Abstract
Neurosolver is a neuromorphic planner and a problem solving system. It was tested on several problem solving and planning tasks such as re-arranging blocks and controlling a software-simulated artificial rat running in a maze. In these tasks, the Neurosolver created models of the problem as temporal patterns in the problem space. These behavioral traces were then used to perform searches and generate actions. In this paper, we present an analysis of the capabilities of the Neurosolver to predict data points in time series. We report on testing those capabilities on the sample data sets that were made available for the neural network forecasting competition NN5 [14]. We conclude with a brief description of several ideas that we are currently applying to the data sets posted for the 2010 competition, NN GC1.
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References
Bieszczad, A., Pagurek, B.: Neurosolver: Neuromorphic General Problem Solver. Information Sciences: An International Journal 105, 239–277 (1998)
Newell, A., Simon, H.A.: GPS: A program that simulates human thought. In: Feigenbaum, E.A., Feldman, J. (eds.) Computer and Thought. McGrawHill, New York (1963)
Burnod, Y.: An Adaptive Neural Network: The Cerebral Cortex. Masson, Paris (1988)
Laird, J.E., Newell, A., Rosenbloom, P.S.: SOAR: An architecture for General Intelligence. Artificial Intelligence 33, 1–64 (1987)
Nillson, N.J.: Principles of Artificial Intelligence. Tioga Publishing Company, Palo Alto (1980)
Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychological Review 82(6), 407–428 (1975)
Anderson, J.R.: A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior 22, 261–295 (1983)
Doob, J.L.: Stochastic Processes. Wiley, Chichester (1953)
Meyn, S.P., Tweedie, R.L.: Markov Chains and Stochastic Stability, 2nd edn. Cambridge University Press, Cambridge (2009)
Markov, A.A.: An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains. Translation by David Link. Science in Context 19(4), 591–600 (2006)
Baum, L.E., Petrie, T.: Statistical Inference for Probabilistic Functions of Finite State Markov Chains. Annals of Mathematical Statistics 37(6), 1554–1563 (1966)
Bieszczad, A.: Exploring Neurosolver’s Forecasting Capabilities. In: Proceedings of the 28th International Symposium on Forecasting, Nice, France, June 22-25 (2008)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn., pp. 122–125. Prentice Hall, Upper Saddle River (2010)
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Bieszczad, A. (2011). Predicting NN5 Time Series with Neurosolver. In: Madani, K., Correia, A.D., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2009. Studies in Computational Intelligence, vol 343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20206-3_19
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DOI: https://doi.org/10.1007/978-3-642-20206-3_19
Publisher Name: Springer, Berlin, Heidelberg
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