Skip to main content

Advertisement

Log in

Enaction-Based Artificial Intelligence: Toward Co-evolution with Humans in the Loop

  • Published:
Minds and Machines Aims and scope Submit manuscript

Abstract

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. These difficulties also relate to the connectionist approaches which, in this context, constitute a cognitive background, maintaining cognition at the status of a entrance/exit processing system.

References

  • Ashby, W. (1960). Design for a brain: The origin of adaptive behavior (2nd ed.). London: Chapman and Hall.

    Google Scholar 

  • Aubin, J. (1991). Viability theory. Basel: Birkhuser.

    MATH  Google Scholar 

  • Auvray, M., Hanneton, S., Lenay, C., & O’Regan, K. (2005). There is something out there: Distal attribution in sensory substitution, twenty years later. Journal of Integrative Neuroscience, 4, 505–521.

    Article  Google Scholar 

  • Beer, R. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91–99.

    Article  MathSciNet  Google Scholar 

  • Beer, R. (2004). Autopoiesis and cognition in the game of life. Artificial Life, 10(3), 309–326.

    Article  Google Scholar 

  • Beer, R., & Gallagher, J. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior, 1(1), 91–122.

    Article  Google Scholar 

  • Bersini, H., & Sener, P. (2002). Le chaos dans les rseaux de neurones. In Approche dynamique de la cognition artificielle (pp. 45–58). Hermes, Trait des sciences cognitives.

  • Beurier, G., Michel, F., & Ferber, J. (2006). A morphogenesis model for multiagent embryogeny. In Artificial life X, Proceedings of the 10th international conference (pp. 84–90).

  • Beurier, G., Simonin, O., & Ferber, J. (2002). Model and simulation of multimodel emergence. In Procedings of IEEE ISSPIT (pp. 231–236).

  • Bickhard, M. H. (2003). The biological emergence of representation. In: T. Brown, & L. Smith (Eds.), Emergence and reduction: Proceedings of the 29th annual symposium of the Jean Piaget Society (pp. 105–131).

  • Bourgine, P., & Stewart, J. (2004). Autopoiesis and cognition. Artificial Life, 10, 327–345.

    Article  Google Scholar 

  • Brogni, A., Vinayagamoorthy, V., Steed, A., & Slater, M. (2007). Responses of participants during an immersive virtual environment experience. The International Journal of Virtual Reality, 6(2), 1–10.

    Google Scholar 

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

    Article  Google Scholar 

  • Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2000). Investigating generalization in the anticipatory classifier system. In Proceedings of parallel problem solving from nature (PPSN VI). Also technical report 2000014 of the Illinois Genetic Algorithms Laboratory.

  • Chalmer, D. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2, 200–219.

    Google Scholar 

  • Chandrasekharan, S., & Stewart, T. (2007). The origin of epistemic structures and proto-representations. Adaptive Behavior, 15(3), 329–353.

    Article  Google Scholar 

  • Clark, A. (1999). An embodied cognitive science? Trends in Cognitive Science, 9, 345–351.

    Article  Google Scholar 

  • Daucé, E. (2002). Systèmes dynamiques pour les sciences cognitives. In Approche dynamique de la cognition artificielle (pp. 33–44). Hermes, Traité des sciences cognitives.

  • Daucé, E., Quoy, M., Cessac, B., Doyon, B., & Samuelides, M. (1998). Selforganization and dynamics reduction in recurrent networks: Stimulus presentation and learning. Neural Networks, 11, 521–533.

    Article  Google Scholar 

  • De Jaegher, H., & Di Paolo, E. (2007). Participatory sense-making an enactive approach to social cognition. Phenomenology and the Cognitive Sciences, 6(4), 485–507.

    Google Scholar 

  • De Loor, P., Bénard, R., & Bossard, C. (2008). Interactive co-construction to study dynamical collaborative situations. To appear in the Proceedings in the international conference on virtual reality, laval virtual.

