nach oben

Minds and Machines

Erschienen in:

01.06.2008

Coordinating with the Future: The Anticipatory Nature of Representation

verfasst von: Giovanni Pezzulo

Erschienen in: Minds and Machines | Ausgabe 2/2008

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future.

Vorheriger Artikel Robosemantics: How Stanley the Volkswagen Represents the World

Nächster Artikel Hubert L. Dreyfus’s Critique of Classical AI and its Rationalist Assumptions

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Additional functions such as action planning, monitoring, and control, are also possible thanks to this functional organization of action. See also (Rosenblueth et al. 1943) for related ideas in behavior control in early cybernetics.

See also (Schubotz 2007) for a discussion of how internal models developed for predicting and controlling one’s own motor system can be used for predicting and simulating external events.

Gardenfors (2004) uses a similar notion of detachment for distinguishing agents which are able to refer to situations which are not motivated by actual or recent stimuli.

It is worth noting that this perspective is at odds with the current view about emergence and self-organizing systems (Haken 1988; Kelso 1995) in which multiple levels of reality and of explanation are recognized to be autonomous (without denying interdependences), against any reductionism. This does not mean that in principle any level of explanation is correct, but that those who prove to be good according to the usual scientific criteria, are guaranteed a role in science: and mentalistic theories have of course to pass this test.

Adams, J. A. (1971). A closed-loop theory of motor learning. Journal of Motor Behavior, 3, 111–149.

Arbib, M. (1992). Schema theory. In S. Shapiro (Ed.), Encyclopedia of artificial intelligence (2nd ed., Vol. 2, pp. 1427–1443). Wiley.

Balkenius, C., & Hulth, N. (1999). Attention as selection-for-action: A scheme for active perception. In Proceedings of EuRobot-1999, Zurich.

Ballard, D. (1991). Animate vision. Artificial Intelligence, 48(1), 1–27.MathSciNetCrossRef

Balleine, B. W., & Dickinson, A. (1998). Goal-directed instrumental action: Contingency and incentive learning and their cortical substrates. Neuropharmacology, 37(4–5), 407–419.CrossRef

Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–600.

Barsalou, L. W. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358(1435), 1177–1187.CrossRef

Barsalou, L., Simmons, W. K., Barbey, A., & Wilson, C. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7(2), 84–91.CrossRef

Beer, R. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173–215.CrossRef

Beer, R. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems, 20, 257–289.CrossRef

Bickhard, M. H. (1998). Levels of representationality. Journal of Experimental and Theoretical Artificial Intelligence, 10(2), 179–215.CrossRef

Bickhard, M. H. (2001). Function, anticipation and representation. In D. M. Dubois, (Ed.), Computing Anticipatory Systems. CASYS 2000 – Fourth International Conference (pp. 459–469). Melville, NY. American Institute of Physics.

Bickhard, M. H., & Terveen, L. (1995). Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Amsterdam: Elsevier Scientific.

Blakemore, S.-J., & Decety, J. (2001). From the perception of action to the understanding of intention. Nature Reviews Neuroscience, 2, 561–567.

Bongard, J., Zykov, V., & Lipson, H. (2006a). Resilient machines through continuous self-modeling. Science, 314(5802), 1118–1121.CrossRef

Bongard, J. C., Zykov, V., & Lipson, H. (2006b). Automated synthesis of body schema using multiple sensor modalities. In Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (ALIFEX) (pp. 220–226).

Breazeal, C., & Scassellati, B. (1999). A context-dependent attention system for a social robot. In IJCAI ’99: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 1146–1153). Morgan Kaufmann Publishers Inc.

Brentano, F. (1985). Psychology from an Empirical Standpoint (trans: Rancurello, A. C., Terrell, D. B., & McAlister, L. L.). London: Routledge.

Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(47), 139–159.CrossRef

Butz, M. V. (2002). Anticipatory learning classifier systems. Boston, MA: Kluwer Academic Publishers.MATH

Butz, M. V., Sigaud, O., & Gérard, P. (2003). Internal models and anticipations in adaptive learning systems. In M. V. Butz, O. Sigaud, & P. Gérard (Eds.), Anticipatory behavior in adaptive learning systems: Foundations, theories, and systems (pp. 86–109). Berlin Heidelberg: Springer-Verlag.

