Agents in dynamic contexts, a system for learning plans

Valeria Seidita, Antonio Chella, Francesco Lanza, Pei Wang, Patrick Hammer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Reproducing the human ability to cooperate and collaborate in a dynamic environment is a significant challenge in the field of human-robot teaming interaction. Generally, in this context, a robot has to adapt itself to handle unforeseen situations. The problem is runtime planning when some factors are not known before the execution starts. This work aims to show and discuss a method to handle this kind of situation. Our idea is to use the Belief-Desire-Intention agent paradigm, its the Jason reasoning cycle and a Non-Axiomatic Reasoning System. The result is a novel method that gives the robot the ability to select the best plan.
Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
Pages823-825
Number of pages3
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Seidita, V., Chella, A., Lanza, F., Wang, P., & Hammer, P. (2020). Agents in dynamic contexts, a system for learning plans. In Proceedings of the ACM Symposium on Applied Computing (pp. 823-825)