  • Dellaert, F., & Beer, R. (1994). Toward an evolvable model of development for autonomous agent synthesis. In: Artificial Life IV, Proceedings of the 4th international workshop on the synthesis and simulation of living systems (pp. 246–257).

  • Dempster, B. (2000). Sympoietic and autopoietic systems: A new distinction for self-organizing systems. In Proceedings of the world congress of the systems sciences and ISSS 2000 (pp. 1–18).

  • Desmeulles, G., Querrec, G., Redou, P., Kerdlo, S., Misery, L., Rodin, V., et al. (2006). The virtual reality applied to the biology understanding: The in virtuo experimentation. Expert Systems with Applications, 30(1), 82–92.

    Article  Google Scholar 

  • Di Paolo, E. (2000). Homeostatic adaptation to inversion in the visual field and other sensorimotor disruptions. In J. Meyer, A. Berthoz, D. Floreano, H. Roitblat, & S.Wilson (Eds.), From animals to animats 6. Proceedings of the VI international conference on simulation of adaptove behavior (pp. 440–449).

  • Di Paolo, E. (2005). Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences, 4, 429–452.

    Article  Google Scholar 

  • Di Paolo, E., & Iizuka, H. (2008). How (not) to model autonomous behavior. BioSystems, 91(2), 409–423.

    Article  Google Scholar 

  • Di Paolo, E., Rohde, M., & De Jaegher, H. (2007). Horizons for the enactive minds: Value, social interaction, and play. Cognitive Science Research Paper, 587.

  • Dittrich, P., Ziegler, J., & Banzhaf, W. (2001). Artificial chemistries—a review. Artificial Life, 7(3), 225–275.

    Article  Google Scholar 

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

    MATH  Google Scholar 

  • Dreyfus, H. (1979). What computers can’t do. The limits of artificial intelli-gence. New York: Harper&Row, Publisher, Inc.

    Google Scholar 

  • Dreyfus, H. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Philosophical Psychology, 20(2), 247–268.

    Article  Google Scholar 

  • Eggenberger, P. (2004). Genome-physics interaction as a new concept to reduce the number of genetic parameters in artificial evolution. In R. Sarke, R. Reynolds, H. Abbass, K.-C. Tan, R. McKay, D. Essam, & T. Gedeon (Eds.), Proceedings of the IEEE 2003 congress on evolutionary computation (pp. 191–198). Congress of Evolutionary Computation.

  • Favier, P., & De Loor, P. (2006). From decision to action: Intentionality, a guide for the specification of intelligent agents’ behaviour. International Journal of Image and Graphics, 6(1), 87–99.

    Article  Google Scholar 

  • Federici, D., & Downing, K. (2006). Evolution and development of a multicellular organism: Scalability, resilience, and neutral complexification. Artificial Life, 12(3), 381–409.

    Article  Google Scholar 

  • Floreano, D., Mitri, S., Magnenat, S., & Keller, L. (2007). Evolutionary conditions for the emergence of communication in robots. Current Biology, 17(6), 514–519.

    Article  Google Scholar 

  • Floreano, D., & Urzelai, J. (2000). Evolutionary robots with on-line selforganization and behavioral fitness. Neural Networks, 13, 431–443.

    Article  Google Scholar 

  • Fodor, J. (2000). The mind doesn’t work that way. Cambridge, MA: MIT Press.

    Google Scholar 

  • Foerster, H. (1984). On constructing a reality. In The invented reality: How do we know what we belive we now? (Contributions to constructivism) (pp. 41–61). Northon and Company.

  • Freeman, W. (2001). How brains make up their minds. New York: Columbia University Press.

    Google Scholar 

  • Freeman, W., & Sharkda, C. (1990). Representations: Who needs them? In Brain organization and memory cells, systems & circuits (pp. 375–380). New-York: Oxford University Press.

  • Froese, T., & Ziemke, T. (2009). Enactive artificial intelligence: Investigating the systemic organization of life and mind. Artificial Intelligence, 173, 466–500.