Camazine, S., Franks, N. R., Sneyd, J., Bonabeau, E., Deneubourg, J.-L., & Theraula, G. (2001). Self-organization in biological systems. Princeton, NJ, USA: Princeton University Press.

Cangelosi, A. (2006). The grounding and sharing of symbols. Pragmatics and Cognition, 14, 275–285.CrossRef

Carlsson, K., Petrovic, P., Skare, S., Petersson, K. M., & Ingvar, M. (2000). Tickling expectations: Neural processing in anticipation of a sensory stimulus. Journal of Cognitive Neuroscience, 12(4), 691–703.CrossRef

Carpenter, G. A., & Grossberg, S. (1988). The art of adaptive pattern recognition by a self-organizing neural network. Computer, 21(3), 77–88.CrossRef

Castelfranchi, C. (2005). Mind as an anticipatory device: For a theory of expectations. In BVAI 2005, pp. 258–276.

Chalmers, D. (1996). The conscious mind. Oxford University Press, New York. Tr. It., La mente cosciente, McGraw- Hill, 1999.

Chiel, H. J., & Beer, R. D. (1997). The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences, 20, 553–557.CrossRef

Churchland, P. (1992). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge, MA: MIT Press.

Cisek, P., & Kalaska, J. (2001). Common codes for situated interaction. Behavioral and Brain Sciences, 24(5), 883–884.

Clark, A. (1998). Being there. Putting brain, body, and world together. MIT Press.

Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58, 10–23.CrossRef

Clark, A., & Grush, R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7(1), 5–16.CrossRef

Colombo, M., & Graziano, M. (1994). Effects of auditory and visual interference on auditory-visual delayed matching to sample in monkeys (maca fascicularis). Behavioral Neuroscience, 108, 636–639.CrossRef

Conte, R., & Castelfranchi, C. (1995). Cognitive and social action. London, UK: University College London.

Cotterill, R. (1998). Enchanted looms: Conscious networks in brains and computers. Cambridge University Press.

Cotterill, R. (2001). Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33.CrossRef

Craik, K. (1943). The nature of explanation. Cambridge: Cambridge University Press.

Damasio, A. R. (1994). Descartes’ error: Emotion, reason and the human brain. New York: Grosset/Putnam.

Dautenhahn, K., & Nehaniv, C. (2002). Imitation in animals and artifacts. MIT Press.

Decety, J. (1996). Do imagined and executed actions share the same neural substrate? Brain Research. Cognitive Brain Research, 3, 87–93.CrossRef

Decety, J., & Chaminade, T. (2003). When the self represents the other: A new cognitive neuroscience view on psychological identification. Consciousness and Cognition, 12(20), 577–596.CrossRef

Decety, J., Grèzes, J., Costes, N., Perani, D., Jeannerod, M., Procyk, E., Grassi, F., & Fazio, F. (1997). Brain activity during observation of actions. influence of action content and subject’s strategy. Brain, 120(Pt 10), 1763–1777.CrossRef

Decety, J., Jeannerod, M., & Prablanc, C. (1989). The timing of mentally represented actions. Behavioural Brain Research, 34(1–2), 35–42.CrossRef

Delcomyn, F. (1980). Neural basis for rhythmic behaviour in animals. Science, 210, 492–498.CrossRef

Demiris, Y. (2007). Prediction of intent in robotics and multi-agent systems. Cognitive Processing, 8(3), 151–158.CrossRef

Demiris, J., & Hayes, G. (1996). Imitative learning mechanisms in robots and humans. In Proceedingsof the 5th European Workshop on Learning Robot, pp. 9–16.

Demiris, Y., & Khadhouri, B. (2005). Hierarchical attentive multiple models for execution and recognition (hammer). Robotics and Autonomous Systems Journal, 54, 361–369.CrossRef

Dennett, D. C. (1991). Consciousness explained. Little, Brown & Co.

Desmurget, M., & Grafton, S. (2000). Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences, 4, 423–431.CrossRef

Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review, 3, 357–370.CrossRef

Doya, K. (1996). Temporal difference learning in continuous time and space. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems (Vol. 8, pp. 1073–1079). The MIT Press.

Drescher, G. L. (1991). Made-up minds: A constructivist approach to artificial intelligence. Cambridge, MA: MIT Press.MATH

Dunker, K. (1935). Psychology des produktiven Denkens. Berlin: Springer.

