A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
An Behavior-based Robotics
Interleaving Planning and Robot Execution for Asynchronous User Requests
Autonomous Robots - Special issue on autonomous agents
Experiences with an Autonomous Robot Attending AAAI
IEEE Intelligent Systems
2003 AAAI robot competition and exhibition
AI Magazine
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Planning for a mobile robot to attend a conference
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Development environments for autonomous mobile robots: A survey
Autonomous Robots
Spartacus attending the 2005 AAAI conference
Autonomous Robots
Towards a higher level of human-robot interaction and integration
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A brochette of socially interactive robots
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Electronic Notes in Theoretical Computer Science (ENTCS)
A survey of motivation frameworks for intelligent systems
Artificial Intelligence
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To operate in natural environmental settings, autonomous mobile robots need more than just the ability to navigate in the world, react to perceived situations or follow pre-determined strategies: they must be able to plan and to adapt those plans according to the robot's capabilities and the situations encountered. Navigation, simultaneous localization and mapping, perception, motivations, planning, etc., are capabilities that contribute to the decision-making processes of an autonomous robot. How can they be integrated while preserving their underlying principles, and not make the planner or other capabilities a central element on which everything else relies on? In this paper, we address this question with an architectural methodology that uses a planner along with other independent motivational sources to influence the selection of behavior-producing modules. Influences of the planner over other motivational sources are demonstrated in the context of the AAAI Challenge.