    Article  Google Scholar 

  • Funahashi, K., & Nakamura, N. (1993). Approximation of dynamical systems by continuous time recurrent neural networks. Neural Networks, 6, 801–806.

    Article  Google Scholar 

  • Gapenne, O. (2008). Kinaesthetics and the construction of perceptual objects. In Enaction: A new paradigm for cognitive science. MIT Press.

  • Gardner, M. (1970). The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.

    Article  Google Scholar 

  • Gerard, P., Stolzmann, W., & Sigaud, O. (2002). YACS, a new LCS using anticipation. Journal of Soft Computing, 6(3–4), 216–228.

    MATH  Google Scholar 

  • Gershenson, C. (2004). Cognitive paradigms: Which one is the best? Cognitive Systems Research, 5(2), 135–156.

    Article  Google Scholar 

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

    Google Scholar 

  • Glasersfeld, E. V. (1995). Radical constructivism: A way of knowing and learning. London: Falmer Press.

    Google Scholar 

  • Goldin, D., & Wegner, P. (2008). The interactive nature of computing: Refuting the strong Church–Turing thesis. Minds and Machines, 18(1), 17–38.

    Article  Google Scholar 

  • Gruau, F. (1994). Automatic definition of modular neural networks. Adaptive Behavior, 3, 151–183.

    Article  Google Scholar 

  • Hall, J. S. (2007). Self-improving AI: An analysis. Minds and Machines, 17(3), 249–259.

    Article  Google Scholar 

  • Hara, D. F., & Pfeifer, R. (2003). Morpho-functional machines the new species: Designing embodied intelligence. Berlin: Springer.

    Google Scholar 

  • Harnad, S. (1990). The symbol grounding problem. Physica, 42, 335–346.

    Google Scholar 

  • Harnad, S. (1993). Grounding symbols in the analog world with neural nets. NETS.THINK 2, 2, 1.

  • Harvey, I., Di Paolo, E., Wood, R., Quinn, M., & Tuci, E. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life, 11, 79–98.

    Article  Google Scholar 

  • Henry, F., Daucé, E., & Soula, H. (2007). Temporal pattern identification using spike-timing dependent plasticity. Neurocomputing, 70, 2009–2016.

    Article  Google Scholar 

  • Holland, J. H., & Reitman, J. S. (1978). Cognitive systems based on adaptive algorithms. In D. A. Waterman, & F. Hayes-Roth (Eds.), Pattern-directed inference systems. New York: Academic Press. (Reprinted in: Evolutionary computation. The fossil record, ISBN: 0-7803-3481-7, by David B. Fogel Ed., 1998, IEEE Press.

  • Hornby, G. S., & Pollack, J. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8, 223–246.

    Article  Google Scholar 

  • Husserl, E. (1938). Experience et jugement. PUF (1991).

  • Husserl, E. (1960). Cartesian meditations: An introduction to phenomenology (Dorian Cairns, Trans.). The Hagues: Martinus Nijhoff.

  • Hutchins, E. (2005). Material anchors for conceptual blends. Journal of pragmatics, 37, 1555–1577.

    Article  Google Scholar 

  • Hutchins, E. (2006). Imagining the cognitive life of things. Presented at the symposium: “The Cognitive Lifer of Things: Recasting the boundaries of Mind”, organized at the McDonald Institute for Archeological Research, Cambridge University.

  • Hutton, T. (2007). Evolvable self-reproducing cells in a two-dimensional artificial chemistry. Artificial Life, 13(1), 11–30.

    Article  MathSciNet  Google Scholar 

  • Iizuka, H., & Di Paolo, E. (2007). Toward Spinozist robotics: Exploring the minimal dynamics of behavioural preference. Adaptive Behavior, 15, 359–376.

    Article  Google Scholar 

  • Ikegami, T., & Suzuki, K. (2008). From a homeostatic to a homeodynamic self. BioSystems, 91, 388–400.

    Article  Google Scholar 

  • Jonas, H. (1968). Biological foundations of individuality. International Philosophical Quartely, 8, 231–251.