Edelman, G. M. (1987). Neural Darwinism: The Theory of Neuronal Group Selection. New York: Basic Books

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.CrossRef

Fleischer, J. G. (2007). Neural correlates of anticipation in cerebellum, basal ganglia, and hippocampus. In M. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory behavior in adaptive learning systems: Advances in anticipatory processing, LNAI 4520. Springer.

Fodor, J. (1975). The language of thought. Cambridge, MA: Harvard University Press.

Fodor, J. A. (1980). Methodological solipsism considered as a research strategy in cognitive psychology. Behavioral and Brain Sciences, 3, 63–109.

Fogassi, L., Ferrari, P., Chersi, F., Gesierich, B., Rozzi, S., & Rizzolatti, G. (2005). Parietal lobe: From action organization to intention understanding. Science, 308, 662–667.CrossRef

Friston, K. (2003). Learning and inference in the brain. Neural Networks, 16(9), 1325–1352.CrossRef

Frith, C. (2007). Making up the mind. How the brain creates our mental world. Blackwell.

Frith, C. D., & Frith, U. (2006). How we predict what other people are going to do. Brain Research, 1079(1), 36–46.CrossRef

Gallagher, S., & Jeannerod, M. (2002). From action to interaction. Journal of Consciousness Studies, 9(24), 3–26.

Gallese, V. (2000). The inner sense of action: Agency and motor representations. Journal of Consciousness Studies, 7, 23–40.

Gallese, V. (2001). The ’shared manifold’ hypothesis. from mirror neurons to empathy. Journal of Consciousness Studies, 8, 5–87.

Gallese, V., & Goldman, A. (1998). Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 2(12), 493–501.CrossRef

Gallese, V., & Lakoff, G. (2005). The brain’s concepts: The role of the sensory-motor system in reason and language. Cognitive Neuropsychology, 22, 455–479.CrossRef

Gallese, V., & Metzinger, T. (2003). Motor ontology: The representational reality of goals, actions, and selves. Philosophical Psychology, 13(3), 365–388.CrossRef

Gardenfors, P. (2004). Cooperation and the evolution of symbolic communication. In K. Oller & U. Griebel (Eds.), Evolution of communication systems: A comparative approach (pp. 237–256). Cambridge, MA: MIT Press.

Gardenfors, P., & Orvath, M. (2005). The evolution of anticipatory cognition as a precursor to symbolic communication. In S. Brook (Ed.), Proceedings of the Morris Symposium on the Evolution of Language, NY, USA.

Gattis, M. (2001). Spatial schemas in abstract thought. Cambridge: MIT Press.

Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2003). Learning precise timing with lstm recurrent networks. Journal of Machine Learning Research, 3, 115–143.MathSciNetCrossRefMATH

Ghez, C., & Thach, W. T. (2000). Principles of neural science, chapter The cerebellum (4th ed., pp. 833–835). New York: McGraw-Hill.

Gibson, J. (1966). The senses considered as perceptual systems. Boston, MA: Houghton Mifflin.

Gibson, J. (1979). The ecological approach to visual perception. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Glenberg, A. (1997). What memory is for. Behavioral and Brain Sciences, 20, 1–55.

Goldman, A. (2005). Imitation, mind reading, and simulation. In S. Hurley & N. Chater (Eds.), Perspectives on imitation II (pp. 80–81). Cambridge, MA: MIT Press.

Gorniak, P., & Roy, D. (2007). Situated language understanding as filtering perceived affordances. Cognitive Science, 31(2), 197–231.

Greenwald, A. G. (1970). Sensory feedback mechanisms in performance control: With special reference to the ideomotor mechanism. Psychological Review, 77, 73–99.CrossRef

Gross, H.-M., Volker, S., & Torsten, S. (1999). A neural architecture for sensorimotor anticipation. Neural Networks, 12, 1101–1129.CrossRef

Grush, R. (1997). The architecture of representation. Philosophical Psychology, 10(1), 5–25.CrossRef

Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27(3), 377–396.

Gurney, K., Prescott, T., & Redgrave, P. (2001). A computational model of action selection in the basal ganglia, i. a new functional anatomy. Biological Cybernetics, 84, 401–410.CrossRefMATH

Haken, H. (1988). Information and self-organization, a macroscopic approach to complex systems. Berlin/New York: Springer-Verlag.MATH

Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42, 335–346.CrossRef

Haruno, M., Wolpert, D., & Kawato, M. (2003). Hierarchical mosaic for movement generation. In T. Ono, G. Matsumoto, R. Llinas, A. Berthoz, H. Norgren, & R. Tamura (Eds.) Excepta medica international coungress series. Amsterdam: Elsevier Science.

