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Cognitive Neuroscience
Current Debates, Research & Reports
Volume 6, 2015 - Issue 4
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Discussion Paper

Active inference and epistemic value

, , , , &
Pages 187-214 | Received 23 Oct 2014, Published online: 13 Mar 2015

References

  • Andreopoulos, A., & Tsotsos, J. (2013). A computational learning theory of active object recognition under uncertainty. International Journal of Computer Vision, 101(1), 95–142. doi:10.1007/s11263-012-0551-6
  • Atkeson, C., & Santamar ́ıa, J. (1997). A comparison of direct and model-based reinforcement learning. In International Conference on Robotics and Automation (ICRA). IEEE Press.
  • Attias, H. (2003). Planning by probabilistic inference. In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. Key West, FL.
  • Bach, D. R., & Dolan, R. J. (2012). Knowing how much you don’t know: A neural organization of uncertainty estimates. Nature Reviews. Neuroscience, 13(8), 572–586.
  • Baldassarre, G., & Mirolli, M. (2013). Intrinsically motivated learning in natural and artificial systems. Springer: Berlin.
  • Ballard, D. H., Kit, D., Rothkopf, C. A., & Sullivan, B. (2013). A hierarchical modular architecture for embodied cognition. Multisensory Research, 26, 177. doi:10.1163/22134808-00002414
  • Balleine, B. W., & Dickinson, A. (1998). Goal-directed instrumental action: Contingency and incentive learning and their cortical substrates. Neuropharmacology, 37(4–5), 407–419. doi:10.1016/S0028-3908(98)00033-1
  • Barlow, H. (1961). Possible principles underlying the transformations of sensory messages. In W. Rosenblith (Ed.), Sensory communication (pp. 217–234). Cambridge, MA: MIT Press.
  • Barlow, H. B. (1974). Inductive inference, coding, perception, and language. Perception, 3, 123–134. doi:10.1068/p030123
  • Barto, A., Singh, S., & Chentanez, N. (2004). Intrinsically motivated learning of hierarchical collections of skills. In Proceedings of the 3rd International Conference on Development and Learning (ICDL 2004), San Diego: Salk Institute.
  • Beal, M. J. (2003). Variational algorithms for approximate bayesian inference (PhD. Thesis). University College London.
  • Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1–2), 173–215. doi:10.1016/0004-3702(94)00005-L
  • Berridge, K. C. (2007). The debate over dopamine’s role in reward: The case for incentive salience. Psychopharmacology (Berl.), 191(3), 391–431. doi:10.1007/s00213-006-0578-x
  • Bialek, W., Nemenman, I., & Tishby, N. (2001). Predictability, complexity, and learning. Neural Computation, 13(11), 2409–2463. doi:10.1080/01621459.1989.10478825
  • Bonet, B., & Geffner, H. (2014). Belief tracking for planning with sensing: Width, complexity and approximations. Journal of Artificial Intelligence Research, 50, 923–970.
  • Botvinick, M., & An, J. (2008). Goal-directed decision making in prefrontal cortex: A computational framework. Advances in Neural Information Processing Systems (NIPS).
  • Botvinick, M., & Toussaint, M. (2012). Planning as inference. Trends in Cognitive Sciences, 16(10), 485–488. doi:10.1016/j.tics.2012.08.006
  • Braun, D. A., Ortega, P. A., Theodorou, E., & Schaal, S. (2011). Path integral control and bounded rationality. Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011 IEEE Symposium on, Paris, IEEE.
  • Bromberg-Martin, E. S., & Hikosaka, O. (2009). Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron, 63(1), 119–126. doi:10.1016/j.neuron.2009.06.009
  • Brooks, R. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3), 139–159. doi:10.1016/0004-3702(91)90053-M
  • Bruce, N., & Tsotsos, J. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9 (3):5, 1–24. doi:10.1167/9.3.5
  • Bunzeck, N., & Düzel, E. (2006). Absolute coding of stimulus novelty in the human substantia nigra/VTA. Neuron, 51(3), 369–379. doi:10.1016/j.neuron.2006.06.021
  • Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E. … Knight, R. T. (2006). High gamma power is phase-locked to theta oscillations in human neocortex. Science, 313(5793), 1626–1628. doi:10.1126/science.1128115
  • Cao, F., & Ray, S. (2012). Bayesian hierarchical reinforcement learning. Advances in Neural Information Processing Systems.
  • Cisek, P. (2007). Cortical mechanisms of action selection: The affordance competition hypothesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1485), 1585–1599.
  • Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences, 36(3), 181–204. doi:10.1017/S0140525X12000477
  • Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 933–942. doi:10.1098/rstb.2007.2098
  • D’Ardenne, K., McClure, S. M., Nystrom, L. E., & Cohen, J. D. (2008). BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science, 319(5867), 1264–1267. doi:10.1126/science.1150605
  • Daunizeau, J., Den Ouden, H. E., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010). Observing the observer (I): Meta-bayesian models of learning and decision-making. PLoS One, 5(12), e15554. doi:10.1371/journal.pone.0015554
  • Daw, N. D., & Doya, K. (2006). The computational neurobiology of learning and reward. Current Opinion in Neurobiology, 16(2), 199–204. doi:10.1016/j.conb.2006.03.006
  • Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8(12), 1704–1711. doi:10.1038/nn1560
  • Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441(7095), 876–879. doi:10.1038/nature04766
  • Dayan, P. (2009). Dopamine, reinforcement learning, and addiction. Pharmacopsychiatry, 42(1), S56–65. doi:10.1055/s-0028-1124107
  • Dayan, P., & Hinton, G. E. (1997). Using expectation maximization for reinforcement learning. Neural Computation, 9, 271–278. doi:10.1162/neco.1997.9.2.271
  • Dayan, P., Hinton, G. E., & Neal, R. (1995). The Helmholtz machine. Neural Computation, 7, 889–904. doi:10.1162/neco.1995.7.5.889
  • De Martino, B., Fleming, S. M., Garrett, N., & Dolan, R. J. (2012). Confidence in value-based choice. Nature Neuroscience, 16, 105–110. doi:10.1038/nn.3279
  • Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2), 312–325. doi:10.1016/j.neuron.2013.09.007
  • Ferro, M., Ognibene, D., Pezzulo, G., & Pirrelli, V. (2010). Reading as active sensing: A computational model of gaze planning during word recognition. Frontiers in Neurorobotics, 4, 1.
  • Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299(5614), 1898–1902. doi:10.1126/science.1077349
  • FitzGerald, T., Dolan, R., & Friston, K. (2014). Model averaging, optimal inference, and habit formation. Frontiers in Human Neuroscience. doi:10.3389/fnhum.2014.00457
  • Fox, C., & Roberts, S. (2011). A tutorial on variational Bayes. Artificial Intelligence Review, Springer. doi:10.1007/s10462-10011-19236-10468
  • Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews. Neuroscience, 11(2), 127–138. doi:10.1038/nrn2787
  • Friston, K., Adams, R. A., Perrinet, L., & Breakspear, M. (2012). Perceptions as hypotheses: Saccades as experiments. Frontiers in Psychology, 3, 151. doi:10.3389/fpsyg.2012.00151
  • Friston, K., & Mathys, C. (2015). I think therefore I am. Cognitive Dynamic Systems. S. Haykin, IEEE press: in press.
  • Friston, K., Samothrakis, S., & Montague, R. (2012). Active inference and agency: Optimal control without cost functions. Biological Cybernetics, 106, 523–541. [Epub ahead of print]. doi:10.1007/s00422-012-0512-8
  • Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2014). The anatomy of choice: Dopamine and decision-making. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1655). doi:10.1098/rstb.2013.0481
  • Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., Raymond, R. J., & Dolan, J. (2013). The anatomy of choice: Active inference and agency. Frontiers in Human Neuroscience, 7, 598. doi:10.3389/fnhum.2013.00598
  • Furmston, T., & Barber, D. (2010). Variational methods for reinforcement learning. In International Conference on Artificial Intelligence and Statistics (AISTATS). JMLR: W&CP, vol. 9, (pp. 241–248).
  • Fuster, J. M. (2004). Upper processing stages of the perception-action cycle. Trends in Cognitive Sciences, 8(4), 143–145. doi:10.1016/j.tics.2004.02.004
  • Gurney, K., Prescott, T. J., & Redgrave, P. (2001). A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biological Cybernetics, 84(6), 401–410. doi:10.1007/PL00007984
  • Harlow, H. (1950). Learning and satiation of response to intrinisically motivated complex puzzle performance by monkeys.” Journal of Comparative Physiological Psychology, 40, 289–294. doi:10.1037/h0058114
  • Hauskrecht, M. (2000). Value-function approximations for partially observable Markov decision processes. Journal of Artificial Intelligence Research, 13, 33–94.
  • Helmholtz, H. (1866/1962). Concerning the perceptions in general. Treatise on physiological optics. New York, NY: Dover. III.
