Agents in dynamic contexts, a system for learning plans

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

Risultato della ricerca: Conference contribution

1 Citazioni (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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings of the ACM Symposium on Applied Computing
Pagine823-825
Numero di pagine3
Stato di pubblicazionePublished - 2020

All Science Journal Classification (ASJC) codes

  • Software

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