Some requirements for human-like robots: Why the recent over-emphasis on embodiment has held up progress

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5436, Issue , 2009, Pages 248-277

  Sloman, Aaron ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DESIGN IDEAS; FORMS OF REPRESENTATION; HUMAN INTELLIGENCE; PHYSICAL ROBOTS; REQUIREMENTS FOR ARCHITECTURES;

DYNAMICAL SYSTEMS; INTELLIGENT SYSTEMS; ONTOLOGY; ROBOTS;

MACHINE DESIGN;

EID: 67049095599     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-00616-6_12     Document Type: Conference Paper

References (76)

1. Ambler, A.P., Barrow, H.G., Brown, C.M., Burstall, R.M., Popplestone, R.J.: A Versatile Computer-Controlled Assembly System. In: Proc. Third Int. Joint Conf. on AI, Stanford, California, pp. 298-307 (1973)
2. Berthoz, A.: The Brain's sense of movement. Perspectives in Cognitive Science. Harvard University Press, London (2000)
3. Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. The MIT Press, Cambridge (1984)
4. Brooks, R.A.: Elephants Don't Play Chess. Robotics and Autonomous Systems 6, 3-15 (1990), http://people.csail.mit.edu/brooks/papers/elephants.pdf
5. Chappell, J., Sloman, A.: Natural and artificial meta-configured altricial information-processing systems. International Journal of Unconventional Computing 3(3), 211-239 (2007), http://www.cs.bham.ac.Uk/ research/projects/cosy/papers/#tr0609
6. Cliff, D.: Biologically-Inspired Computing Approaches to Cognitive Systems: a partial tour of the literature. Technical Report HPL-2003-11, Hewlett-Packard Labs, Bristol, UK (2003), http://www.hpl.hp.com/techreports/ 2003/HPL-2003-11.html
7. Cutkosky, M.R., Jourdain, J.M., Wright, P.K.: Testing and Control of a Compliant Wrist. Technical Report CMU-RI-TR-84-04, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (March 1984), http://www.ri.cmu.edu/pubs/pub- 73.html
8. Gibson, E.J., Pick, A.D.: An Ecological Approach to Perceptual Learning and Development. Oxford University Press, New York (2000)
9. Gibson, J.J.: The Ecological Approach to Visual Perception. Houghton Mifflin, Boston (1979)
10. Grush, R.: The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences 27, 377-442 (2004)
11. Harnad, S.: The Symbol Grounding Problem. Physica D 42, 335-346 (1990)
12. Jablonka, E., Lamb, M.J.: Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. MIT Press, Cambridge (2005)
13. Kant, I.: Critique of Pure Reason. Macmillan, Basingstoke (1871); Translated by Norman Kemp Smith (1929)
14. King, R.D., Clark, D.A., Shirazi, J., Sternberg, M.J.: Inductive logic programming used to discover topological constraints in protein structures. In: Proc. International Conference on Intelligent Systems for Molecular Biology, pp. 219-226 (1994)
15. Kirsch, D.: Today the earwig, tomorrow man? Artificial Intellintelligence 47(1), 161-184 (1991), http://adrenaline.ucsd.edu/kirsh/articles/earwig/earwig- cleaned.html
16. Lakatos, I.: Proofs and Refutations. Cambridge University Press, Cambridge (1976)
17. Lakatos, I.: The methodology of scientific research programmes. In: Worrall, J., Currie, G. (eds.) Philosophical papers, vol. I. Cambridge University Press, Cambridge (1980)
18. Lungarella, M., Sporns, O.: Mapping information flow in sensorimotor networks. PLoS Computational Biolology 2(10:e144) (2006) 10.1371/journal.pcbi. 0020144
19. McCarthy, J.: The Well Designed Child (1996), http://www-formal.stanford. edu/jmc/childi.html
20. Minsky, M.L.: The Society of Mind. William Heinemann Ltd., London (1987)
21. Minsky, M.L.: The Emotion Machine. Pantheon, New York (2006)
22. Neisser, U.: Cognition and Reality. W. H. Freeman., San Francisco (1976)
23. Nilsson, N.J.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann, San Francisco (1998)