Heidegger, M. (1977). Sein und Zeit. Tübingen: Niemeyer.

Hesslow, G. (2002). Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences, 6, 242–247.CrossRef

Hoffmann, H. (2007). Perception through visuomotor anticipation in a mobile robot. Neural Networks, 20, 22–33.CrossRefMATH

Hoffmann, H., & Moller, R. (2004). Action selection and mental transformation based on a chain of forward models. In S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam, & J.-A. Meyer (Eds.), From Animals to Animats 8, Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior (pp. 213–222). Los Angeles, CA: MIT Press.

Hoffmann, J. (1993). Vorhersage und Erkenntnis: Die Funktion von Antizipationen in der menschlichen Verhaltenssteuerung und Wahrnehmung [Anticipation and cognition: The function of anticipations in human behavioral control and perception]. Goettingen, Germany: Hogrefe.

Hoffmann, J., Stöcker, C., & Kunde, W. (2004). Anticipatory control of actions. International Journal of Sport and Exercise Psychology, 2, 346–361.

Hommel, B. (2003). Planning and representing intentional action. The Scientific World Journal, 3, 593–608.

Hommel, B., Musseler, J., Aschersleben, G., & Prinz, W. (2001). The theory of event coding (tec): A framework for perception and action planning. Behavioral and Brain Science, 24(5), 849–878.CrossRef

Houk, J. C., & Wise, S. P. (1995). Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: Their role in planning and controlling action. Cerebral Cortex, 5(2), 95–110.CrossRef

Hurley, S. L. (2005). Active perception and perceiving action: The shared circuits hypothesis. In T. Gendler & J. Hawthorne (Eds.), Perceptual experience. Oxford: Oxford University Press.

Hurley, S., & Chater, N. (2004). Perspectives on imitation: From cognitive neuroscience to social science. Cambridge, MA: MIT Press.

Husserl, E. (1982). Ideas pertaining to a pure phenomenology and to a phenomenological philosophy. Nijhoff: The Hague.

Iacoboni, M. (2002). Modulation of motor and premotor activity during imitation of target-directed actions. Cereb Cortex, 12, 847–855.CrossRef

Iacoboni, M. (2003). Understanding others: Imitation, language, empathy. In S. Hurley & N. Chater (Eds.), Perspectives on imitation: From cognitive neuroscience to social science. Cambridge, MA: MIT Press.

Jacobs, R., Jordan, M., Nowlan, S., & Hinton, G. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.CrossRef

James, W. (1890). The principles of psychology. New York: Dover Publications.

James, M., Singh, S., & Littman, M. (2004). Planning with predictive state representations. In Proceedings of the International Conference on Machine Learning and Applications (pp. 304–311).

Jeannerod, M. (1979). Visuomotor experiments: Failure to replicate, or failure to match the theory? Behavioral and Brain Sciences, 2, 71.

Jeannerod, M. (1994). The representing brain: Neural correlates of motor intention and imagery. The Behavioral and Brain Sciences, 17, 187–245.

Jeannerod, M. (1997). The cognitive neuroscience of action. Oxford: Blackwell.

Jeannerod, M. (2001). Neural simulation of action: A unifying mechanism for motor cognition. NeuroImage, 14, S103–S109.CrossRef

Jeannerod, M., & Decety, J. (1995). Mental motor imagery: A window into the representational stages of action. Current Opinion in Neurobiology, 5(6), 727–732.CrossRef

Jeannerod, M., & Pacherie, E. (2004). Agency, simulation, and self-identification. Mind and Language, 19(2), 113–146.CrossRef

Johnson, M., & Demiris, Y. (2005a). Hierarchies of coupled inverse and forward models for abstraction in robot action planning, recognition and imitation. In Proceedings of the AISB 2005 Symposium on Imitation in Animals and Artifacts (pp. 69–76). Hertfordshire, UK.

Johnson, M., & Demiris, Y. (2005b). Perceptual perspective taking and action recognition. International Journal of Advanced Robotic Systems, 2(4), 301–309.

Johnson-Laird, P. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge: Cambridge University Press and Harvard University Press.