    Google Scholar 

  • Kant, I. (1790). Kritik der urteilskraft. Hacket Publishing Compagny, 1987.

  • Korb, K. B. (2004). The frame problem: An AI fairy tale. Minds and Machines, 8(3), 317–351.

    Article  MathSciNet  Google Scholar 

  • Kosslyn, S., Thomson, W., & Ganis, G. (2006). The case for mental imagery. New York, NY: Oxford University Press.

    Google Scholar 

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

    Google Scholar 

  • Langton, C. (1984). Self-reproduction in cellular automata. Physica D, 10, 135–144.

    Article  Google Scholar 

  • Laughlin, R. (2005). A different universe: Reinventing physics from the bottom down. New York: Basic Books.

    Google Scholar 

  • Lenay, C. (1996). Mental symbols and genetic symbols: analogies between theoretical perspectives in biology and cognitive science. Behavioural Processes, 35, 251–262.

    Article  Google Scholar 

  • Luciani, A., & Cadoz, C. (Eds.), (2007). In Enactive/07. enaction in arts. 4th International conference on enactive interfaces, Grenoble, France.

  • Lutz, A., Lachaux, J., Martinerie, J., & Varela, J. (2001). Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task. In Proceedings of the national academy of sciences (pp. 1–6).

  • Madina, D., Ono, N., & Ikegami, T. (2003). Cellular evolution in a 3D lattice artificial chemistry. In ECAL (pp. 59–68).

  • Manac’h, K., & De Loor, P. (2007). Passage du discret au continu: identification des verrous pour la simulation de systmes autopoitiques. In ARCo’07, Colloque de l’Association pour la Recherche Cognitive. Cognition-Complexité-Collectif (pp. 29–30).

  • Manac’h, K. & De Loor, P. (2009) Guiding for associative Learning: How to shape artificial dynamic cognition. To appear in the Proceedings of the 10th European conference on artificial life, Budapest.

  • Mataric, M. J. (2001). Learning in behavior-based multi-robot systems: Policies, models, and other agents. Cognitive Systems Research, 2(1), 81–93.

    Article  Google Scholar 

  • Maturana, H., Uribe, G., & Frenk, S. (1968). A biological theory of relativistic colour coding in the primat retina. Archivos de Biología y Medicina Experimentales, 1, 1–30.

    Google Scholar 

  • Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living. Boston: Reidel.

    Google Scholar 

  • McCarthy, J. (1969). Programs with common sense. In Semantic information processing (pp. 403–418). Cambridge: MIT.

  • McCarthy, J., & Buva, S. (1998). Formalizing context (expanded notes). In A. Aliseda, R. van Glabbeek, & C. Westersthl (Eds.), Computing natural language, in CSLI Lecture Notes (Vol. 8, pp. 13–50). Stanford, CA: CSLI Publications.

    Google Scholar 

  • McGee, K. (2005). Enactive cognitive science. Part 1: Background and research themes. Constructivist Foundations, 1(1), 19–34.

    MathSciNet  Google Scholar 

  • McGee, K. (2006). Enactive cognitive science. Part 2: Methods, insights, and potential. Constructivist Foundations, 1(2), 73–82.

    Google Scholar 

  • McGeer, T. (1990). Passive dynamic walking. International Journal of Robotics Research, 9(2), 62–82.

    Article  Google Scholar 

  • McMullin, B. (2004). Thirty years of computational autopoiesis: A review. Artificial Life, 10, 277–295.

    Article  Google Scholar 

  • Merleau-Ponty, M. (1945). Phénomnologie de la perception. Collection “Tel “, 1990. éditions gallimard edition.

  • Miller, J. (2003). Evolving developmental programs for adaptation, morphogenesis, and self-repair. In Advances in artificial life: 7th European conference, LNAI (Vol. 2801, pp. 256–265).

  • Minsky, M. (1982). Why people think computers can’t. AI Magazine, 3(4), 224–236.