24. Pfeifer, R., Iida, F., Gomez, G.: Designing intelligent robots-on the implications of embodiment. Journal of Robotics Society of Japan 24(07), 9-16 (2006), http://www.robotcub.org/misc/review3/07-Pfeifer-Iida-Gomez-RSJ.pdf
25. Philipona, D.L., O'Regan, J.K.: Color naming, unique hues, and hue cancellation predicted from singularities in reflection properties. Visual Neuroscience 23(3-4), 331-339 (2006)
26. Poincare, H.: Science and hypothesis. W. Scott, London (1905), http://www.archive.org/details/scienceandhypoth00poinuoft
27. Rips, L.J., Bloomfield, A., Asmuth, J.: From Numerical Concepts to Concepts of Number. The Behavioral and Brain Sciences (in press)
28. Sauvy, J., Suavy, S.: The Child's Discovery of Space: From hopscotch to mazes - an introduction to intuitive topology. Penguin Education, Harmondsworth (1974); Translated from the French by Pam Wells
29. Shanahan, M.P.: A cognitive architecture that combines internal simulation with a global workspace. Consciousness and Cognition 15, 157-176 (2006)
30. Sloman, A.: 'Necessary', 'A Priori' and 'Analytic'. Analysis 26(1), 12-16 (1965), http://www.cs.bham.ac.uk/research/projects/cogaff/07.html#701
31. Sloman, A.: The Computer Revolution in Philosophy. Harvester Press (and Humanities Press), Hassocks (1978), http://www.cs.bham.ac.uk/research/cogaff/crp
32. Sloman, A.: Image interpretation: The way ahead?. In: Braddick, O.J., Sleigh, A.C. (eds.) Physical and Biological Processing of Images (Proceedings of an international symposium organised by The Rank Prize Funds, London, 1982), pp. 380-401. Springer, Berlin (1982), http://www.cs.bham.ac.uk/research/ projects/cogaff/06.html#0604
33. Sloman, A.: What enables a machine to understand? In: Proc. 9th IJCAI, Los Angeles, pp. 995-1001 (1985)
34. Sloman, A.: On designing a visual system (towards a gibsonian computational model of vision). Journal of Experimental and Theoretical AI 1(4), 289-337 (1989), http://www.cs.bham.ac.uk/research/projects/cogaff/81-95.html#7
35. Sloman, A.: Actual possibilities. In: Aiello, L.C., Shapiro, S.C. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the Fifth International Conference (KR 1996), Boston, MA, pp. 627-638. Morgan Kaufmann, San Francisco (1996)
36. Sloman, A.: The Cognition and Affect Project: Architectures, Architecture- Schemas, And The New Science of Mind. Technical report, School of Computer Science, University of Birmingham (2003) (revised, August 2008), http://www.cs.bham.ac.uk/research/projects/cogaff/03.html#200307
37. Sloman, A.: Cross-Disciplinary Reflections: Philosophical Robotics. Research Note: Draft chapter for a book on the CoSy project COSY-TR-0806, School of Computer Science, University of Birmingham (2008), http://www.cs.bham.ac.uk/ research/projects/cosy/papers/#tr0806
38. Sloman, A.: Putting the Pieces Together Again. In: Sun, R. (ed.) Cambridge Handbook on Computational Psychology, ch. 26, pp. 684-709. Cambridge University Press, New York (2008), http://www.cs.bham.ac.uk/research/projects/ cogaff/07.html#710
39. Sloman, A., Chappell, J.: The Altricial-Precocial Spectrum for Robots. In: Proceedings IJCAI 2005, Edinburgh, pp. 1187-1192. IJCAI (2005), http://www.cs.bham.ac.uk/research/cogaff/05.html#200502
40. Sloman, A.: Requirements for a Fully Deliberative Architecture (Or component of an architecture). Research Note COSY-DP-0604, School of Computer Science, University of Birmingham, Birmingham, UK (May 2006), http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0604
41. Sloman, A.: Diversity of Developmental Trajectories in Natural and Artificial Intelligence. In: Morrison, C.T., Oates, T.T. (eds.) Computational Approaches to Representation Change during Learning and Development. AAAI Fall Symposium 2007, Technical Report FS-07-03, pp. 70-79. AAAI Press, Menlo Park (2007), http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0704
