Abstract
Qualitative modeling tends to be closer to human type of reasoning than traditional numerical modeling and proved to be very useful in certain branches of cognitive robotics. However, due to the lack of precise numerical relations, planning with qualitative models has been achieved to a limited extent. Typically, it is bound to predicting possible future behaviors of the system, and demands additional exploration of numerical relations, before constructed plans can be executed. In this paper we show how qualitative models can be interpreted in terms of reactive planning, to produce executable actions without the need for additional numerical learning. We demonstrate our method on two classical motion planning problems – pursuing and obstacle avoidance, and a complex problem of pushing objects.
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References
Bratko, I.: An assessment of machine learning methods for robotic discovery. In: 30th International Conference on Information Technology Interfaces, ITI 2008, pp. 53–60, Dubrovnik (2008)
Bratko, I., Šuc, D.: Learning qualitative models. AI Mag. 24(4), 107–119 (2003)
De Kleer, J., Brown, J.S.: A qualitative physics confluences. Artif. Intell. 24(1–3), 7–83 (1984)
Forbus, K.D.: Qualitative process theory. Artif. Intell. 24(1–3), 85–168 (1984)
Kuipers, B.: Qualitative simulation. Artif. Intell. 29(3), 289–338 (1986)
Sammut, C., Yik, T.: Multistrategy learning for robot behaviours. In: Advances in Machine Learning I, Chap. 5, pp. 457–476. Springer, Heidelberg (2010)
Šoberl, D., Žabkar, J., Bratko, I.: Qualitative planning of object pushing by a robot. In: Foundations of Intelligent Systems. LNCS, vol. 9384, pp. 410–419. Springer (2015)
Troha, M., Bratko, I.: Qualitative learning of object pushing by a robot. In: 25th International Workshop on Qualitative Reasoning, pp. 175–180, Barcelona (2011)
Wellman, M.P.: Qualitative simulation with multivariate constraints. In: Second International Conference on Principles of Knowledge Representation and Reasoning, pp. 547–557. Morgan Kaufmann (1991)
Wiley, T., Bratko, I.: A Multi-strategy architecture for on-line learning of robotic behaviours using qualitative reasoning. In: Proceedings of the Third Annual Conference on Advances in Cognitive Systems, vol. 2015, pp. 1–16, Atlanta, USA (2015)
Wiley, T., Sammut, C., Hengst, B., Bratko, I.: A planning and learning hierarchy using qualitative reasoning for the on-line acquisition of robotic behaviors. Adv. Cogn. Syst. 4, 93–112 (2016)
Žabkar, J., Bratko, I., Demšar, J.: Learning qualitative models through partial derivatives by Padé. In: Proceedings of the 21st Annual Workshop on Qualitative Reasoning, pp. 193–202 (2007)
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Šoberl, D., Bratko, I. (2017). Reactive Motion Planning with Qualitative Constraints. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_5
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DOI: https://doi.org/10.1007/978-3-319-60042-0_5
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