    Google Scholar 

  • Moreno, A., Etxeberria, A., & Umerez, J. (2008). The autonomy of biological individuals and artificial models. BioSystems, 91, 308–319.

    Article  Google Scholar 

  • Morin, E. (1980). La vie de la vie (t. 2). Le Seuil, Nouvelle édition, coll. Points.

  • Neildeiz, T. M. A., Parisot, A., Vignal, C., Rameau, P., Stockholm, D., Picot, J., et al. (2008). Epigenetic gene expression noise and phenotypic diversification of clonal cell populations. Differentiation, 76, 33–40.

    Google Scholar 

  • Noë, A. (2004). Action in perception. Cambridge, MA: MIT Press.

    Google Scholar 

  • Nolfi, S., & Floreano, D. (1998). How co-evolution can enhance the adaptive power of artificial evolution: Implications for evolutionary robotics. In Lecture notes in computer science (Vol. 1468, pp. 22–38). Berlin/Heidelberg: Springer.

  • Nolfi, S., & Floreano, D. (2000). Evolutionnary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: MIT Press/Bradford Books.

    Google Scholar 

  • Nolfi, S., & Parisi, D. (1995).Genotypes for neural networks. In The handbook of brain theory and neural networks. Cambridge, MA: MIT Press.

  • Nunez, R. (1999). Could the future taste purple? Reclaiming mind, body and cognition. Journal of Consciousness Studies, 6, 41–60.

    Google Scholar 

  • Parenthoën, M., & Tisseau, J. (2005). Enactive modeling. In Tutorial book of virtual concept (pp. 1–18). Biarritz, France: ENSIAME-LAMIH/LIPSIESTIA.

  • Pfeifer, R., & Gomez, G. (2005). Interacting with the real world: design principles for intelligent systems. Artificial life and Robotics, 9(1), 1–6.

    Article  Google Scholar 

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

    Google Scholar 

  • Piaget, J. (1970). L’′epist′emologie g′en′etique. Paris: Que sais-je, PUF.

    Google Scholar 

  • Piaget, J. (1975). The requilibration of cognitive structures. The University of Chicago Press. (The original French edition: L’′equilibration des structures cognitives: Problème central du developpement, reprint 1985).

  • Pylyshyn, Z. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Pylyshyn, Z. (2003). Seeing and vizualizing: It’s not what you think. Cambridge: MIT/Bradford.

    Google Scholar 

  • Rohde, M., & Di Paolo, E. (2006). An evolutionary robotics simulation of human minimal social interaction (long abstract). In SAB’06 workshop on behaviour and mind as a complex adaptive system, Rome, Italy.

  • Rohde, M., & Stewart, J. (2008). Ascriptional and ‘genuine’ autonomy. BioSystems Special issue on Modeling Autonomy, 91(2), 424–433.

    Google Scholar 

  • Rosch, E. (1999). Reclaiming concepts. In Reclaiming cognition: The primacy of action, intention, and emotion (pp. 61–67). Imprint Academic: Thorverton, UK.

  • Rosenblatt, F. (1958). The perceptron: A probalistic model for information storage and organisation in the brain. Psychological Review, 65, 386–408.

    Article  MathSciNet  Google Scholar 

  • Ruiz-Mirazo, K., & Mavelli, F. (2008). On the way toward ‘basic autonomous agents’: Stochatic simulations of minimal lipid-peptide cells. Biosystems, 91, 374–387.

    Article  Google Scholar 

  • Sanchez, M., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature, 6, 332–339.

    Google Scholar 

  • Searle, J. (1997). The mystery of consciousness. review collection.

  • Shanon, B. (1993). The representational and the presentational: An essay on cognition and the study of mind. London and New York: Harvester-Wheatsheaf and Prentice Hall.

    Google Scholar 

  • Sharkey, N., & Ziemke, T. (1998). Biological and physiological foundations. Connection Science, 10, 361–391.

    Article  Google Scholar 

  • Simon, H. (1969). The science of the artificial. Cambridge, MA: MIT Press.

