[Adams, S. S., and Burbeck, S. 2012. Beyond the Octopus: From General Intelligence toward a Human-like Mind. In Theoretical Foundations of Artificial General Intelligence. Springer. 49-65.10.2991/978-94-91216-62-6_4]Search in Google Scholar
[Avila-García, O., and Cañamero, L. 2005. Hormonal modulation of perception in motivation-based action selection architectures. In Procs of the Symposium on Agents that Want and Like. SSAISB.]Search in Google Scholar
[Bach, J. 2009. Principles of synthetic intelligence. Oxford University Press.]Search in Google Scholar
[Bach, J. 2015. Modeling motivation in MicroPsi 2. In AGI 2015 Conference Proceedings, 3-13. Springer.10.1007/978-3-319-21365-1_1]Search in Google Scholar
[Bear, M. F.; Connors, B. W.; and Paradiso, M. A. 2015. Neuroscience. Wolters Kluwer.]Search in Google Scholar
[Bolker, B. M. 2008. Ecological models and data in R. Princeton University Press.10.1515/9781400840908]Search in Google Scholar
[Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press.]Search in Google Scholar
[Bouneffouf, D.; Rish, I.; and Cecchi, G. A. 2017. Bandit Models of Human Behavior: Reward Processing in Mental Disorders. In AGI 2017 Conference Proceedings, 237-248. Springer.10.1007/978-3-319-63703-7_22]Search in Google Scholar
[Buro, M. 1998. From simple features to sophisticated evaluation functions. In International Conference on Computers and Games, 126-145. Springer.10.1007/3-540-48957-6_8]Search in Google Scholar
[Caswell, H. 2001. Matrix population models. Wiley Online Library. Avaliable at https://www.sinauer.com/media.]Search in Google Scholar
[Christensen, V., and Walters, C. J. 2004. Ecopath with Ecosim: methods, capabilities and limitations. Ecological modelling 172(2-4):109-139.10.1016/j.ecolmodel.2003.09.003]Search in Google Scholar
[Dörner, D. 2001. Bauplan für eine Seele. Rororo. Rowohlt-Taschenbuch-Verlag.]Search in Google Scholar
[Draganski, B., and May, A. 2008. Training-induced structural changes in the adult human brain. Behavioural brain research 192(1):137-142.10.1016/j.bbr.2008.02.01518378330]Search in Google Scholar
[Fahlman, S. E., and Lebiere, C. 1990. The cascade-correlation learning architecture. In Advances in neural information processing systems, 524-532.]Search in Google Scholar
[Goertzel, B.; Pennachin, C.; and Geisweiller, N. 2014. The OpenCog Framework. In Engineering General Intelligence, Part 2. Springer. 3-29.10.2991/978-94-6239-030-0_1]Search in Google Scholar
[Goodfellow, I.; Bengio, Y.; and Courville, A. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.]Search in Google Scholar
[Hammer, P.; Lofthouse, T.; and Wang, P. 2016. The OpenNARS implementation of the non-axiomatic reasoning system. In AGI 2016 Conference Proceedings. Springer. 160-170.10.1007/978-3-319-41649-6_16]Search in Google Scholar
[Hochreiter, S., and Schmidhuber, J. 1997. Long short-term memory. Neural computation 9(8):1735-1780.10.1162/neco.1997.9.8.17359377276]Search in Google Scholar
[Insa-Cabrera, J. 2016. Towards a Universal Test of Social Intelligence. Ph.D. Dissertation, Universitat Politècnica de València, Valencia, Spain.]Search in Google Scholar
[Johnson, M.; Hofmann, K.; Hutton, T.; and Bignell, D. 2016. The Malmo platform for artificial intelligence experimentation. In International joint conference on artificial intelligence (IJCAI), 4246.]Search in Google Scholar
[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.]Search in Google Scholar
[Keramati, M., and Gutkin, B. S. 2011. A reinforcement learning theory for homeostatic regulation. In Advances in neural information processing systems, 82-90.]Search in Google Scholar
[Langton, C. G. 1997. Artificial life: An overview. MIT Press.]Search in Google Scholar
[LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep learning. nature 521(7553):436.10.1038/nature1453926017442]Search in Google Scholar
[Lindgren, K., and Verendel, V. 2013. Evolutionary Exploration of the Finitely Repeated Prisoners’ Dilemma-The Effect of Out-of-Equilibrium Play. Games 4(1):1-20.10.3390/g4010001]Search in Google Scholar
[Mitchell, T. M. 1978. Version spaces: an approach to concept learning. Technical report, STANFORD UNIV, CALIF, DEPT OF COMPUTER SCIENCE.]Search in Google Scholar
