Schema-based design and the AKIRA schema language: An overview

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

Volumn 4520 LNAI, Issue , 2007, Pages 128-152

  Pezzulo, Giovanni ^a   Calvi, Gianguglielmo ^b  

^a INSTITUTE OF COGNITIVE SCIENCES AND TECHNOLOGIES   (Italy)

^b Noze srl   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMOUS AGENTS; COMPUTER SIMULATION; DATA STRUCTURES; KNOWLEDGE ENGINEERING; QUERY PROCESSING;

AKIRA SCHEMA LANGUAGE (AKSL); AUTONOMOUS AGENT ARCHITECTURES;

QUERY LANGUAGES;

EID: 38149142319     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74262-3_8     Document Type: Conference Paper

References (68)

1. akira, 2003. http://www.akira-project.org/
2. Arbib, M.: Levels of modelling of mechanisms of visually guided behavior. Behavioral and Brain Science 10, 407-465 (1987)
3. Arbib, M.: Schema theory. In: Shapiro, S. (ed.) Encyclopedia of Artificial Intelligence, 2nd edn. vol. 2, pp. 1427-1443. Wiley, Chichester, UK (1992)
4. Arbib, M.A.: The metaphorical brain 2: Neural networks and beyond. Wiley, New York (1989)
5. Arkin, R., Ali, K., Weitzenfeld, A., Cervantes-Prez, F.: Behavioral models of the praying mantis as a basis for robotic behavior. Robotics and Autonomous Systems 32(1), 39-60 (2000)
6. Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge (1998)
7. Barsalou, L.W.: Perceptual symbol systems. Behavioral and Brain Sciences 22, 577-600 (1999)
8. Bartlett, F.C.: Remembering. Cambridge University Press, Cambridge (1932)
9. Bates, E.: The Emergence of Symbols. Academic Press, London (1979)
10. Beer, R.D.: Intelligence as Adaptive Behavior: An Eperiment in Computational Neuroethology. Academic Press, San Diego (1990)
11. Bickhard, M.H.: Levels of representationality. Journal of Experimental and Theoretical Artificial Intelligence 10(2), 179-215 (1998)
12. Brooks, R.A.: Intelligence without representation. Artificial Intelligence 47(47), 139-159 (1991)
13. Butz, M.V.: Anticipatory learning classifier systems. Kluwer Academic Publishers, Boston, MA (2002)
14. Crabbe, F.L.: Optimal and non-optimal compromise strategies in action selection. In: Proceedings of the Eighth International Conference on Simulation of Adaptive Behavior (2004)
15. Damasio, A.R.: Descartes' Error: Emotion, Reason and the Human Brain. Grosset/Putnam, New York, 1994. trad. it. di Cartesio, L., Adelphi, Milano (1999)
16. Davidsson, P.: A framework for preventive state anticipation. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 151-166. Springer, Heidelberg (2003)
17. Demiris, Y.: Prediction of intent in robotics and multi-agent systems. to appear in Cognitive Processing (2007)
18. Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition (hammer). Robotics and Autonomous Systems Journa 54, 361-369 (2005)
19. Desmurget, M., Grafton, S.: Forward modeling allows feedback control for fast reaching movements. Trends Cogn. Sci. 4, 423-431 (2000)
20. Doya, K.: Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr. Opin. Neurobiol 10(6), 732-739 (December 2000)
21. Drescher, G.L.: Made-Up Minds: A Constructivist Approach to Artificial Intelligence. MIT Press, Cambridge, MA (1991)
22. Fleischer, J.G.: Neural correlates of anticipation in cerebellum, basal ganglia, and hippocampus. In: this volume
23. Frith, C.D., Blakemore, S.J., Wolpert, D.M.: Abnormalities in the awareness and control of action. Philos Trans R Soc. Lond. B Biol. Sci. 355(1404), 1771-1788 (December 2000)
24. Gallese, V., Metzinger, T.: Motor ontology: The representational reality of goals, actions, and selves. Philosophical Psychology 13(3), 365-388 (2003)
25. Gross, H.-M., Volker, S., Torsten, S.: A neural architecture for sensorimotor anticipation. Neural Networks 12, 1101-1129 (1999)
26. Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behav Brain Sci. 27(3), 377-396 (June 2004)
27. Haruno, M., Wolpert, D., Kawato, M.: Hierarchical mosaic for movement generation. In: Ono, T., Matsumoto, G., Llinas, R., Berthoz, A., Norgren, H., Tamura, R. (eds.) Excepta Medica International Congress Series, Elsevier Science, Amsterdam (2003)
28. Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6, 242-247 (2002)
29. Hoffmann, H.: Perception through visuomotor anticipation in a mobile robot. Neural Networks 20, 22-33 (2007)
30. Hoffmann, J.: Anticipatory behavioral control. In: Butz, M.V., Sigaud, O., Gerard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems, pp. 44-65. Springer-Verlag, Berlin Heidelberg (2003)
31. Holland, J., Holyoak, K., Nisbett, R., Thagard, P.: Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, Massachussets (1986)