    Google Scholar 

  • Stewart, J. (1996). Cognition = life: Implication for higher-level cognition. Behavioural Process, 35, 311–326.

    Article  Google Scholar 

  • Stewart, J., & Gapenne, O. (2004). Reciprocal modelling of active perception of 2-D forms in a simple tactile-vision substitution system. Minds and Machines, 14, 309–330.

    Article  Google Scholar 

  • Stewart, J., Gapenne, O., & Di Paolo, E. (2008). Enaction: A new paradigm for cognitive science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Stockholm, D., Benchaouir, R., Picot, J., Rameau, P., Neildeiz, T. M. A., et al. (2007). The origin of phenotypic heterogeneity in a clonal cell population in vitro. PLoS ONE, 2(4), e394. doi:10.1371/journal.pone.0000394.

  • Strogatz, S. (1994). Nonlinear dynamics and chaos. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Sutton, R., & Barto, A. (1998). Reinforcement learning. Cambridge, MA: MIT Press.

    Google Scholar 

  • Thompson, E. (2007). Look again: Phenomenology and mental imagery. Phenomenology and the Cognitive Science, 6, 137–170.

    Article  Google Scholar 

  • Thompson, E., & Varela, F. J. (2001). Radical embodiment: Neural dynamics and consciousness. Trends in Cognitive Sciences, 5(10), 418–425.

    Article  Google Scholar 

  • Tisseau, J. (2001). Virtual reality—in virtuo autonomy. Acreditation to direct research, field: Computer science, University of Brest.

  • Turing, A. (1950). Computing machinery and intelligence. Minds, 59, 433–460.

    Article  MathSciNet  Google Scholar 

  • Vaario, J. (1994). Artificial life as constructivist AI. Journal of SICE (Japanese Society of Instrument and Control Engineers), 33(1), 65–71.

    Google Scholar 

  • Varela, F. (1979). The principles of biological autonomy. New York: North Holland.

    Google Scholar 

  • Varela, F., Maturana, H., & Uribe, H. (1974). Autopoiesis: The organization of living systems, its characterization, and a model. Biosystems, 5, 187–196.

    Article  Google Scholar 

  • Varela, F., Thompson, E., & Rosch, E. (1993). The embodied mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Von Neumann, J. (1966). Theory of self-reproducing autonomata. Champaign, IL: Arthur Burks.

    Google Scholar 

  • von Uexküll, J. (1957). A stroll through the worlds of animals and men. In Instinctive behavior: The development of a modern concept (pp. 5–80).

  • Vygotsky, L. S. (1986). Thought and language—rev’d edition. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Watanabe, T., Koizumi, K., Kishi, K., Nakamura, M., Kobayashi, K., Kazuno, M., et al. (2007). A uniform framework of molecular interaction for an artificial chemistry with compartments. In Proceedings of the 2007 IEEE Symposium on Artificial Life (CI-ALife 2007) (pp. 54–60).

  • Wilson, S. W. (1987). Classifier systems and the animat problem. Machine Learning, 2, 199–228. Also Research Memo RIS-36r, the Rowland Institute for Science, Cambridge, MA, 1986.

  • Wood, R., & Di Paolo, E. A. (2007). New models for old questions: Evolutionary robotics and the ‘a not b’ error. In Springer (Ed.), Proceedings of the 9th European conference on artificial life ECAL 2007.

  • Ziemke, T. (2001). The construction of ‘reality’ in the robot. Foundations of Science, 6(1), 163–233.

    Article  Google Scholar 

  • Ziemke, T. (2004). Embodied AI as science: Models of embodied cognition, embodied models of cognition, or both? LNAI, 3139, 27–36.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pierre De Loor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

De Loor, P., Manac’h, K. & Tisseau, J. Enaction-Based Artificial Intelligence: Toward Co-evolution with Humans in the Loop. Minds & Machines 19, 319–343 (2009). https://doi.org/10.1007/s11023-009-9165-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11023-009-9165-3

Keywords

Navigation