Jordan, M. I., & Rumelhart (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16, 307–354.CrossRef

Kant, I. (1781/1998). Critique of pure reason. Cambridge: Cambridge University Press.

Kaplan, F., & Hafner, V. (2006). The challenges of joint attention. Interaction Studies, 7(2).

Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. New York: Oxford University Press.

Kawato, M. (1990). Computational schemes and neural network models for formulation and control of multijoint arm trajectories. In W. T. Miller, R. S. Sutton, & P. J. Werbos (Eds.), Neural Networks for Control (pp. 197–228). Cambridge, MA: MIT Press.

Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718–727.CrossRef

Keijzer, F. (2001). Representation and behavior. Cambridge, MA: MIT Press.

Kelso, J. A. (1991). Anticipatory dynamical systems, intrinsic pattern dynamics and skill learning. Human Movement Science, 10(1), 93–111.CrossRef

Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, Mass: MIT Press.

Knoblich, G., & Prinz, W. (2005). Higher-order motor disorders, chapter Linking perception and action: An ideomotor approach (pp. 79–104). Oxford, UK: Oxford University Press.

Kondo, T., & Ito, K. (2006). A design principle of adaptive neural controllers for realizing anticipatory behavior in reaching movement under unexperienced environments. In M. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre, (Eds.), Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006).

Kording, K., & Wolpert, D. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10, 319–326.CrossRef

Kosslyn, S. M., & Sussman, A. (1994). Roles of imagery in perception: Or, there is no such thing as immaculate perception. In M. Gazzaniga (Ed.), The cognitive neurosciences (pp. 1035–1042). Cambridge, MA: MIT Press.

Kuipers, B., Beeson, P., Modayil, J., & Provost, J. (2006). Bootstrap learning of foundational representations. Connection Science (accepted for publication), 18(2).

Kunde, W., Koch, I., & Hoffmann, J. (2004). Anticipated action effects affect the selection, initiation and execution of actions. The Quarterly Journal of Experimental Psychology. Section A: Human Experimental Psychology, 57(1), 87–106.CrossRef

Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press.

Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought. Basic Books.

Landauer, T. K. (1962). Rate of implicit speech. Perceptual and Motor Skills, 15, 646.

Littman, M., Sutton, R., & Singh, S. (2001). Predictive representations of state. In Proceedings of NIPS-02, Vancouver.

Lu, X., & Ashe, J. (2005). Anticipatory activity in primary motor cortex codes memorized movement sequences. Neuron, 45(6), 967–973.CrossRef

Lungarella, M., Metta, G., Pfeifer, R., & Sandini, G. (2004). Developmental robotics: A survey. Connection Science, 0(0), 1–40.

Maravita, A., Spence, C., & Driver, J. (2003). Multisensory integration and the body schema: Close to hand and within reach. Current Biology, 13(13), R531–R539.CrossRef

Markman, A. B., & Dietrich, E. (2000). Extending the classical view of representation. Trends in Cognitive Sciences, 4(12), 470–475.CrossRef

Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of living. Dordrecht, Holland: D. Reidel Pub.

Mehta, B., & Schaal, S. (2002). Forward models in visuomotor control. Journal of Neurophysiology, 88, 942–953.

Mel, B. (1990). Vision-based robot motion planning. In W. T. Miller III, R. S. Sutton, & P. Werbos (Eds.), Neural Networks for Control. Bradford: MIT.

Meltzoff, A., & Prinz, W. (2002). The imitative mind: Development, evolution and brain bases. Cambridge University Press, Cambridge.

Merleau-Ponty, M. (1945). Phénoménologie de la perception. Paris: Gallimard.

Miall, R. C., & Wolpert, D. M. (1996). Forward models for physiological motor control. Neural Networks, 9(8), 1265–1279.CrossRefMATH

Middleton, F. A., & Strick, P. L. (2000). Basal ganglia output and cognition: Evidence from anatomical, behavioral, and clinical studies. Brain and Cognition, 42(2), 183–200.CrossRef

Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt, Rinehart and Winston.

Mohan, V., & Morasso, P. (2006). A forward/inverse motor controller for cognitive robotics. In Proceedings of the International Conference on Artifical Neural Networks (pp. 602–611).

Neisser, U. (1976). Cognition and reality. San Francisco, CA: Freeman.

Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.

