworth, A.K. (2009). Agents, bodies, constraints, dynamics and evolution. AI Magazine.

Mackworth, A.K. (1977). On reading sketch maps. In Proc. Fifth International Joint Conf. on Artificial Intelligence, pp. 598-606. MIT, Cambridge, MA.

Manning, C. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA.

Mas-Colell, A., Whinston, M.D., and Green, J.R. (1995). Microeconomic Theory. Oxford University Press, New York, NY.

Matheson, J.E. (1990). Using influence diagrams to value information and control. In R.M. Oliver and J.Q. Smith (Eds.), Influence Diagrams, Belief Nets and Decision Analysis, chapter 1, pp. 25-48. Wiley.

McAllester, D. and Rosenblitt, D. (1991). Systematic nonlinear planning. In Proc. 9th National Conference on Artificial Intelligence, pp. 634-639.

McCarthy, J. (1986). Applications of circumscription to formalizing common-sense knowledge. Artificial Intelligence, 28(1): 89-116.

McCarthy, J. and Hayes, P.J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In M. Meltzer and D. Michie (Eds.), Machine Intelligence 4, pp. 463-502. Edinburgh University Press.

McCulloch, W. and Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5: 115-133.

McDermott, D. and Hendler, J. (1995). Planning: What it is, what it could be, an introduction to the special issue on planning and scheduling. Artificial Intelligence, 76: 1-16.

McLuhan, M. (1964). Understanding Media: The Extensions of Man. New American Library, New York.

Meir, R. and Rätsch, G. (2003). An introduction to boosting and leveraging. In In Advanced Lectures on Machine Learning (LNAI2600), pp. 119--184. Springer. http://www.boosting.org/papers/MeiRae03.pdf.

Mendelson, E. (1987). Introduction to Mathematical Logic. Wadsworth and Brooks, Monterey, CA, third edition.

Michie, D., Spiegelhalter, D.J., and Taylor, C.C. (Eds.) (1994). Machine Learning, Neural and Statistical Classification. Series in Artificial Intelligence. Ellis Horwood, Hemel Hempstead, Hertfordshire, England.

Mihailidis, A., Boger, J., Candido, M., and Hoey, J. (2007). The use of an intelligent prompting system for people with dementia. ACM Interactions, 14(4): 34-37.

Minsky, M. (1961). Steps towards artificial intelligence. Proceedings of the IEEE, 49: 8-30. http://web.media.mit.edu/ minsky/papers/steps.html.

Minsky, M. (1986). The Society of Mind. Simon and Schuster, New York.

Minsky, M. and Papert, S. (1988). Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, MA, expanded edition.

Minsky, M.L. (1975). A framework for representing knowledge. In P. Winston (Ed.), The Psychology of Computer Vision, pp. 211-277. McGraw-Hill, New York. Alternative version is in [Haugeland (1997)], and reprinted in [Brachman and Levesque (1985)].

Minsky, M. (1952). A neural-analogue calculator based upon a probability model of reinforcement. Technical report, Harvard University Psychological Laboratories, Cambridge, MA.

Minton, S., Johnston, M.D., Philips, A.B., and Laird, P. (1992). Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58(1-3): 161-205. http://dx.doi.org/10.1016/0004-3702(92)90007-K.

Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.

Mitchell, T. (1997). Machine Learning. McGraw-Hill, New York.

Mitchell, T.M. (1977). Version spaces: A candidate elimination approach to rule learning. In Proc. 5th International Joint Conf. on Artificial Intelligence, pp. 305-310. Cambridge, MA.

Motik, B., Patel-Schneider, P.F., and Grau, B.C. (Eds.) (2009a). OWL 2 Web Ontology Language Direct Semantics. W3C. http://www.w3.org/TR/owl2-semantics/.

Motik, B., Patel-Schneider, P.F., and Parsia, B. (Eds.) (2009b). OWL 2 Web Ontology Language Structural Specification and Functional-Style Syntax. W3C. http://www.w3.org/TR/owl2-syntax/.

Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing, 13(3,4): 245-286.

Muggleton, S. and De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20: 629-679.

