Reasoning in Highly Reactive Environments
Francesco Pacenza
- Year
- 2019
- Access
- Open access
Abstract
The aim of my Ph.D. thesis concerns Reasoning in Highly Reactive Environments. As reasoning in highly reactive environments, we identify the setting in which a knowledge-based agent, with given goals, is deployed in an environment subject to repeated, sudden and possibly unknown changes. This is for instance the typical setting in which, e.g., artificial agents for video-games (the so called "bots"), cleaning robots, bomb clearing robots, and so on are deployed. In all these settings one can follow the classical approach in which the operations of the agent are distinguished in "sensing" the environment with proper interface devices, "thinking", and then behaving accordingly using proper actuators. In order to operate in an highly reactive environment, an artificial agent needs to be: 1. Responsive -> The agent must be able to react repeatedly and in a reasonable amount of time; 2. Elastic -> The agent must stay reactive also under varying workload; 3. Resilient -> The agent must stay responsive also in case of internal failure or failure of one of the programmed actions in the environment. Nowadays, thanks to new technologies in the field of Artificial Intelligence, it is already technically possible to create AI agents that are able to operate in reactive environments. Nevertheless, several issues stay unsolved, and are subject of ongoing research.
Keywords
Related papers
A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing
Qiang Cui, Chuan Yu, Daoqian Yang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
Geometric digital twin: A digital and intelligent model for aero-engine assembly accuracy prediction
Ke Shang, Xin Jin, Teli Xu +4 more
Robotics and Computer-Integrated Manufacturing · 2026
Revolutionizing Industries Through AI-Driven Robotics
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
Design and dynamic performance prediction of a novel large-aperture offset-feed deployable antenna
Chuang Shi, Tianming Liu, Ning Xue +6 more
Aerospace Science and Technology · 2026