Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains

Abstract

Various methods of learning qualitative models by autonomous robots have previously been proposed as a viable alternative to traditional numerical modeling, but an efficient way of using such models for planning still remained an open problem. This paper summarizes a doctorat thesis, which proposes a novel domain-independent qualitative approach to automated planning and execution of robotic plans. The proposed method is demonstrated in five different robotic domains. Različne metode učenja kvalitativnih modelov v avtonomni robotiki so bile v preteklosti predlagane kot možna alternativa tradicionalnemu numeričnemu modeliranju, toda učinkovit način uporabe takšnih modelov za planiranje je še vedno ostal odprt problem. Ta članek povzema doktorska disertacijo, ki predlaga nov, domensko neodvisen kvalitativen pristop k avtomatiziranemu planiranju in izvedbi robotskih planov. Disertacija predlagano metodo demonstrira v petih različnih robotskih domenah.

References

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Šoberl, D. (2021) Automated planning with induced qualitative models in dynamic robotic domains, PhD thesis, University of Ljubljana. https://repozitorij.uni-lj.si/IzpisGradiva.php?id=126285

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Šoberl D., Bratko I. (2019) Learning Explainable Control Strategies Demonstrated on the Pole-and-Cart System, Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science, vol 11606, Springer, Cham, pp. 483--494. DOI: 10.1007/978-3-030-22999-3_42

Authors

  • Domen Šoberl University of Ljubljana Faculty of Computer and Information Science

DOI:

https://doi.org/10.31449/inf.v45i3.3759

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Published

09/15/2021

How to Cite

Šoberl, D. (2021). Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains. Informatica, 45(3). https://doi.org/10.31449/inf.v45i3.3759

Issue

Vol. 45 No. 3 (2021)

Section

Thesis summary

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