Muscettola, N., Nayak, P., Pell, B., and Williams, B. (1998). Remote agent: to boldly go where no AI system has gone before. Artificial Intelligence, 103: 5-47.

Nash, Jr., J.F. (1950). Equilibrium points in n-person games. Proceedings of the National Academy of Sciences of the United States of America, 36: 48-49.

Nau, D.S. (2007). Current trends in automated planning. AI Magazine, 28(4): 43-58.

Neumann, J.V. and Morgenstern, O. (1953). Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ, third edition.

Newell, A. and Simon, H.A. (1976). Computer science as empirical enquiry: Symbols and search. Communications of the ACM, 19: 113-126. Reprinted in [Haugeland (1997)].

Newell, A. and Simon, H.A. (1956). The logic theory machine: A complex information processing system. Technical Report P-868, The Rand Corporation. http://shelf1.library.cmu.edu/IMLS/MindModels/logictheorymachine.pdf.

Niles, I. and Pease, A. (2001). Towards a standard upper ontology. In C. Welty and B. Smith (Eds.), Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS-2001). Ogunquit, Maine. http://www.ontologyportal.org/Pubs.html#FOIS.

Nilsson, N. (2007). The physical symbol system hypothesis: Status and prospects. In e.a. M. Lungarella (Ed.), 50 Years of AI, Festschrift, volume 4850 of LNAI, pp. 9-17. Springer. http://ai.stanford.edu/pers/pssh.pdf.

Nilsson, N.J. (1971). Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York.

Nilsson, N.J. (2009). The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press, Cambridge, England.

Nisan, N. (2007). Introduction to mechanisn design (for computer scientists). In N.Nisan et al. (Ed.), Algorithmic Game Theory, chapter 9, pp. 209-242. Cambridge University Press, Cambridge, England.

Nisan, N., Roughgarden, T., Tardos, E., and Vazirani, V.V. (Eds.) (2007). Algorithmic Game Theory. Cambridge University Press. http://www.cambridge.org/journals/nisan/downloads/Nisan_Non-printable.pdf.

Noy, N.F. and Hafner, C.D. (1997). The state of the art in ontology design: A survey and comparative review. AI Magazine, 18(3): 53-74. http://www.aaai.org/Library/Magazine/vol18.php#Fall.

Ordeshook, P.C. (1986). Game theory and political theory: An introduction. Cambridge University Press, New York.

Panton, K., Matuszek, C., Lenat, D., Schneider, D., Witbrock, M., Siegel, N., and Shepard, B. (2006). Common sense reasoning - from Cyc to intelligent assistant. In Y. Cai and J. Abascal (Eds.), Ambient Intelligence in Everyday Life, LNAI 3864, pp. 1-31. Springer.

Pearl, J. (1984). Heuristics. Addison-Wesley, Reading, MA.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA.

Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge University Press.

Peden, M.e.a. (Ed.) (2004). World Report on Road Traffic Injury Prevention. World Health Organization, Geneva.

Peng, Y. and Reggia, J.A. (1990). Abductive Inference Models for Diagnostic Problem-Solving. Symbolic Computation - AI Series. Springer-Verlag, New York.

Pereira, F.C.N. and Shieber, S.M. (2002). Prolog and Natural-Language Analysis. Microtome Publishing.

Pollack, M.E. (2005). Intelligent technology for an aging population: The use of ai to assist elders with cognitive impairment. AI Magazine, 26(2): 9-24.

Poole, D. (1993). Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64(1): 81-129.

Poole, D. (1997). The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence, 94: 7-56. http://www.cs.ubc.ca/spider/poole/abstracts/icl.html. Special issue on economic principles of multi-agent systems.

Poole, D., Goebel, R., and Aleliunas, R. (1987). Theorist: A logical reasoning system for defaults and diagnosis. In N. Cercone and G. McCalla (Eds.), The Knowledge Frontier: Essays in the Representation of Knowledge, pp. 331-352. Springer-Verlag, New York, NY.

Poole, D., Mackworth, A., and Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press, New York.

Posner, M.I. (Ed.) (1989). Foundations of Cognitive Science. MIT Press, Cambridge, MA.

