On the emergence of intelligent global behaviours from simple local actions

International Journal of Systems Science

Volumn 31, Issue 7, 2000, Pages 861-872

  Benjamin, D Paul ^a  

^a PACE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRA; ARTIFICIAL INTELLIGENCE; BEHAVIORAL RESEARCH; NONLINEAR SYSTEMS; ROBOTICS;

GLOBAL BEHAVIORS;

SYSTEMS SCIENCE;

EID: 0034236827     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/002077200406589     Document Type: Article

References (22)

1. Agibalov, G. P., and Evtushenko, N. V., 1984, Algebraic characterization of permutation automata decomposable into a cascade connection of smaller components. Kibernetika, 20, 9-15 (Engl. Trans., 1984, Cybernetics (USA), 20, 12-22).
2. Arbib, M. A., and Manes, E. G., 1974, Foundations of system theory: decomposable systems. Automatica, 10, 285-302.
3. Benjamin, D. P., 1992a, Reformulating path planning problems by task-preserving abstraction. Journal of Robotics and Autonomous Systems, 9, 1-9; 1992b, Towards an effective theory of reformulation. Proceedings of the W orkshop on Change of Representation and Problem Reformulation, edited by M. R. Lowry, Technical Report FIA-92-06, Ames Research Center, National Aeronautics and Space Administration, pp. 13-27; 1994, Formulating patterns in problem solving. Annals of Mathematics and AI, 10, 1-23.
4. Benjamin, D. P., Cameron, a., Dorst, L., Rosar, M., and Wu, H., 1991, Integrating perception with problem solving. Proceedings of the AAAI Spring Symposium on Integrated Intelligent Architectures, Stanford University, California, March 1991, pp. 41-45.
5. Evtushenko, N. V., 1979, On the realization of automata by cascade connection of standard automata. Automatic Control and Computer Science, 13, 50-53.
6. Halatsis, C., Sigala, M., and Philokyprou, G., 1978, Polylinear decomposition of synchronous sequential machines. IEEE Transactions on Computers, 27, 1144-1152.
7. Hou, Y., 1987, Trinity algebra and its application to machine decompositions. Information Processing Letters, 26, 127-134.
8. Howie, J. M., 1976, An Introduction to Semigroup Theory (New York: Academic Press).
9. Ito, M., 1981, Representation of a connected automaton by a Cartesian composition. Foundations of Control Engineering, 6,87-94.
10. Korf, R. E., 1987, Planning as search: a quantitative approach. Artificial Intelligence, 33, 65-88.
11. Lallement, G., 1979, Semigroups and Combinatorial Applications (New York: Wiley).
12. Manna, Z., and Waldinger, R., 1987, How to clear a block: a theory of plans. Journal of Automated Reasoning, 3, 343-377.
13. McCarthy, j., 1968. Programs with common sense. Semantic Information Processing, edited by M. Minsky (Cambridge, Massachusetts: MIT Press), pp. 403-417.
14. Moore, C., 1998, Predicting non-linear cellular automata quickly by decomposing them into linear ones. Physica D, 111, 27-41.
15. Nelson, E., 1967, Tensor Analysis (Princeton University Press).
16. Perceval, I., and Richards, D., 1982, Introduction to Dynamics (Cambridge University Press).
17. Petrich, M., 1984, Inverse Semigoups (New York: Wiley).
18. Pylyshyn, Z. W., 1987, The Robot’s Dilemma, The Frame Problem in Artifi cial Intelligence (Norwood, NJ: Ablex).
19. Simon, H. A., 1969, The Sciences of the Artificial (Cambridge, Massachusetts: MIT Press).
20. Stoker, J. J., 1969, Differential Geometry (New York: Wiley-Interscience).
21. Topolskiy, N. G., 1979, Description and machine decompositional synthesis of digital automata with a large number of states. Tekh. Kibernetitic, 17, 136-147 Engl. Trans., 1979, Engng. Cybernetics (USA), 17, 95-105.
22. Zimmer, R. M., 1990. Representation engineering and category theory. Change of Representation and Inductive Bias, edited by D. Benjamin (Boston, Massachusetts: Kluwer), pp. 169-182.