42. Sloman, A.: Architectural and representational requirements for seeing processes and affordances. In: Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour. Psychology Press, London (2008), http://www.cs.bham.ac.uk/research/projects/cosy/ papers#tr0801
43. Sloman, A.: Architectural and representational requirements for seeing processes, proto-affordances and affordances. Research paper COSY-TR-0801a, School of Computer Science, University of Birmingham, UK, Also presented at Dagstuhl workshop on Logic and Probability for Scene Interpretation (March 2008), http://www.cs.bham.ac.uk/research/projects/cosy/papers#tr0801a
44. Sloman, A.: Kantian Philosophy of Mathematics and Young Robots. In: Autexier, S., Campbell, J., Rubio, J., Sorge, V., Suzuki, M., Wiedijk, F. (eds.) AISC 2008, Calculemus 2008, and MKM 2008. LNCS, vol. 5144, pp. 558-573. Springer, Heidelberg (2008), http://www.cs.bham.ac.uk/research/projects/cosy/ papers#tr0802
45. Sloman, A.: Varieties of Meta-cognition in Natural and Artificial Systems. In: Cox, M.T., Raja, A. (eds.) Workshop on Metareasoning, AAAI 2008 Conference, pp. 12-20. AAAI Press, Menlo Park (2008), http://www.cs.bham.ac.uk/ research/projects/cosy/papers/#tr0803
46. Sloman, A., Chappell, J.: Computational Cognitive Epigenetics (Commentary on [12]). Behavioral and Brain Sciences 30(4), 375-376 (2007), http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0703
47. Sloman, A., Cosy-partners.: CoSy deliverable DR.2.1 Requirements study for representations. Technical Report COSY-TR-0507, The University of Birmingham, UK (2005), http://www.cs.bham.ac.uk/research/projects/cosy/papers/ #tr0507
48. Strawson, P.F.: Individuals: An essay in descriptive metaphysics. Methuen, London (1959)
49. Sussman, G.J.: A computational model of skill acquisition. American Elsevier, Amsterdam (1975)
50. Tarsitano, M.: Route selection by a jumping spider (Portia labiata) during the locomotory phase of a detour. Animal Behaviour 72(6), 1437-1442 (2006), http://dx.doi.org/10.1016/].anbehav.2006.05.007
51. Trehub, A.: The Cognitive Brain. MIT Press, Cambridge (1991), http://www.people.umass.edu/trehub/
52. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433-460 (1950); reprinted in Feigenbaum, E.A., Feldman, J. (eds.): Computers and Thought, pp. 11-35. McGraw-Hill, New York (1963)
53. Warneken, F., Tomasello, M.: Altruistic helping in human infants and young chimpanzees. Science, 1301-1303, March 3 (2006) doi:10.1126/science. 1121448
54. Ziemke, T.: Situated and Embodied Cognition. Cognitive Systems Research 3(3) (2002) (Editor's introduction to special issue)
55. 278-302 Co-evolution of Rewards and Meta-parameters in Embodied Evolution Stefan Elfwing Eiji Uchibe Kenji Doya Neural Computation Unit, Okinawa Institute of Science and Technology 12-22 Suzaki, Uruma, Okinawa 904-2234, Japan {elfwing,uchibe,doya}@oist.jp Keywords: Embodied Evolution, Evolutionary Robotics, Reinforcement Learning, Meta-learning, Shaping Rewards, Meta-parameters. Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous, and autonomous properties of biological evolution. The evaluation, selection, and reproduction are carried out by cooperation and competition of the robots, without any need for human intervention. An embodied evolution framework is therefore well suited to study the adaptive learning mechanisms for artificial agents that share the same fundamental constraints as biological agents: self-preservation and self-reproduction. In this paper we propose a framework for performing embodied evolution with a limited number of robots, by utilizing time-sharing in subpopulations of virtual agents. Within this framework, we explore the combination of within-generation