Price, C.J., Travé-Massuyàs, L., Milne, R., Ironi, L., Forbus, K., Bredeweg, B., Lee, M.H., Struss, P., Snooke, N., Lucas, P., Cavazza, M., and Coghill, G.M. (2006). Qualitative futures. The Knowledge Engineering Review, 21(04): 317-334. doi: 10.1017/S026988890600097X. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=561496&fulltextType=RE&fileId=S026988890600097X.

Puterman, M. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley and Sons, New York.

Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1: 81-106. Reprinted in [Shavlik and Dietterich (1990)].

Quinlan, J.R. (1993). C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.

Quinlan, J.R. and Cameron-Jones, R.M. (1995). Induction of logic programs: FOIL and related systems. New Generation Computing, 13(3,4): 287-312.

Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2): 257-286.

Reiter, R. (1991). The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In V. Lifschitz (Ed.), Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pp. 359-380. Academic Press, San Diego, CA.

Reiter, R. (2001). Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press.

Riesbeck, C. and Schank, R. (1989). Inside Case-Based Reasoning. Lawrence Erlbaum, Hillsdale, NJ.

Robinson, J.A. (1965). A machine-oriented logic based on the resolution principle. Journal ACM, 12(1): 23-41.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6): 386-408.

Rosenschein, S.J. and Kaelbling, L.P. (1995). A situated view of representation and control. Artificial Intelligence, 73: 149-173.

Rubinstein, R.Y. (1981). Simulation and the Monte Carlo Method. John Wiley and Sons.

Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart and J.L. McClelland (Eds.), Parallel Distributed Processing, chapter 8, pp. 318-362. MIT Press, Cambridge, MA. Reprinted in [Shavlik and Dietterich (1990)].

Russell, B. (1917). Mysticism and Logic and Other Essays. G. Allen and Unwin, London.

Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Series in Artificial Intelligence. Prentice-Hall, Englewood Cliffs, NJ, third edition. http://aima.cs.berkeley.edu/.

Russell, S. (1997). Rationality and intelligence. Artificial Intelligence, 94: 57-77.

Sacerdoti, E.D. (1975). The nonlinear nature of plans. In Proc. 4th International Joint Conf. on Artificial Intelligence, pp. 206-214. Tbilisi, Georgia, USSR.

Samuel, A.L. (1959). Some studies in machine learning using the game of checkers. IBM Journal on Research and Development, 3(3): 210-229. http://www.research.ibm.com/journal/rd/033/ibmrd0303B.pdf.

Sandholm, T. (2007). Expressive commerce and its application to sourcing: How we conducted $35 billion of generalized combinatorial auctions. AI Magazine, 28(3): 45-58.

Savage, L.J. (1972). The Foundation of Statistics. Dover, New York, 2nd edition.

Schank, R.C. (1990). What is AI, anyway? In D. Partridge and Y. Wilks (Eds.), The Foundations of Artificial Intelligence, pp. 3-13. Cambridge University Press, Cambridge, England.

Schapire, R.E. (2002). The boosting approach to machine learning: An overview. In MSRI Workshop on Nonlinear Estimation and Classification. Springer Verlag. http://www.cs.princeton.edu/ schapire/boost.html.

Schubert, L.K. (1990). Monotonic solutions to the frame problem in the situation calculus: An efficient method for worlds with fully specified actions. In H.E. Kyburg, R.P. Loui, and G.N. Carlson (Eds.), Knowledge Representation and Defeasible Reasoning, pp. 23-67. Kluwer Academic Press, Boston, MA.

Shachter, R. and Peot, M.A. (1992). Decision making using probabilistic inference methods. In Proc. Eighth Conf. on Uncertainty in Artificial Intelligence (UAI-92), pp. 276-283. Stanford, CA.

Shafer, G. and Pearl, J. (Eds.) (1990). Readings in Uncertain Reasoning. Morgan Kaufmann, San Mateo, CA.

Shanahan, M. (1989). Prediction is deduction, but explanation is abduction. In Proc. 11th International Joint Conf. on Artificial Intelligence (IJCAI-89), pp. 1055-1060. Detroit, MI.

Shanahan, M. (1997). Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia. MIT Press, Cambridge, MA.

