Abstract
We investigated the role of the length of the future time interval in which an agent predicts what will happen. A number of simulated robot experiments were performed where four thieves try to collect pieces of gold from a house that is guarded by a single robot. The thieves try to anticipate the movement of the guard to select behaviors that will allow them to steel the gold without being seen. This scenario was investigated in four experiments with different visual fields of the guard and different strategies of the thieves. The results show that it is not always better to predict longer into the future and that best behavior would results when the agents match their predictions to the time it will take to perform their tasks.
This work was supported in part by the EU funded project MindRACES, FP6-511931.
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References
Rosen, R.: Anticipatory systems. Pergamon Press, Oxford (1985)
Davidsson, P.: Learning by linear anticipation in multi-agent systems. Distributed Artificial Intelligence Meets Machine Learning 1221, 62–72 (1996)
Sharifi, M., Mousavian, H., Aavani, A.: Predicting the future state of the robocup simulation environment: heuristic and neural networks approaches. Systems, Man and Cybernetics 1, 27–32 (2003)
Veloso, M., Stone, P., Bowling, M.: Anticipation as a key for collaboration in a team of agents: A case study in robotic soccer. In: Schenker, P.S., McKee, G.T., eds.: Proceedings of SPIE Sensor Fusion and Decentralized Control in Robotic Systems II, Bellingham, vol. 3839, pp. 134–143 (September 1999)
Behnke, S., Egorova, A., Gloye, A., Rojas, R., Simon, M.: Predicting away robot control latency. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 712–719. Springer, Heidelberg (2004)
Johansson, B., Balkenius, C.: Robots with anticipation and attention. In: Funk, P., Rognvaldsson, T., Xiong, N. (eds.) Advances in Artificial Intelligence in Sweden, pp. 202–204. Mälardalen University, Västerås (2005)
Johansson, B., Kolodziej, A., Balkenius, C.: Anticipation and attention in robot control. LUCS Minor 14, Lund University Cognitive Science (2008)
Balkenius, C., Morén, J., Johansson, B., Johnsson, M.: Ikaros: Building cognitive models for robots. In: Hülse, M., Hild, M. (eds.) Workshop on current software frameworks in cognitive robotics integrating different computational paradigms, Nice, France, in conjunction with IROS 2008 (2008)
Jordan, M., Rumelhart, D.: Forward models: Supervised learning with a distal teacher. Cognitive Science 16, 207–354 (1992)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering 82, 35–45 (1960)
Butz, M.V.: Anticipatory learning classifier systems. Kluwer, Boston (2002)
Wolpert, D.M., Flanagan, J.: Motor prediction. Current Biology (11), R729–R732 (2001)
Balkenius, C., Johansson, B.: Event prediction and object motion estimation in the development of visual attention. In: Berthouze, L., Kaplan, F., Kozima, H., Yano, H., Konczak, J., Metta, G., Nadel, J., Sandini, G., Stojanov, G., Balkenius, C. (eds.) Proceedings of the Fifth International Conference on Epigenetic Robotics. Lund University Cognitive Studies, vol. 123, pp. 17–22 (2005)
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Johansson, B., Balkenius, C. (2009). Prediction Time in Anticipatory Systems. In: Pezzulo, G., Butz, M.V., Sigaud, O., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2008. Lecture Notes in Computer Science(), vol 5499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02565-5_16
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DOI: https://doi.org/10.1007/978-3-642-02565-5_16
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