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Global-Bibliography.Pdf Bibliography The following abbreviations are used for frequently cited conferences and journals: AAAI Proceedings of the AAAI Conference on Artificial Intelligence AAMAS Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems ACL Proceedings of the Annual Meeting of the Association for Computational Linguistics AIJ Artificial Intelligence (Journal) AIMag AI Magazine AIPS Proceedings of the International Conference on AI Planning Systems AISTATS Proceedings of the International Conference on Artificial Intelligence and Statistics BBS Behavioral and Brain Sciences CACM Communications of the Association for Computing Machinery COGSCI Proceedings of the Annual Conference of the Cognitive Science Society COLING Proceedings of the International Conference on Computational Linguistics COLT Proceedings of the Annual ACM Workshop on Computational Learning Theory CP Proceedings of the International Conference on Principles and Practice of Constraint Programming CVPR Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition EC Proceedings of the ACM Conference on Electronic Commerce ECAI Proceedings of the European Conference on Artificial Intelligence ECCV Proceedings of the European Conference on Computer Vision ECML Proceedings of the The European Conference on Machine Learning ECP Proceedings of the European Conference on Planning EMNLP Proceedings of the Conference on Empirical Methods in Natural Language Processing FGCS Proceedings of the International Conference on Fifth Generation Computer Systems FOCS Proceedings of the Annual Symposium on Foundations of Computer Science GECCO Proceedings of the Genetics and Evolutionary Computing Conference HRI Proceedings of the International Conference on Human-Robot Interaction ICAPS Proceedings of the International Conference on Automated Planning and Scheduling ICASSP Proceedings of the International Conference on Acoustics, Speech, and Signal Processing ICCV Proceedings of the International Conference on Computer Vision ICLP Proceedings of the International Conference on Logic Programming ICLR Proceedings of the International Conference on Learning Representations ICML Proceedings of the International Conference on Machine Learning ICPR Proceedings of the International Conference on Pattern Recognition ICRA Proceedings of the IEEE International Conference on Robotics and Automation ICSLP Proceedings of the International Conference on Speech and Language Processing IJAR International Journal of Approximate Reasoning IJCAI Proceedings of the International Joint Conference on Artificial Intelligence IJCNN Proceedings of the International Joint Conference on Neural Networks IJCV International Journal of Computer Vision ILP Proceedings of the International Workshop on Inductive Logic Programming IROS Proceedings of the International Conference on Intelligent Robots and Systems ISMIS Proceedings of the International Symposium on Methodologies for Intelligent Systems ISRR Proceedings of the International Symposium on Robotics Research JACM Journal of the Association for Computing Machinery JAIR Journal of Artificial Intelligence Research JAR Journal of Automated Reasoning JASA Journal of the American Statistical Association JMLR Journal of Machine Learning Research JSL Journal of Symbolic Logic KDD Proceedings of the International Conference on Knowledge Discovery and Data Mining KR Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning LICS Proceedings of the IEEE Symposium on Logic in Computer Science NeurIPS Advances in Neural Information Processing Systems PAMI IEEE Transactions on Pattern Analysis and Machine Intelligence PNAS Proceedings of the National Academy of Sciences of the United States of America PODS Proceedings of the ACM International Symposium on Principles of Database Systems RSS Proceedings of the Conference on Robotics: Science and Systems SIGIR Proceedings of the Special Interest Group on Information Retrieval SIGMOD Proceedings of the ACM SIGMOD International Conference on Management of Data SODA Proceedings of the Annual ACM–SIAM Symposium on Discrete Algorithms STOC Proceedings of the Annual ACM Symposium on Theory of Computing TARK Proceedings of the Conference on Theoretical Aspects of Reasoning about Knowledge UAI Proceedings of the Conference on Uncertainty in Artificial Intelligence 1084 Bibliography 1085 Aaronson, S. (2014). My conversation with ”Eugene Akametalu, A. K., Fisac, J. F., Gillula, J. H., Kay- Andre, D., Friedman, N., and Parr, R. (1998). Gener- Goostman,” the chatbot that’s all over the news for nama, S., Zeilinger, M. N., and Tomlin, C. J. (2014). alized prioritized sweeping. In NeurIPS 10. allegedly passing the Turing test. Shtetl-Optimized, Reachability-based safe learning with