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Learning and decision-making in artificial animals

International joint conference on artificial intelligence (IJCAI), 4246. Jonsson, A., and Barto, A. G. 2001. Automated state abstraction for options using the U-tree algorithm. In Advances in neural information processing systems, 1054-1060. Keramati, M., and Gutkin, B. S. 2011. A reinforcement learning theory for homeostatic regulation. In Advances in neural information processing systems, 82-90. Langton, C. G. 1997. Artificial life: An overview. MIT Press. LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep

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Artificial General Intelligence: Concept, State of the Art, and Future Prospects

-Simon Scale. Number 11. Williams & Wilkins Company. Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press. Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112. Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre

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Tra-la-Lyrics 2.0: Automatic Generation of Song Lyrics on a Semantic Domain

.; Schorlemmer, M.; and Smaill, A., eds., Computational Creativity Research: Towards Creative Machines , Atlantis Thinking Machines. Atlantis-Springer. chapter 12, 243–266. Lerdahl, F., and Jackendoff, R. 1983. A generative theory of tonal music . Cambridge. MA: The MIT Press. Manurung, H. 2003. An evolutionary algorithm approach to poetry generation . Ph.D. Dissertation, University of Edinburgh. Misztal, J., and Indurkhya, B. 2014. Poetry Generation System With an Emotional Personality. In Proceedings of 5th International Conference on Computational

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On Defining Artificial Intelligence

, N. A. 2016. Alien Mindscapes – A Perspective on the Search for Extraterrestrial Intelligence. Astrobiology 16:661–676. Carnap, R. 1950. Logical Foundations of Probability . Chicago: The University of Chicago Press. Cohen, P. R. 2005. If Not Turing’s Test, then what? AI Magazine 26:61–67. Davis, R. 1998. What are intelligence? and why? 1996 AAAI Presidential Address. AI Magazine 19(1):91–111. Domingos, P. 2018. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World . New York, NY, USA: Basic Books

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Causal Mathematical Logic as a guiding framework for the prediction of “Intelligence Signals” in brain simulations

.; and et al. 2004. Computation Within Cultured Neural Networks. Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society .7, 5340-5343. Dewar, C.R. 2009. Maximum Entropy Production as an Inference Algorithm that Translates Physical Assumptions into Macroscopic Predictions: Don’t Shoot the Messenger. Entropy. 11(4), 931-944. Di Caprio, D.; Badiali, J.P.; and Holovko, M. 2008. Simple field theoretical approach of Coulomb systems. Entropic effects. Available electronically from http

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Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

. [6] Clark D. M. Evolution of algebraic terms 1: term to term operation continuity. International Journal of Algebra and Computation, 23(05):1175-1205, 2013. [7] Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002. [8] Ficici S. G. and Pollack J. B. Pareto optimality in coevolutionary learning. In Kelemen J. and Sosík P., editors, Advances in Artificial Life, 6th European Conference, ECAL 2001, volume 2159

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Benchmarking of Problems and Solvers: a Game-Theoretic Approach

References [1] Auger, A., Hansen, N., Performance evaluation of an advanced local search evolutionary algorithm, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2, 2005, 1777-1784. [2] Benson, H.Y., Shanno, D.F., Vanderbei, R.J., Interior-point methods for nonconvex nonlinear programming: Jamming and comparative numerical testing, Operations Research and Financial Engineering, Princeton University, Technical Report ORFE-00-02, 2000. [3] Billups, S.C., Dirkse, S.P., Ferris, M.C., A comparison of algorithms for large-scale mixed

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Genetic Algorithm Modification for Production Scheduling

, Springer, Berlin 2008. [5] Gao J., Gen M., Sun L. A hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problem, GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation , ACM 2006, 1158. [6] Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning , Dorling Kindersley Pvt Ltd, New Delhi 2008, 17. [7] Grimes D., Hebrard E., Model and strategies for variants of the job shop scheduling problem, CP'11: Proceedings of the 17th

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An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Algorithm using Cuckoo Search and Differential Evolution for Data Clustering. Indian Journal of Science and Technology , 8 , 24, 2015. [7] Changhe, L. and Y. Shengxiang. A clustering particle swarm optimizer for dynamic optimization. in Evolutionary Computation, 2009. CEC '09. IEEE Congress on .2009, 439-446. [8] Chen, C.-Y. and Y. Fun. Particle swarm optimization algorithm and its application to clustering analysis. in Networking, Sensing and Control, 2004 IEEE International Conference on .2004, 789-794 Vol.2. [9] Chuang, L.-Y., C.-J. Hsiao, and C

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Toward an Efficient Resolution for a Single-machine Bi-objective Scheduling Problem with Rejection

References [1] Y. Bartal, S. Leonardi, A. Marchetti-Spaccamela, J. Sgall, L. Stougie, “Multiprocessor scheduling with rejection”, Journal of Discrete Mathematics 13(1), 64–78, (2000). [2] J. Blazewicz, K.H. Ecker, E. Pesch, G. Schmidt, J. Weglarz, “Handbook on scheduling : From theory to applications”, Springer Berlin Heidelberg New York (2007). [3] K. Deb, S. S. Agrawal, A. Pratap, T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, Lecture Notes in Computer Sciences , 1917, 849

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