32. ikaros,(2002) http://ww.lucs.lu.se/IKAROS.
33. irrlicht, (2003) http://irrlicht.sourceforge.net/.
34. James, W.: The Principles of Psychology. Dover Publications, New York (1890)
35. Johansson, B., Balkenius, C.: An experimental study of anticipation in robot navigation. In: this volume
36. Kalman, R.E.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82(1), 35-45 (1960)
37. Lyons, D.M., Arbib, M.A.: A formal model of computation for sensory-based robotics. IEEE Journal of Robotics and Automation 5(3), 280-293 (1989)
38. Maes, P.: Situated agents can have goals. In: Maes, P. (ed.) Designing Autonomous Agents, pp. 49-70. MIT Press, Cambridge (1990)
39. Mccauley, L.: Neural schemas: A mechanism for autonomous action selection and dynamic motivation. In: the 3rd WSES Neural Networks and Applications Conference (2002)
40. Miall, R.C., Wolpert, D.M.: Forward models for physiological motor control. Neural Networks 9(8), 1265-1279 (1996)
41. Middleton, F.A., Strick, P.L.: Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies. Brain Cogn. 42(2), 183-200 (2000)
42. Miller, G.A., Galanter, E., Pribram, K.H.: Plans and the Structure of Behavior. Holt, Rinehart and Winston, New York (1960)
43. Minsky, M.: The Society of Mind. Simon & Schuster (1988)
44. Neisser, U.: Cognition and reality. Freeman, San Francisco, CA (1976)
45. Newell, A., Simon, H.A.: Human problem solving. Prentice-Hall, Englewood Cliffs, NJ (1972)
46. Norman, D.A., Shallice, T.: Attention to action: Willed and automatic control of behaviour. In: Davidson, R.J., Schwartz, G.E., Shapiro, D. (eds.) Consciousness and Self-Regulation: Advances in Research and Theory, Plenum Press, New York (1986)
47. Pezzulo, G., Calvi, G.: A schema based model of the praying mantis. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 211-223. Springer Verlag, Heidelberg (2006)
48. Pezzulo, G., Calvi, G.: 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 (2006)
49. Pezzulo, G., Calvi, G.: Designing modular architectures in the framework akira. To appear in Multiagent and Grid Systems, 3(1) (2007)
50. Pezzulo, G., Calvi, G., Castelfranchi, C.: Dipra: Distributed practical reasoning architecture. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, pp. 1458-1464 (2007)
51. Pezzulo, G., Calvi, G., Ognibene, D., Lalia, D.: 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, Washington, DC, USA. Vol-2, pp. 146-152. IEEE Computer Society, Los Alamitos, CA, USA (2005)
52. Pezzulo, G., Castelfranchi, C.: The symbol detachment problem. Cognitive Processing (to appear, 2007)
53. Piaget, J.: The Construction of Reality in the Child. Ballentine (1954)
54. Rao, R.P., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci 2(1), 79-87 (January 1999)
55. Roy, D.: Semiotic schemas: a framework for grounding language in action and perception. Artificial Intelligence 167(1-2), 170-205 (2005)
56. Saffiotti, A.: Handling uncertainty in control of autonomous robots. In: Artificial Intelligence Today, pp. 381-407 (1999)
57. Schank, R.C., Abelson, R.P.: Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures. Erlbaum, L., Hillsdale, NJ (1977)
58. Schultz, W.: Predictive reward signal of dopamine neurons. Journal of Neurophysiology 80, 1-27 (1998)
59. Selfridge, O.: The Mechanisation of Thought Processes, volume 10, chapter Pandemonium: A paradigm for learning, pp. 511-529. National Physical Laboratory Symposia. Her Majesty's Stationary Office, London (1959)
60. Shapiro, D., Schmidt, R.: The schema theory: Recent evidence & developmental implications. In: Clark, J.K.J. (ed.) The development of movement control and co-ordination, Wiley, New York (1982)
61. Smith, O.J.M.: A controller to overcome dead time. ISA Journal 6(2), 28-33 (1959)
62. Tani, J.: Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Netw. 16(1), 11-23 (January 2003)
63. Tani, J., Ito, M., Sugita, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using rnnpb. Neural Netw. 17(8-9), 1273-1289 (2004)
64. Tani, J., Nolfi, S.: Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Netw. 12(7-8), 1131-1141 (October 1999)
65. Weitzenfeld, A., Peguero, O., Gutiérrez, S.: NSL/ASL: Distributed simulation of modular neural networks. In: MICAI, pp. 326-337 (2000)
66. Wolpert, D.M., Gharamani, Z., Jordan, M.: An internal model for sensorimotor integration. Science 269, 1179-1182 (1995)
67. Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317-1329 (1998)
68. Ziemke, T., Jirenhed, D.-A., Hesslow, G.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85-104 (2005)