Gaussian pro- Andre, D. and Russell, S. J. (2002). State abstraction www.scottaaronson.com/blog/?p=1858. cesses. In 53rd IEEE Conference on Decision and for programmable reinforcement learning agents. In Control. Aarts, E. and Lenstra, J. K. (2003). Local Search AAAI-02. in Combinatorial Optimization. Princeton University Akgun, B., Cakmak, M., Jiang, K., and Thomaz, A. Andreae, P. (1985). Justified Generalisation: Learn- Press. (2012). Keyframe-based learning from demonstration. ing Procedures from Examples. Ph.D. thesis, MIT. International Journal of Social Robotics, 4, 343–355. Aarup, M., Arentoft, M. M., Parrod, Y., Stader, J., Andrieu, C., Doucet, A., and Holenstein, R. (2010). and Stokes, I. (1994). OPTIMUM-AIV: A knowledge- Aldous, D. and Vazirani, U. (1994). “Go with the win- Particle Markov chain Monte Carlo methods. J. Royal based planning and scheduling system for spacecraft ners” algorithms. In FOCS-94. Statistical Society, 72, 269–342. AIV. In Fox, M. and Zweben, M. (Eds.), Knowledge Alemi, A. A., Chollet, F., Een, N., Irving, G., Szegedy, Based Scheduling. Morgan Kaufmann. Andrychowicz, M., Baker, B., Chociej, M., Jozefow- C., and Urban, J. (2017). DeepMath - Deep sequence icz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, Abbas, A. (2018). Foundations of Multiattribute Util- models for premise selection. In NeurIPS 29. M., Powell, G., Ray, A., et al. (2018a). Learning dex- ity. Cambridge University Press. Allais, M. (1953). Le comportment de l’homme ra- terous in-hand manipulation. arXiv:1808.00177. Abbeel, P. and Ng, A. Y. (2004). Apprenticeship learn- tionnel devant la risque: critique des postulats et ax- Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., ing via inverse reinforcement learning. In ICML-04. iomes de l’ecole´ Americaine.´ Econometrica, 21, 503– Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, 546. Abney, S., McAllester, D. A., and Pereira, F. (1999). P., and Zaremba, W. (2018b). Hindsight experience Relating probabilistic grammars and automata. In Allan, J., Harman, D., Kanoulas, E., Li, D., Van Gysel, replay. In NeurIPS 30. ACL-99. C., and Vorhees, E. (2017). Trec 2017 common core Aneja, J., Deshpande, A., and Schwing, A. (2018). track overview. In Proc. TREC. Abramson, B. (1987). The expected-outcome model of Convolutional image captioning. In CVPR-18. two-player games. Ph.D. thesis, Columbia University. Allen, J. F. (1983). Maintaining knowledge about tem- Aoki, M. (1965). Optimal control of partially observ- poral intervals. CACM, 26, 832–843. Abramson, B. (1990). Expected-outcome: A general able Markov systems. J. Franklin Institute, 280, 367– model of static evaluation. PAMI, 12, 182–193. Allen, J. F. (1984). Towards a general theory of action 386. and time. AIJ, 23, 123–154. Appel, K. and Haken, W. (1977). Every planar map is Abreu, D. and Rubinstein, A. (1988). The structure four colorable: Part I: Discharging. Illinois J. Math., of Nash equilibrium in repeated games with finite au- Allen, J. F. (1991). Time and time again: The many 21, 429–490. tomata. Econometrica, 56, 1259–1281. ways to represent time. Int. J. Intelligent Systems, 6, 341–355. Appelt, D. (1999). Introduction to information extrac- Achlioptas, D. (2009). Random satisfiability. In Biere, tion. AI Communications, 12, 161–172. A., Heule, M., van Maaren, H., and Walsh, T. (Eds.), Allen, J. F., Hendler, J., and Tate, A. (Eds.). (1990). Handbook of Satisfiability. IOS Press. Readings in Planning. Morgan Kaufmann. Apt, K. R. (1999). The essence of constraint propaga- tion. Theoretical Computer Science, 221, 179–210. Ackerman, E. and Guizzo, E. (2016). The next gener- Allen, P. and Greaves, M. (2011). The singularity isn’t ation of Boston Dynamics’ Atlas robot is quiet, robust, near. Technology review, 12, 7–8. Apt, K. R. (2003). Principles of Constraint Program- ming and tether free. IEEE Spectrum, 24, 2016. Allen-Zhu, Z., Li, Y., and Song, Z. (2018). A . Cambridge University Press. Ackerman, N., Freer, C., and Roy, D. (2013). On convergence theory for deep learning via over- Apte´, C., Damerau, F., and Weiss, S. (1994). Auto- the computability of conditional probability. arXiv parameterization. arXiv:1811.03962. mated learning of decision rules for text categoriza- tion. ACM Transactions on Information Systems, 12, 1005.3014. Alterman, R. (1988). Adaptive planning. Cognitive 233–251. Ackley, D. H. and Littman, M. L. (1991). Interac- Science, 12, 393–422. Arbuthnot, J. (1692). Of the Laws of Chance. Motte, tions between learning and evolution. In Langton, C., Amarel, S. (1967). An approach to heuristic problem- Taylor, C., Farmer, J. D., and Rasmussen, S. (Eds.), London. Translation into English, with additions, of solving and theorem proving in the propositional cal-
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