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.
Lanza F., Hammer P., Seidita V., Wang P., Chella A. (2020). Agents in dynamic contexts, a system for learning plans. In Proceedings of the ACM Symposium on Applied Computing (pp. 823-825). Association for Computing Machinery [10.1145/3341105.3374083].
Agents in dynamic contexts, a system for learning plans
Lanza F.;Seidita V.;Chella A.
2020-01-01
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
2020. SAC2020+TOC.pdf
accesso aperto
Tipologia:
Versione Editoriale
Dimensione
958.66 kB
Formato
Adobe PDF
|
958.66 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
