PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | nr 4 | 119-123
Tytuł artykułu

Trends and Differences of Applying Intelligence to an Agent

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The agent technology has recently become one of the most vibrant and fastest growing areas in information technology. On of the most promising characteristics of agent is its intelligence. Intelligent agent is the agent that percepts its environment, collects all information about its environment that it needs, processes these information and then generate proper actions according to these information. This paper discusses trends and differences between two main types of intelligence that can be applied to agent: accumulative intelligence and dynamic intelligence. Accumulative intelligence is discussed with its two perspectives: moment perspective and historical perspective. Auto-vehicle driver is also discussed as an application example of accumulative intelligence. Also, MOSAIC, Mimesis, and MINDY models are reviewed as the pioneering works of dynamic intelligence.
Wydawca

Rocznik
Tom
Strony
119-123
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
autor
  • Faculty of Electronic Engineering, Computer Science & Engineering Department, Menoufia University, Menouf, 32952, Egypt, a_elmhalaway@Hotmail.com
Bibliografia
  • [1] M. Wooldridge, An Introduction to MultiAgent System. Chichester, England: Wiely, 2002.
  • [2] M. Haruno, D. M. Wolpert, and M. Kawato, “MOSAIC model for sensorimotor learning and control”, Int. J. Neural Computation, vol. 13, no. 10, pp. 2201–2220, 2001.
  • [3] R. Pefeifer and C. Scheier, Understanding Intelligence. Cambridge, MA: MIT Press, 1999.
  • [4] E. S. Reed, From Soul to Mind: the Emergence of Psychology from Erasums Darwin to William James. Yale University Press, 1998.
  • [5] S. J. Russell and P. Norvig, Artificial Intelligence: a Modern approach. Prentice Hall, 2003.
  • [6] Y. Kuniyoshi, Y. Ohmura, K. Terada, A. Nagakubo, S. Eitoku, and T. Yamamoto, “Embodied basis of invariant features in execution and perception of whole-body dynamic actions – knacks and focuses of roll-and-rise motion”, Int. J. Robotics and Autonomous Sys., vol. 48, no. 4, pp. 198–201, 2004.
  • [7] S. Weagraff, “The case for intelligent agents: preparing for the future of care”, in Proc. Int. Conf. CIMCA-IAWTIC’05, Vienna, Austria, 2005, pp. 976–980.
  • [8] S. Gao and D. Xu, “Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering”, Int. J. Expert Sys. with Appl., vol. 39, no. 3, pp. 1493–1504, 2009.
  • [9] R. Benenson, S. Petti, T. Fraichard, and M. Parent, “Towards urban driverless vehicles”, Int. J. Vehicle Autonomous Sys., vol. 6, no. 1–2, pp. 4–23, 2008.
  • [10] S. Kolski, D. Ferguson, M. Bellino, and R. Seigwart, “Autonomous driving in structured and unstructured environments”, in Proc. IEEE Intelligent Vehicles Symp., Tokyo, Japan, 2006, pp. 558–563.
  • [11] E. Bertolazzi, F. Biral, P. Bosetti, M. De Cecco, R. Oboe and F. Zendri. “Development of a reduced size unmanned car”, in Proc. 10th IEEE Int. Worksh. Adv. Motion Control AMC 2008, Trento, Italy, 2008, vol. 26–28, pp. 763–770.
  • [12] A. P. Borges, R. Ribeiro, B. C. Avila, F. Enembreck, and E. E. Scalabrin, “A learning agent to help drive vehicles”, in Proc. 13th Int. Conf. Comput. Supported Coop. Work in Design, Santiago, Chile, 2009, pp. 1–11.
  • [13] C. Yang, and S. Letourneau. “Learning to predict train wheel failures”, in Proc. 11th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Chicago, IL, USA, 2005, pp. 1–11.
  • [14] G. Rzevski and P. Skobelev, “Emergent intelligence in large scale multi-agent systems”, Int. J. Education and Inform. Technol., vol. 1, no. 2, pp. 64–71, 2007.
  • [15] G. Rizzolattie, L. Fadiga, V. Gallese, and L. Fogassi, “Premotor cortex and the recognition of motor actions”, Int. J. Cognitive Brain Research, vol. 3, no. 2, pp. 131–141, 1996.
  • [16] M. Fujita, “Intelligence dynamics: a concept and preliminary experiments for open ended learning agents”, J. Autonomous Agents and Multi-Agent Sys., vol. 19, no.3, pp. 248–271, 2009.
  • [17] S. Vijayakumar and S. Schaal, “LWPR: an O (n) algorithm for incremental real time learning in high dimensional space”, in Proc. 17th Int. Conf. Machine Learning ICML 2000, Stanford, CA, USA, 2000, pp. 1079–1086.
  • [18] K. Doya, K. Samejima, K. Katagiri, and M. Kawato, “Multiple model-based reinforcement learning”, Int. J. Neural Computation, vol. 14, no. 6, pp. 1347–1369, 2002.
  • [19] D. C. Bentivegna, C. G. Atkeson, and G. Cheng, “Learning tasks from observation and practice”, Int. J. Robotics and Autonomous Sys., vol. 47, no. 2–3, pp. 163–169, 2004.
  • [20] K. Sabe, K. Hidai, K. Kawamoto, and H. Suzuki, “A proposal for intelligence model, MINDY for open ended learning system”, in Proc. Int. Worksh. Intelligence Dynamics at IEEE/RSJ Humanoids, Genoa, Italy, 2006.
  • [21] J. Ma, J. Theiler, and S. Perkins, “Accurate on-line support vector regression”, Int. J. Neural Comput., vol. 15, no. 11, pp. 2683–2703, 2003.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BATA-0015-0027
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.