[Niv, Y. 2009. Reinforcement learning in the brain. Journal of Mathematical Psychology 53(3):139-154.10.1016/j.jmp.2008.12.005]Search in Google Scholar
[Nivel, E.; Thórisson, K. R.; Steunebrink, B. R.; Dindo, H.; Pezzulo, G.; Rodriguez, M.; Hernandez, C.; Ognibene, D.; Schmidhuber, J.; Sanz, R.; et al. 2013. Bounded recursive self-improvement. arXiv preprint arXiv:1312.6764.]Search in Google Scholar
[Nusser, S. 2009. Robust Learning in Safety-Related Domains. Machine Learning Methods for Solving Safety-Related Application Problems, Otto-von-Guericke-Universität Magdeburg.]Search in Google Scholar
[Roijers, D. M.; Vamplew, P.; Whiteson, S.; Dazeley, R.; et al. 2013. A Survey of Multi- Objective Sequential Decision-Making. J. Artif. Intell. Res.(JAIR) 48:67-113.10.1613/jair.3987]Search in Google Scholar
[Rooney, N. J., and Cowan, S. 2011. Training methods and owner-dog interactions: Links with dog behaviour and learning ability. Applied Animal Behaviour Science 132(3):169-177.10.1016/j.applanim.2011.03.007]Search in Google Scholar
[Russell, S. J., and Zimdars, A. 2003. Q-decomposition for reinforcement learning agents. In Proceedings of the 20th International Conference on Machine Learning (ICML-03), 656-663.]Search in Google Scholar
[Rusu, A. A.; Rabinowitz, N. C.; Desjardins, G.; Soyer, H.; Kirkpatrick, J.; Kavukcuoglu, K.; Pascanu, R.; and Hadsell, R. 2016. Progressive neural networks. arXiv preprint arXiv:1606.04671.]Search in Google Scholar
[Schmidhuber, J. 2015. Deep Learning in Neural Networks: An Overview. Neural Networks 61:85-117.10.1016/j.neunet.2014.09.00325462637]Search in Google Scholar
[Strannegård, C., and Nizamani, A. R. 2016. Integrating Symbolic and Sub-symbolic Reasoning. In AGI 2016 Conference Proceedings, 171-180. Springer.10.1007/978-3-319-41649-6_17]Search in Google Scholar
[Strannegård, C.; Nizamani, A. R.; Juel, J.; and Persson, U. 2016. Learning and Reasoning in Unknown Domains. Journal of Artificial General Intelligence 7(1):104-127.10.1515/jagi-2016-0002]Search in Google Scholar
[Sutton, R. S., and Barto, A. G. 1998. Reinforcement learning: An introduction. MIT press.10.1109/TNN.1998.712192]Search in Google Scholar
[Taylor, J.; Yudkowsky, E.; LaVictoire, P.; and Critch, A. 2016. Alignment for advanced machine learning systems. Machine Intelligence Research Institute.]Search in Google Scholar
[Thórisson, K. R. 2012. A new constructivist AI: from manual methods to self-constructive systems. In Theoretical Foundations of Artificial General Intelligence. Springer. 145-171.10.2991/978-94-91216-62-6_9]Search in Google Scholar
[Tuci, E.; Giagkos, A.; Wilson, M.; and Hallam, J., eds. 2016. From Animals to Animats. 1st International Conference on the Simulation of Adaptive Behavior. Springer.10.1007/978-3-319-43488-9]Search in Google Scholar
[Von Glasersfeld, E. 1995. Radical Constructivism: A Way of Knowing and Learning. Studies in Mathematics Education Series: 6. ERIC.]Search in Google Scholar
[Wang, P., and Hammer, P. 2015. Assumptions of Decision-Making Models in AGI. In AGI 2015 Conference Proceedings. Springer. 197-207.10.1007/978-3-319-21365-1_21]Search in Google Scholar
[Watkins, C. J. C. H. 1989. Learning from delayed rewards. Ph.D. Dissertation, King’s College, Cambridge.]Search in Google Scholar
[Wilson, S. W. 1986. Knowledge growth in an artificial animal. In Adaptive and Learning Systems. Springer. 255-264.10.1007/978-1-4757-1895-9_18]Search in Google Scholar
[Wilson, S. W. 1991. The animat path to AI. In Meyer, J. A., and Wilson, S. W., eds., From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior.]Search in Google Scholar
[Wolfe, N.; Sharma, A.; Drude, L.; and Raj, B. 2017. The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning. arXiv preprint arXiv:1701.04465.]Search in Google Scholar
[Yoshida, N. 2017. Homeostatic Agent for General Environment. Journal of Artificial General Intelligence 8(1).10.1515/jagi-2017-0001]Search in Google Scholar
[Zaremba, W., and Sutskever, I. 2015. Reinforcement learning neural turing machinesrevised. arXiv preprint arXiv:1505.00521.]Search in Google Scholar