Shapiro, S.C. (Ed.) (1992). Encyclopedia of Artificial Intelligence. Wiley, New York, second edition.

Sharkey, N. (2008). The ethical frontiers of robotics. Science, 322(5909): 1800 - 1801. DOI: 10.1126/science.1164582.

Shavlik, J.W. and Dietterich, T.G. (Eds.) (1990). Readings in Machine Learning. Morgan Kaufmann, San Mateo, CA.

Shelley, M.W. (1818). Frankenstein; or, The Modern Prometheus. Lackington, Hughes, Harding, Mavor and Jones, London.

Shoham, Y. and Leyton-Brown, K. (2008). Multiagent Systems: Algorithmic, Game Theoretic, and Logical Foundations. Cambridge University Press.

Simon, H.A. (1995). Artificial intelligence: an empirical science. Artificial Intelligence, 77(1): 95-127.

Simon, H. (1996). The Sciences of the Artificial. MIT Press, Cambridge, MA, third edition.

Singer, P.W. (2009a). Robots at war: The new battlefield. The Wilson Quarterly. www.wilsoncenter.org/index.cfm?fuseaction=wq.essay&essay_id=496613.

Singer, P.W. (2009b). Wired for War: The Robotics Revolution and Conflict in the 21st Century. Penguin, New York.

Smith, B. (2003). Ontology. In L. Floridi (Ed.), Blackwell Guide to the Philosophy of Computing and Information, pp. 155--166. Oxford: Blackwell. http://ontology.buffalo.edu/smith/articles/ontology_pic.pdf.

Smith, B.C. (1991). The owl and the electric encyclopedia. Artificial Intelligence, 47: 251-288.

Smith, B.C. (1996). On the Origin of Objects. MIT Press, Cambridge, MA.

Somerville, M. (2006). The Ethical Imagination: Journeys of the Human Spirit. House of Anansi Press, Toronto.

Spall, J.C. (2003). Introduction to Stochastic Search and Optimization: Estimation, Simulation. Wiley.

Spiegelhalter, D.J., Franklin, R.C.G., and Bull, K. (1990). Assessment, criticism and improvement of imprecise subjective probabilities for a medical expert system. In M. Henrion, R.D. Shachter, L. Kanal, and J. Lemmer (Eds.), Uncertainty in Artificial Intelligence 5, pp. 285-294. North-Holland, Amsterdam, The Netherlands.

Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. MIT Press, Cambridge MA, 2nd edition.

Sterling, L. and Shapiro, E. (1986). The Art of Prolog. MIT Press, Cambridge, MA.

Stillings, N.A., Feinstein, M.H., Garfield, J.L., Rissland, E.L., Rosenbaum, D.A., Weisler, S.E., and Baker-Ward, L. (1987). Cognitive Science: An Introduction. MIT Press, Cambridge, MA.

Stone, P. (2007). Learning and multiagent reasoning for autonomous agents. In The 20th International Joint Conference on Artificial Intelligence (IJCAI-07), pp. 13-30. http://www.cs.utexas.edu/ pstone/Papers/bib2html-links/IJCAI07-award.pdf.

Stone, P. and Veloso, M. (2000). Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 8: 345-383.

Sutton, R.S. and Barto, A.G. (1998). Reinforcement Learning: An Introduction. MIT Press, Canbridge, MA.

Tarski, A. (1956). Logic, Semantics, Metamathematics. Clarendon Press, Oxford, England. Papers from 1923 to 1938 collected and translated by J. H. Woodger.

Tate, A. (1977). Generating project networks. In Proc. 5th International Joint Conf. on Artificial Intelligence, pp. 888-893. Cambridge, MA.

Tharp, T. (2003). The Creative Habit: Learn It and Use It for Life. Simon and Schuster.

Thrun, S. (2006). Winning the darpa grand challenge. In Innovative Applications of Artificial Intelligence Conference, (IAAI-06), pp. 16-20. Boston, MA.

Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. MIT Press, Cambridge, MA.

Turing, A. (1950). Computing machinery and intelligence. Mind, 59: 433-460. Reprinted in [Haugeland (1997)].

Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185: 1124-1131.

