Zacytuj

Ackley, D., and Littman, M. 1992. Interactions between Learning and Evolution. In Langton, C. G.; Taylor, C.; Farmer, C. D.; and Rasmussen, S., eds., Artificial Life II, SFI Studies in the Sciences of Complexity. Reading, MA, USA: Addison-Wesley. 487–509.Search in Google Scholar

Ashton, B. J.; Ridley, A. R.; Edwards, E. K.; and Thornton, A. 2018. Cognitive performance is linked to group size and affects fitness in Australian magpies. Nature 554(7692):364–367.10.1038/nature25503581549929414945Search in Google Scholar

Avila-García, O., and Cañamero, L. 2005. Hormonal modulation of perception in motivation-based action selection architectures. In Proc. of the Symposium on Agents that Want and Like.Search in Google Scholar

Bach, J. 2015. Modeling motivation in MicroPsi 2. In Proceedings of the 8th International Conference on Artificial General Intelligence, 3–13. Springer.10.1007/978-3-319-21365-1_1Search in Google Scholar

Boehm, A.-M., et al. 2012. FoxO is a critical regulator of stem cell maintenance in immortal Hydra. Proceedings of the National Academy of Sciences 109(48):19697–19702.10.1073/pnas.1209714109351174123150562Search in Google Scholar

Bohannon, J. 2008. Flunking spore. Science 322(5901):531–531.10.1126/science.322.5901.531b18948523Search in Google Scholar

Bouneffouf, D.; Rish, I.; and Cecchi, G. 2017. Bandit Models of Human Behavior: Reward Processing in Mental Disorders. In Proc. of the 10th International Conference on Artificial General Intelligence, 237–248. Springer.10.1007/978-3-319-63703-7_22Search in Google Scholar

Braitenberg, V. 1986. Vehicles: Experiments in synthetic psychology. MIT press.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_8Search in Google Scholar

Carlsson, M. 2018. Animat Navigation Using Landmarks. Master’s thesis, Chalmers University of Technology.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.2003.09.003Search in Google Scholar

Coello, C. A. C.; Lamont, G. B.; Van Veldhuizen, D. A.; et al. 2007. Evolutionary algorithms for solving multi-objective problems, volume 5. Springer.Search in Google Scholar

Cressey, D. 2015. Tropical paradise inspires virtual ecology lab. Nature 517(7534):255–256.10.1038/517255a25592514Search 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.2008.02.01518378330Search in Google Scholar

Ernst, D.; Geurts, P.; and Wehenkel, L. 2005. Tree-based batch mode reinforcement learning. Journal of Machine Learning Research 6(Apr):503–556.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

Futuyma, D. 2009. Evolution. Sinauer Associates.Search in Google Scholar

Gabbay, D. M.; Hodkinson, I.; and Reynolds, M. 1994. Temporal logic (vol. 1): mathematical foundations and computational aspects. Oxford University Press, Inc.Search in Google Scholar

Grund Pihlgren, G., and Lallo, N. 2018. Rule-Based Sequence Learning Extension for Animats. Master’s thesis, Chalmers University of Technology.Search in Google Scholar

Healy, S. D., and Jones, C. M. 2002. Animal learning and memory: an integration of cognition and ecology1. Zoology 105(4):321–327.10.1078/0944-2006-0007116351881Search in Google Scholar

Hubbell, S. P. 2001. The unified neutral theory of biodiversity and biogeography. Monographs in population biology. Princeton University Press.Search in Google Scholar

Johannesson, L.; Nilsson, M.; and Strannegård, C. 2018. Basic Language Learning in Artificial Animals. In Biologically Inspired Cognitive Architectures Meeting, 155–161. Springer.10.1007/978-3-319-99316-4_20Search in Google Scholar

Karakotsios, K., and Bremer, M. 1993. SimLife: The official strategy guide. Prima Pub.Search in Google Scholar

Keramati, M., and Gutkin, B. 2011. A reinforcement learning theory for homeostatic regulation. In Advances in neural information processing systems, 82–90.Search in Google Scholar

Keramati, M., and Gutkin, B. 2014. Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife 3.10.7554/eLife.04811427010025457346Search in Google Scholar

Knuth, D. E. 1997. The Art of Computer Programming, volume 2. Pearson Education.Search in Google Scholar

Koza, J. R. 1989. Hierarchical Genetic Algorithms Operating on Populations of Computer Programs. In IJCAI, volume 89, 768–774.Search in Google Scholar

Koza, J. R. 1994. Genetic programming as a means for programming computers by natural selection. Statistics and computing 4(2):87–112.10.1007/BF00175355Search 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/nature1453926017442Search in Google Scholar

Lotka, A. J. 1925. Elements of Physical Biology, by Alfred J. Lotka. Williams & Wilkins.Search in Google Scholar

Mäkeläinen, F.; Torén, H.; and Strannegård, C. 2018a. Code for Homeostatic Agents with Dynamic State Representation. gitlab.com/fredrikma/aaa_survivability.Search in Google Scholar

Mäkeläinen, F.; Torén, H.; and Strannegård, C. 2018b. Efficient Concept Formation in Large State Spaces. In Proc. of the 11th International Conference on Artificial General Intelligence, Prague, Czech Republic, 140–150. Springer.10.1007/978-3-319-97676-1_14Search in Google Scholar

Niv, Y. 2009. Reinforcement learning in the brain. Journal of Mathematical Psychology 53(3):139–154.10.1016/j.jmp.2008.12.005Search 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.3987Search 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

Sims, K. 1994. Evolving virtual creatures. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques, 15–22. ACM.10.1145/192161.192167Search 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-2016-0002Search in Google Scholar

Strannegård, C.; Svangård, N.; Lindström, D.; Bach, J.; and Steunebrink, B. 2017. The Animat Path to Artificial General Intelligence. In Proceedings of the Workshop on Architectures for Generality and Autonomy 2017.Search in Google Scholar

Such, F. P.; Madhavan, V.; Conti, E.; Lehman, J.; Stanley, K. O.; and Clune, J. 2017. Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. arXiv preprint arXiv:1712.06567.Search in Google Scholar

Sutton, R. S., and Barto, A. G. 1998. Reinforcement learning: An introduction. MIT press.10.1109/TNN.1998.712192Search in Google Scholar

Vladimirov, N.; Mu, Y.; Kawashima, T.; Bennett, D. V.; Yang, C.-T.; Looger, L. L.; Keller, P. J.; Freeman, J.; and Ahrens, M. B. 2014. Light-sheet functional imaging in fictively behaving zebrafish. Nature methods 11(9):883.10.1038/nmeth.304025068735Search in Google Scholar

Von Neumann, J., et al. 1951. The general and logical theory of automata. Cerebral mechanisms in behavior 1(41):1–2.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_18Search 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, 15–21. MIT Press.Search in Google Scholar

Yaeger, L. 1994. Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context. In Proceedings of the Artificial Life III conference, 263–298. Addison-Wesley.Search in Google Scholar

Yoshida, N. 2017. Homeostatic Agent for General Environment. Journal of Artificial General Intelligence 8(1):1–22.10.1515/jagi-2017-0001Search in Google Scholar

eISSN:
1946-0163
Język:
Angielski
Częstotliwość wydawania:
2 razy w roku
Dziedziny czasopisma:
Computer Sciences, Artificial Intelligence