Nolfi, S. (2002). Power and limits of reactive agents. Neurocomputing, 49, 119–145.CrossRef

Nolfi, S. (2005). Behaviour as a complex adaptive system: On the role of self-organization in the development of individual and collective behaviour. ComplexUs, 2, 195–203.CrossRef

O’Regan, J., & Noe, A. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24(5), 883–917.

Oudeyer, P.-Y., Kaplan, F., Hafner, V. V., & Whyte, A. (2005). The playground experiment: Task-independent development of a curious robot. In D. Bank & L. Meeden (Eds.), Proceedings of the AAAI Spring Symposium on Developmental Robotics, 2005 (pp. 42–47). Stanford, California.

Pacherie, E., & Dokic, J. (2006). From mirror neurons to joint actions. Cognitive Systems Research, 7(2–3), 101–112.CrossRef

Paine, R. W., & Tani, J. (2005). How hierarchical control self-organizes in artificial adaptive systems. Adaptive Behavior, 13(3), 211–225.CrossRef

Peirce, C. S. (1897/1940). Philosophical writings of Peirce, chapter Logic as semiotic: The theory of signs. Dover.

Pezzulo, G. (2006). How can a massively modular mind be context-sensitive? A computational approach. In Proceedings of the 7th International Conference on Cognitive Modeling 2006.

Pezzulo, G., Baldassarre, G., Butz, M. V., Castelfranchi, C., & Hoffmann, J. (2007). From actions to goals and vice-versa: Theoretical analysis and models of the ideomotor principle and tote. In M. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory behavior in adaptive learning systems: Advances in anticipatory processing, LNAI 4520. Springer.

Pezzulo, G., & Calvi, G. (2006a). A schema based model of the praying mantis. In S. Nolfi, G. Baldassarre, R. Calabretta, J. Hallam, D. Marocco, O. Miglino, J.-A. Meyer, & D. Parisi (Eds.), From Animals to Animats 9: Proceedings of the Ninth International Conference on Simulation of Adaptive Behaviour, volume LNAI 4095 (pp. 211–223). Berlin, Germany: Springer Verlag.

Pezzulo, G., & Calvi, G. (2006b). Toward a perceptual symbol system. In Proceedings of the Sixth International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems. Lund University Cognitive Science Studies 118.

Pezzulo, G., & Calvi, G. (2007). Schema-based design and the akira schema language: An overview. In M. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory behavior in adaptive learning systems: Advances in anticipatory processing, LNAI 4520. Springer.

Pezzulo, G., Calvi, G., Ognibene, D., & Lalia, D. (2005). Fuzzy-based schema mechanisms in akira. In CIMCA ’05: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (Vol. 2, pp. 146–152). Washington, DC, USA: IEEE Computer Society.

Pezzulo, G., & Castelfranchi, C. (2007). The symbol detachment problem. Cognitive Processing, 8(2), 115–131.CrossRef

Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press.

Piaget, J. (1954). The construction of reality in the child. Ballentine.

Piaget, J. (1985). Equilibration of cognitive structures. University of Chicago Press.

Pierce, D. M., & Kuipers, B. J. (1997). Map learning with uninterpreted sensors and effectors. Artificial Intelligence, 92, 169–227.CrossRefMATH

Popper, K. R. (1996). Alles Leben ist Problemlösen. Über Erkenntnis, Geschichte und Politik. München: R. Piper-Verlag.

Port, R., & van Gelder, T. (1995). Mind as motion: Explorations in the dynamics of cognition. Cambridge MA: MIT Press.

Prinz, W. (1990). A common coding approach to perception and action. In O. Neumann & W. Prinz (Eds.), Relationships between perception and action (pp. 167–201). Berlin: Springer Verlag.

Prinz, W. (2005). An ideomotor approach to imitation. In S. Hurley & N. Chater (Eds.), Perspectives on imitation: From neuroscience to social science (Vol. 1, pp. 141–156). Cambridge, MA: MIT Press.

Rainer, G., Rao, S. C., & Miller, E. K. (1999). Prospective coding for objects in primate prefrontal cortex. The Journal of Neuroscience, 19(13), 5493–5505.

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.CrossRef

Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019–1025.CrossRef

Rizzolatti, G., & Arbib, M. A. (1998). Language within our grasp. Trends in Neurosciences, 21(5), 188–194.CrossRef

Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192.CrossRef

Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3.

Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2, 661–670.CrossRef

Roitblat, H. (1980). Codes and coding processes in pigeon short-term memory. Animal Learning & Behavior, 8, 341–351.

Rosen, R. (1985). Anticipatory systems. Pergamon Press.

Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose and teleology. Philosophy of Science, 10(1), 18–24.CrossRef

Roy, D. (2005). Semiotic schemas: A framework for grounding language in action and perception. Artificial Intelligence, 167(1–2), 170–205.CrossRef

Roy, D., yuh Hsiao, K., Mavridis, N., & Gorniak, P. (2006). Ripley, hand me the cup: Sensorimotor representations for grounding word meaning. In International Conference of Automatic Speech Recognition and Understanding.

Ruby, P., & Decety, J. (2001). Effect of subjective perspective taking during simulation of action: A pet investigation of agency. Nature Neuroscience, 4(5), 546–550.

Ryle, G. (1949). The concept of mind. New York: Barnes and Noble.

Scassellati, B. (1999). Imitation and mechanisms of joint attention: A developmental structure for building social skills on a humanoid robot. Lecture Notes in Computer Science, 1562, 176–195.CrossRef

Schenck, W., & Moller, R. (2006). Learning a visual forward model for a robot camera head. In M. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006).

Schmidhuber, J. (1991). Adaptive confidence and adaptive curiosity. Technical Report FKI-149-91, Institut für Informatik, Technische Universitat.

Schmidhuber, J. (2002). Exploring the predictable. In S. Ghosh & S. Tsutsui (Eds.), Advances in evolutionary computing (pp. 579–612). Springer.

Schomaker, L. (2004). Anticipation in cybernetic systems: A case against mindless anti-representationalism. In IEEE lntemational Conference on Systems, Man and Cybernetics, pp. 2037–2045.

Schöner, G. (2002). Dynamical systems approaches to neural systems and behavior. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences (pp. 10571–10575). Pergamon: Oxford.

Schöner, G., & Kelso, J. A. S. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239, 1513–1520.CrossRef

Schubotz, R. I. (2007). Prediction of external events with our motor system: Towards a new framework. Trends in Cognitive Sciences, 11(5), 211–218.CrossRef

Schultz, W., Dayan, P., & Montague, P. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.CrossRef

Shanahan, M. (2005). Cognition, action selection, and inner rehearsal. In Proceedings IJCAI 2005 Workshop on Modelling Natural Action Selection, pp. 92–99.

Simons, D. J., & Levin, D. T. (1997). Change blindness. Trends in Cognitive Sciences, 1(7), 261–267.CrossRef

Sloman, A. (2006). Orthogonal recombinable competences acquired by altricial species. Technical report, University of Birmingham.

Steels, L. (2003). Intelligence with representation. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 361(1811), 2381–2395.MathSciNetCrossRef

Stephan, V., & Gross, H.-M. (2003). Visuomotor anticipation – a powerful approach to behavior-driven perception. Künstliche Intelligenz, 17(2), 12–17.

Stern, D. (2004). The present moment. New York, London: W.W. Norton & Company.

Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3, 9–44.

Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88(2), 135–170.CrossRef

Tani, J. (2003). Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Networks, 16(1), 11–23.CrossRef

Tani, J., Ito, M., & Sugita, Y. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using rnnpb. Neural Networks, 17(8–9), 1273–1289.CrossRef

Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks, 12(7–8), 1131–1141.CrossRef

Taylor, C. (1971). What is involved in a genetic psychology? In T. Mischel (Ed.), Cognitive development and epistemology (pp. 393–416). New York: Academic Press.

Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of perception and action. MIT Press.

Tinbergen, N. (1951). The study of instinct. New York: Oxford University Press.

Tolman, E. (1932). Purposive behavior in animals and men. New York: Appleton-Century-Crofts.

Tomasello, M., Kruger, A., & Ratner, H. (1993). Cultural learning. Behavioral and Brain Sciences, 16, 495–552.CrossRef

Toussaint, M. (2006). A sensorimotor map: Modulating lateral interactions for anticipation and planning. Neural Computation, 18(5), 1132–1155.CrossRefMATH

Tsoukalas, L. H. (1998). Neurofuzzy approaches to anticipation: A new paradigm for intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 28(4), 573–582.CrossRef

van Gelder, T. (1995). What might cognition be, if not computation? Journal of Philosophy, 92(7), 345–381.CrossRef

Varela, F. J., Thompson, E. T., & Rosch, E. (1992). The embodied mind: Cognitive science and human experience. The MIT Press.