Valiant, L.G. (1984). A theory of the learnable. Communications of the ACM, 27: 1134-1142. Reprinted in [Shavlik and Dietterich (1990)].

van Beek, P. and Chen, X. (1999). Cplan: A constraint programming approach to planning. In AAAI-99, pp. 585-590.

van Emden, M.H. and Kowalski, R.A. (1976). The semantics of predicate logic as a programming language. Journal ACM, 23(4): 733-742.

Visser, U. and Burkhard, H.D. (2007). Robocup: 10 years of achievements and challenges. AI Magazine, 28(2): 115-130.

Viswanathan, P., Mackworth, A.K., Little, J.J., and Mihailidis, A. (2007). Intelligent wheelchairs: Collision avoidance and navigation assistance for older adults with cognitive impairment. In Proc. Workshop on Intelligent Systems for Assisted Cognition. Rochester, NY,.

Waldinger, R. (1977). Achieving several goals simultaneously. In E. Elcock and D. Michie (Eds.), Machine Intelligence 8: Machine Representations of Knowledge, pp. 94-136. Ellis Horwood, Chichester, England.

Walsh, T. (2007). Representing and reasoning with preferences. AI Magazine, 28(4): 59-69.

Warren, D.H.D. and Pereira, F.C.N. (1982). An efficient easily adaptable system for interpreting natural language queries. Computational Linguistics, 8(3-4): 110 - 122. http://portal.acm.org/citation.cfm?id=972944.

Webber, B.L. and Nilsson, N.J. (Eds.) (1981). Readings in Artificial Intelligence. Morgan Kaufmann, San Mateo, CA.

Weiss, G. (Ed.) (1999). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge, MA.

Weiss, S. and Kulikowski, C. (1991). Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Mateo, CA.

Weld, D. (1999). Recent advances in AI planning. AI Magazine, 20(2). http://www.cs.washington.edu/homes/weld/papers/pi2.pdf.

Weld, D.S. (1992). Qualitative physics: Albatross or eagle? Computational Intelligence, 8(2): 175-186. Introduction to special issue on the future of qualitative physics.

Weld, D.S. (1994). An introduction to least commitment planning. AI Magazine, 15(4): 27-61.

Weld, D. and de Kleer, J. (Eds.) (1990). Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann, San Mateo, CA.

Whitley, D. (2001). An overview of evolutionary algorithms. Journal of Information and Software Technology, 43: 817-831. http://www.cs.colostate.edu/ genitor/2001/overview.pdf.

Wilkins, D.E. (1988). Practical Planning: Extending the Classical AI Planning Paradigm. Morgan Kaufmann, San Mateo, CA.

Winograd, T. (1990). Thinking machines: Can there be? Are we? In D. Partridge and Y. Wilks (Eds.), The Foundations of Artificial Intelligence: A Sourcebook, pp. 167-189. Cambridge University Press, Cambridge, England.

Winograd, T. (1972). Understanding Natural Language. Academic Press, New York.

Woods, W.A. (2007). Meaning and links. AI Magazine, 28(4): 71-92.

Wooldridge, M. (2002). An Introduction to Multiagent Systems. John Wiley and Sons, Chichester, England.

Yang, Q. (1997). Intelligent Planning: A Decomposition and Abstraction-Based Approach. Springer-Verlag, New York.

Yang, S. and Mackworth, A.K. (2007). Hierarchical shortest pathfinding applied to route-planning for wheelchair users. In Proc. Canadian Conf. on Artificial Intelligence, AI-2007. Montreal, PQ,.

Zhang, N.L. and Poole, D. (1994). A simple approach to Bayesian network computations. In Proc. of the Tenth Canadian Conference on Artificial Intelligence, pp. 171-178.

Zhang, N.L. (2004). Hierarchical latent class models for cluster analysis. Journal of Machine Learning Research, 5(6): 697-723.

Zhang, Y. and Mackworth, A.K. (1995). Constraint nets: A semantic model for hybrid dynamic systems. Theoretical Computer Science, 138: 211-239.

Zilberstein, S. (1996). Using anytime algorithms in intelligent systems. AI Magazine, 17(3): 73-83.