  • Hollerman, J. R., & Schultz, W. (1996). Activity of dopamine neurons during learning in a familiar task context. Social Neuroscience Abstracts, 22, 1388.
  • Howard, R. (1966). Information value theory. IEEE Transactions on Systems, Science and Cybernetics, SSC-2 (1),22–26. doi:10.1109/TSSC.1966.300074
  • Humphries, M. D., Khamassi, M., & Gurney, K. (2012). Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Frontiers in Neuroscience, 6, 9. doi:10.3389/fnins.2012.00009
  • Itti, L., & Baldi, P. (2009). Bayesian surprise attracts human attention. Vision Research, 49(10), 1295–1306. doi:10.1016/j.visres.2008.09.007
  • Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews. Neuroscience, 2(3), 194–203. doi:10.1038/35058500
  • Jezek, K., Henriksen, E., Treves, A., Moser, E., & Moser, M.-B. (2011). Theta-paced flickering between place-cell maps in the hippocampus. Nature, 478, 246–249. doi:10.1038/nature10439
  • Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1–2), 99–134. doi:10.1016/S0004-3702(98)00023-X
  • Kakade, S., & Dayan, P. (2002). Dopamine: Generalization and bonuses. Neural Networks, 15(4–6), 549–559. doi:10.1016/S0893-6080(02)00048-5
  • Kamar., E., & Horvitz, E. (2013). Light at the end of the tunnel: A Monte Carlo approach to computing value of information. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems.
  • Kappen, H. J., Gomez, Y., & Opper, M. (2012). Optimal control as a graphical model inference problem. Machine Learning, 87(2), 159–182. doi:10.1007/s10994-012-5278-7
  • Kass, R. E., & Steffey, D. (1989). Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). Journal of the American Statistical Association, 407, 717–726.
  • Kirsh, D., & Maglio, P. (1994). On distinguishing epistemic from pragmatic action. Cognitive Science, 18(4), 513–549. doi:10.1207/s15516709cog1804_1
  • Klyubin, A. S., Polani, D., & Nehaniv, C. L. (2008). Keep your options open: An information-based driving principle for sensorimotor systems. PLoS One, 3(12), e4018. doi:10.1371/journal.pone.0004018
  • Krause., A., & Guestrin, C. (2005). Optimal nonmyopic value of information in graphical models: Efficient algorithms and theoretical limits. In International Joint Conference on Artificial Intelligence (IJCAI). Carnegie Mellon University.
  • Krebs, R. M., Schott, B. H., Schütze, H., & Düzel, E. (2009). The novelty exploration bonus and its attentional modulation. Neuropsychologia, 47, 2272–2281. doi:10.1016/j.neuropsychologia.2009.01.015
  • Lee, T. S., & Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20, 1434–1448. doi:10.1364/JOSAA.20.001434
  • Lepora, N., Martinez-Hernandez, U., & Prescott, T. (2013). Active touch for robust perception under position uncertainty. In IEEE proceedings of ICRA.
  • Linsker, R. (1990). Perceptual neural organization: Some approaches based on network models and information theory. Annual Review of Neuroscience, 13, 257–281. doi:10.1146/annurev.ne.13.030190.001353
  • Little, D. Y., & Sommer, F. T. (2013). Learning and exploration in action-perception loops. Front Neural Circuits, 7, 37. doi:10.3389/fncir.2013.00037
  • Littman, M., Sutton, R., & Singh, S. (2002). Predictive Representations of State. Advances in Neural Information Processing Systems 14 (NIPS).
  • Lungarella, M., & Sporns, O. (2006). Mapping information flow in sensorimotor networks. PLoS Computational Biology, 2, e144. doi:10.1371/journal.pcbi.0020144
  • McClure, S. M., Daw, N. D., & Montague, P. R. (2003). A computational substrate for incentive salience. Trends in Neurosciences, 26(8), 423–428. doi:10.1016/S0166-2236(03)00177-2
  • Miller, G., Galanter, E., & Pribram, K. (1960). Plans and the structure of behavio. New York, NY: Henry Holt.
  • Moser, E. I., Kropff, E., & Moser, M.-B. (2008). Place cells, grid cells, and the brain’s spatial representation system. Annual Review of Neuroscience, 31, 69–89. doi:10.1146/annurev.neuro.31.061307.090723
  • Mushiake, H., Saito, N., Sakamoto, K., Itoyama, Y., & Tanji, J. (2006). Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans. Neuron, 50, 631–641. doi:10.1016/j.neuron.2006.03.045
  • Najemnik, J., & Geisler, W. (2005). Optimal eye movement strategies in visual search. Nature, 434, 387–391. doi:10.1038/nature03390
  • Nepora, N., & Gurney, K. (2012). The basal ganglia optimize decision making over general perceptual hypotheses. Neural Computation, 24(11), 2924–2945. doi:10.1162/NECO_a_00360
  • Niv, Y. (2007). Cost, benefit, tonic, phasic: What do response rates tell us about dopamine and motivation? Annals of the New York Academy of Sciences, 1104, 357–376.
