Topics

Background

In this special session we propose to integrate methodologies including 1) the formalisation of the necessary properties for the definition of life, 2) the implementation of artificial agents, and 3) the study of the relation between life and cognition and focus on the study of the relationship between biological, artificial and cognitive systems. To do so we propose three complementary perspectives:

  • A theoretical approach. The study of biological and artificial systems should be based on a unified set of techniques and formulations [1]. Of particular interest are frameworks inspired for instance by cybernetics [2], dynamical systems theory [3], stochastic optimal control [4] and Bayesian inference [5], implemented for example in systems biology [6] or cognitive science/computational neuroscience as the Bayesian brain hypothesis [7,8]. Applications to real-world problems, including features like noise and delays, are very relevant to our proposal. Formulations that include cognition are also welcomed, since studies of the mind could play a crucial role in the identification of fundamental properties of both living and nonliving architectures [9].
  • A combinational approach. On this view, biological and artificial systems are combined into hybrid structures and investigated as a unique extended entity. The enhancement of biological organisms through the combination of these with artificial components is of extreme interest to several fields. In biology, scientists are exploring living systems and their limitations through hybridisation techniques, implementing for instance cyborg insects [10]. In clinical and behavioural sciences, it has been suggested that providing human beings with artificial components may help in tracking their behaviour in order to improve therapies [11]; robotic equipment is also already vastly used in the treatment of motor control dysfunctions [12]. On the other hand, engineering applications can also potentially benefit from the combination of organic and inorganic elements as in the example of computing machines augmented with reservoirs of real neurons [13].
  • An interactionist approach. The study of coupled living-artificial systems is emerging as a new paradigm for the definition and investigation of cognitive functions [14-16], e.g. Humanoid Robotics [16]. This is mostly due to recent technological developments (e.g. Virtual/Augmented Reality [17]) allowing for more targeted investigations of the mind. Here we focus on the study of properties emerging from the dynamic interactions of coupled agents. In particular, we are interested in the coupling of artificial/non-artificial systems in order to identify and understand what defines cognition and agency, e.g. when does the interaction feel like one with a living (or cognitive) organism? When can we attribute agency to a “partner” interacting with us? [18]


References

  1. Tani, Jun. Exploring robotic minds: actions, symbols, and consciousness as self-organizing dynamic phenomena. Oxford University Press, 2016.
  2. Wiener, Norbert. "Cybernetics." Scientific American 179.5 (1948): 14-19.
  3. Beer, Randall D. "Dynamical approaches to cognitive science." Trends in cognitive sciences 4.3 (2000): 91-99.
  4. Todorov, Emanuel. "Efficient computation of optimal actions." Proceedings of the national academy of sciences 106.28 (2009): 11478-11483.
  5. Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
  6. Iglesias, Pablo A. "Systems biology: The role of engineering in the reverse engineering of biological signaling." Cells 2.2 (2013): 393-413.
  7. Friston, Karl. "The free-energy principle: a unified brain theory?." Nature Reviews Neuroscience 11.2 (2010): 127-138.
  8. Baltieri, Manuel, and Buckley, Christopher L. “An active inference implementation of phototaxis.” Proceedings of the European Conference on Artificial Life (2017): 36-43.
  9. Kirchhoff, Michael D., and Tom Froese. "Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis." Entropy 19.4 (2017): 169.
  10. Sato, Hirotaka, et al. "Deciphering the role of a coleopteran steering muscle via free flight stimulation." Current Biology 25.6 (2015): 798-803.
  11. The parasitic humanoid (see references at the bottom http://www-hiel.ist.osaka-u.ac.jp/~t_maeda/parasite/index.html)
  12. Hochberg, Leigh R., et al. "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm." Nature 485.7398 (2012): 372-375.
  13. Takahashi, Hirokazu, et al. "Reservoir computing with dissociated neuronal culture." Frontiers in Neuroscience 27 (2016).
  14. Di Paolo, Ezequiel A., Marieke Rohde, and Hiroyuki Iizuka. "Sensitivity to social contingency or stability of interaction? Modelling the dynamics of perceptual crossing." New ideas in psychology 26.2 (2008): 278-294.
  15. Dumas, Guillaume, et al. "The human dynamic clamp as a paradigm for social interaction." Proceedings of the National Academy of Sciences 111.35 (2014): E3726-E3734.
  16. Kanda, Takayuki, and Hiroshi Ishiguro. Human-robot interaction in social robotics. CRC Press, 2012.
  17. Suzuki, Keisuke, Sohei Wakisaka, and Naotaka Fujii. "Substitutional reality system: a novel experimental platform for experiencing alternative reality." Scientific reports 2 (2012): srep00459.
  18. Turing, Alan M. "Computing machinery and intelligence." Mind 59.236 (1950): 433-460.