Vaughan, R. T., & Zuluaga, M. (2006). Use your illusion: Sensorimotor self-simulation allows complex agents to plan with incomplete self-knowledge. In Proceedings of the International Conference on Simulation of Adaptive Behaviour (SAB), Rome, Italy.

Vijayakumar, S., D’souza, A., & Schaal, S. (2005). Incremental online learning in high dimensions. Neural Computation, 17(12), 2602–2634.MathSciNetCrossRef

von Holst, E., & Mittelstaedt, H. (1950). Das reafferenzprinzip. Naturwissenschaften, 37, 464–476.CrossRef

von Uexküll, J. (1985). Environment [umwelt] and inner world of animals. In G. M. Burghardt (Ed.), Foundations of comparative ethology (pp. 222–245). New York: Van Nostrand Reinhold.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.

Weng, J., & Zhang, Y. (2002). Developmental robots: A new paradigm. In Proceedings Second International Workshop on Epigenetic Robotics.

Wickens, J. (1997). Basal ganglia: Structure and computations. Network: Computation in Neural Systems, 8, R77–R109.CrossRef

Witkowski, M. (2003). Towards a four factor theory of anticipatory learning. In M. Butz, O. Sigaud, & P. Gérard (Eds.), Anticipatory behavior in adaptive learning systems, LNAI 2684 (pp. 66–85). Springer.

Wittgenstein, L. (1953). Philosophical investigations. Oxford: Basil Blackwell.

Wohlschlger, A., Gattis, M., & Bekkering, H. (2003). Action generation and action perception in imitation: An instance of the ideomotor principle. Philosophical Transactions of the Royal Society of London, 358, 501–515.CrossRef

Wolfe, B., James, M. R., & Singh, S. (2005). Learning predictive state representations in dynamical systems without reset. In ICML ’05: Proceedings of the 22nd International Conference on Machine learning (pp. 980–987). New York, NY, USA: ACM Press.

Wolfe, B., & Singh, S. (2006). Predictive state representations with options. In ICML ’06: Proceedings of the 23rd International Conference on Machine learning (pp. 1025–1032). New York, NY, USA: ACM Press.

Wolpert, D. M. (1997). Computational approaches to motor control. Trends in Cognitive Sciences, 1, 209–216.CrossRef

Wolpert, D. M., Doya, K., & Kawato, M. (2003). A unifying computational framework for motor control and social interaction. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358(1431), 593–602.CrossRef

Wolpert, D., & Flanagan, J. (2001). Motor prediction. Current Biology, 11, R729–R732.CrossRef

Wolpert, D. M., Gharamani, Z., & Jordan, M. (1995). An internal model for sensorimotor integration. Science, 269, 1179–1182.CrossRef

Wolpert, D. M., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11(7–8), 1317–1329.CrossRef

Ziemke, T., Jirenhed, D.-A., & Hesslow, G. (2005). Internal simulation of perception: A minimal neuro-robotic model. Neurocomputing, 68, 85–104.CrossRef

Zlatev, J., & Balkenius, C. (2001). Introduction: Why “epigenetic robotics”? In Proceedings of the First International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (Vol. 85, pp. 1–4). Lund University Cognitive Studies.

Zlatev, J., Persson, T., & Gardenfors, P. (2005). Bodily mimesis as ’the missing link’ in human cognitive evolution. Technical Report LUCS121, Lund University Cognitive Science, Lund, Sweden.

Titel: Coordinating with the Future: The Anticipatory Nature of Representation
verfasst von: Giovanni Pezzulo
Publikationsdatum: 01.06.2008
Verlag: Springer Netherlands
Erschienen in: Minds and Machines / Ausgabe 2/2008
Print ISSN: 0924-6495
Elektronische ISSN: 1572-8641
DOI: https://doi.org/10.1007/s11023-008-9095-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2008

Sergei Nirenburg, Victor Raskin, Ontological Semantics

Robosemantics: How Stanley the Volkswagen Represents the World

Jon Williamson, Bayesian Nets and Causality: Philosophical and Computational Foundations

Turing’s Responses to Two Objections

Samir Chopra, Scott D. Dexter, Decoding Liberation: The Promise of Free and Open Source Software

The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence

Premium Partner