  • O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304(5669), 452–454. doi:10.1126/science.1094285
  • O’Regan, J., & Noë, A. (2001). A sensorimotor account of vision and visual consciousness. The Behavioral and Brain Sciences, 24, 939–973. doi:10.1017/S0140525X01000115
  • Ognibene, D., & Baldassarre, G. (2014). Ecological active vision: Four Bio-inspired principles to integrate bottom-up and adaptive top-down attention tested with a simple camera-arm robot. Autonomous Mental Development, IEEE Transactions on. IEEE.
  • Ognibene, D., Chinellato, E., Sarabia, M., & Demiris, Y. (2013). Contextual action recognition and target localization with an active allocation of attention on a humanoid robot. Bioinspiration & Biomimetics, 8, 3. doi:10.1088/1748-3182/8/3/035002
  • Ognibene, D., & Demiris, Y. (2013). Toward active event perception. In International Joint conference of Artificial Intelligence. IJCIA: Beijing.
  • Ognibene, D.,Pezzulo, G., & Baldassarre, G. (2010). Learning to look in different environments: An active-vision model which learns and readapts visual routines. In S. Doncieux (ed.), Proceedings of the 11th International Conference on Simulation of Adaptive Behaviour , SAB 2010, Paris - Clos Lucé, France, August 25–28, 2010. Proceedings. Springer Berlin Heidelberg.
  • Ognibene, D., Catenacci Volpi, N., Pezzulo, G., & Baldassare, G. (2013). Learning epistemic actions in model-free memory-free reinforcement learning: Experiments with a neuro-robotic model. In Second International Conference on Biomimetic and Biohybrid Systems. Proceedings. pp. 191–203. doi:10.1007/978-3-642-39802-5_17
  • Oja, E. (1989). Neural networks, principal components, and subspaces. International Journal of Neural Systems, 1, 61–68. doi:10.1142/S0129065789000475
  • Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607–609. doi:10.1038/381607a0
  • Optican, L., & Richmond, B. J. (1987). Temporal encoding of two-dimensional patterns by single units in primate inferior cortex. II Information Theoretic Analysis. Journal of Neurophysiology, 57, 132–146.
  • Ortega, P. A., & Braun, D. A. (2011). Information, utility and bounded rationality. Lecture Notes on Artificial Intelligence, 6830, 269–274.
  • Ortega, P. A., & Braun, D. A. (2013). Thermodynamics as a theory of decision-making with information-processing costs. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 469, 2153. doi:10.1098/rspa.2012.0683
  • Oudeyer, P.-Y., & Kaplan, F. (2007). What is intrinsic motivation? A typology of computational approaches. Frontiers in Neurorobotics, 1, 6. doi:10.3389/neuro.12.006.2007
  • Penny, W., Zeidman, P., & Burgess, N. (2013). Forward and backward inference in spatial cognition. Plos Computational Biology, 9(12), e1003383. doi:10.1371/journal.pcbi.1003383
  • Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006). Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature, 442(7106), 1042–1045. doi:10.1038/nature05051
  • Pezzementi, Z., Plaku, E., Reyda, C., & Hager, G. (2011). Tactile-object recognition from appearance information. IEEE Transactions on Robotics, 27, 473–487. doi:10.1109/TRO.2011.2125350
  • Pezzulo, G., & Castelfranchi, C. (2009). Thinking as the control of imagination: A conceptual framework for goal-directed systems. Psychological Research Psychologische Forschung, 73, 559–577. doi:10.1007/s00426-009-0237-z
  • Pezzulo, G., Rigoli, F., & Chersi, F. (2013). The mixed instrumental controller: Using value of information to combine habitual choice and mental simulation. Frontiers in Psychology, 4, 92. doi:10.3389/fpsyg.2013.00092
  • Pezzulo, G., Van Der Meer, M., Lansink, C., & Pennartz, C. (2014). Internally generated sequences in learning and executing goal-directed behavior. Trends in Cognitive Sciences, 18(2), pp. 647–657.
  • Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. doi:10.1038/4580
  • Redgrave, P., & Gurney, K. (2006). The short-latency dopamine signal: A role in discovering novel actions? Nature Reviews. Neuroscience, 7(12), 967–975. doi:10.1038/nrn2022
  • Roy, N., Burgard, W., Fox, D., & Thrun, S. (1999). Coastal navigation: Robot navigation under uncertainty in dynamic environments. In Proceedings of the IEEE International Conference on Robotics andAutomation (ICRA). Washington, DC: IEEE Computer Society.
  • Ryan, R., & Deci, E. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum.
  • Schembri, M., Mirolli, M., & Baldassare, G. (2007). Evolving internal reinforcers for an intrinsically motivated reinforcement learning robot. In Y. Demiris, B. Scassellati, & D. Mareschal (Eds.) Proceedings of the 6th IEEE International Conference on Development and Learning (ICDL2007). IEEE.
  • Schmidhuber, J. (1991). Curious model-building control systems. In Proceeding of International Joint Conference on Neural Networks. Singapore: IEEE. 2: 1458–1463.
  • Schneider, A., Sturm, J., Stachniss, C., Reisert, M., Burkhardt, H., & Burgard, W. (2009). Object identification with tactile sensors using bag-of-features. IROS 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009. p. 243. IEEE.
  • Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27.
  • Schultz, W., Apicella, P., & Ljungberg, T. (1993). Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Journal of Neuroscience, 13, 900–913.
  • Schwartenbeck, P., FitzGerald, T., Mathys, C., Dolan, R., & Friston, K. (2014). The dopaminergic midbrain encodes the expected certainty about desired outcomes. Cerebral Cortex. pii: bhu159. doi:10.1093/cercor/bhu159
  • Shackle, G. (1972). Epistemics and economics. Cambridge, MA: Cambridge University Press.
  • Singh, A., Krause, A., Guestrin, C., & Kaiser, W. (2009). Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34(2), 707.
  • Solway, A., & Botvinick, M. (2012). Goal-directed decision making as probabilistic inference: A com- putational framework and potential neural correlates. Psychological Review, 119, 120–154. doi:10.1037/a0026435
  • Sornkarn, N., Nanayakkara, T., & Howard, M. (2014). Internal impedance control helps information gain in embodied perception. In IEEE International Conference on Robotics and Automation (ICRA), IEEE.
  • Still, S. (2009). Information-theoretic approach to interactive learning. EPL (Europhysics Letters), 85(2), 28005. doi:10.1209/0295-5075/85/28005
  • Still, S., & Precup, D. (2012). An information-theoretic approach to curiosity-driven reinforcement learning. Theory in Biosciences, 131(3), 139–148. doi:10.1007/s12064-011-0142-z
  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
  • Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: An approach for hierarchical learning in sensory–motor systems. Neural Networks, 12, 1131–1141. doi:10.1016/S0893-6080(99)00060-X
  • Tishby, N., & Polani, D. (2010). Information theory of decisions and actions. In V. Cutsuridis, A. Hussain, & J. Taylor (Eds.), Perception-reason-action cycle: Models, algorithms and systems. Springer: Berlin.
  • Toussaint, M., & Storkey, A. (2006). Probabilistic inference for solving discrete and continuous state Markov Decision Processes. In Proceeding of the 23nd International Conference on Machine Learning. ACM. pp. 945–952.
  • Van den Broek, J. L., Wiegerinck, W. A. J. J., & Kappen, H. J. (2010). Risk-sensitive path integral control. UAI, 6, 1–8.
  • Verschure, P. F., Voegtlin, T., & Douglas, R. J. (2003). Environmentally mediated synergy between perception and behavior in mobile robots. Nature, 425, 620–624. doi:10.1038/nature02024
  • Vijayakumar, S., Toussaint, M., Petkos, G., & Howard, M. (2009). Planning and moving in dynamic environments. In B. Sendhoff (Ed.), Creating brain-like intelligence (pp. 151–191). Berlin: Springer-Verlag.
  • Viswanathan, G., Buldyrev, S., Havlin, S., Da Luz, M., Raposo, E., & Stanley, H. (1999). Optimizing the success of random searches. Nature, 401(6756), 911–914. doi:10.1038/44831
  • Walther, D., Rutishauser, U., Koch, C., & Perona, P. (2005). Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 100, 41–63. doi:10.1016/j.cviu.2004.09.004
  • Wittmann, B. C., Daw, N. D., Seymour, B., & Dolan, R. J. (2008). Striatal activity underlies novelty-based choice in humans. Neuron, 58(6), 967–973. doi:10.1016/j.neuron.2008.04.027

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