learning of basic survival behaviors by reinforcement learning, and evolutionary adaptations over the generations of the basic behavior selection policy, the reward functions, and meta-parameters for reinforcement learning. We apply a biologically inspired selection scheme, in which there is no explicit communication of the individuals' fitness information. The individuals can only reproduce offspring by mating, a pair-wise exchange of genotypes, and the probability that an individual reproduces offspring in its own subpopulation is dependent on the individual's "health", i.e., energy level, at the mating occasion. We validate the proposed method by comparing the proposed method with evolution using standard centralized selection, in simulation, and by transferring the obtained solutions to hardware using two real robots. © Springer-Verlag Berlin Heidelberg 2009
56. Baldwin, J.: A New Factor in Evolution. American Naturalist 30, 441-451 (1896)
57. Doya, K.: Metalearning and Neuromodulation. Neural Networks 15, 4 (2002)
58. Doya, K., Uchibe, E.: The Cyber Rodent Project: Exploration of Adaptive Mechanisms for Self-Preservation and Self-Reproduction. Adaptive Behavior 13(2), 149160 (2005)
59. Elfwing, S., Uchibe, E., Doya, K., Christensen, H.I.: Darwinian Embodied Evolution of the Learning Ability for Survival. Adaptive Behavior (in Press)
60. Floreano, D., Mondada, F.: Evolution of Plastic Neurocontrollers for Situated Agents. In: International Conference on Simulation of Adaptive Behavior, pp. 401410 (1996)
61. Hinton, G., Nowlan, S.: How Learning can Guide Evolution. Complex Systems 1, 495-502 (1987)
62. Laud, A., DeJong, G.: The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping. In: International Conference on Machine Learning, pp. 440-447 (2003)
63. Nehmzow, U.: Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm. In: International Workshop on Epigenetic Robotics and Robotics (2002)
64. Niv, Y., Joel, D., Meilijson, I., Ruppin, E.: Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors. Adaptive Behavior 10(1), 5-24 (2002)
65. Ng, A., Harada, D., Russell, S.: Policy Invariance under Reward Transformations: Theory and Application to Reward Shaping. In: International Conference on Machine Learning, pp. 278-287 (1999)
66. Nolfi, S., Floreano, D.: Evolutionary Robotics. MIT Press, Cambridge (2000)
67. Rummery, G.A., Niranjan, M.: On-line Q-learning using Connectionist Systems. Technical report CUED/F-INFENG/TR 166, Cambridge University Engineering Department (1994)
68. Ruppin, E.: Evolutionary Autonomous Agents: A Neuroscience Perspective. Nature Review Neuroscience 3, 132-141 (2002)
69. Paenke, I., Sendhoff, B., Kawecki, T.: Influence of Plasticity and Learning on Evolution under Directional Selection. American Naturalist 170(2), 1-12 (2007)
70. Paenke, I., Kawecki, T., Sendhoff, B.: The Influence of Learning on Evolution - A Mathematical Framework. Artificial Life (2008)
71. Singh, S.P., Sutton, R.S.: Reinforcement Learning with Replacing Eligibility Traces. Machine Learning 22(1-3), 123-158 (1996)
72. Sutton, R. S.: Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. In: Advances in Neural Information Processing Systems 8, pp. 1038-1044 (1996)
73. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
74. Turney, P., Whitley, D., Anderson, R.: Introduction to the Special Issue: Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect. Evolutionary Computation 4(3), iv-viii (1996)
75. Urzelai, J., Floreano, D.: Evolutionary Robotics: Coping with Environmental Change. In: Genetic and Evolutionary Computation Conference, pp. 941-948 (2000)
76. Watson, R., Ficici, S., Pollack, J.: Embodied Evolution: Distributing an Evolutionary Algorithm in a Population ofRobots. Robotics and Autonomous Systems 39(1), 1-18 (2002)