Intention is choice with commitment
Artificial Intelligence
Intelligent software agents
Multihierarchical Graph Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
A Semi-Autonomous Reactive Control Architecture
Journal of Intelligent and Robotic Systems
Formalizing Regions in the Spatial Semantic Hierarchy: An AH-Graphs Implementation Approach
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Theory and Evaluation of Human Robot Interactions
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
The Journal of Machine Learning Research
Assistive navigation of a robotic wheelchair using a multihierarchical model of the environment
Integrated Computer-Aided Engineering
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Control Architecture for Human–Robot Integration: Application to a Robotic Wheelchair
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment particularly, the driver in the case of a wheelchair to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities that include algorithmic behaviours and human skills and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.