Navigating with a rat brain: a neurobiologically-inspired model for robot spatial representation
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
The Behavior Language: User''s Guide
The Behavior Language: User''s Guide
A Colony Architecture for an Artificial Creature
A Colony Architecture for an Artificial Creature
A Distributed Model for Mobile Robot Environment-Learning and Navigation
A Distributed Model for Mobile Robot Environment-Learning and Navigation
Lazy Acquisition of Place Knowledge
Artificial Intelligence Review - Special issue on lazy learning
Reusable Strategies for Software Agents via the Subsumption Architecture
APSEC '99 Proceedings of the Sixth Asia Pacific Software Engineering Conference
A semi-autonomous wheelchair with helpstar
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Modeling adaptive autonomous agents
Artificial Life
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We address the problem of integrating a number of capabilities into a situated system without introducing a hybrid solution consisting of a reactive low-level, and a deliberative high-level. We demonstrate a fully intergrated physical system (a mobile robot) performing navigation, landmark detection, map learning, and planning, without explicit integration. Inspired by biology and the subsumption architecture, our approach proceeds bottom-up and is fully reactive. It maximizes the use of direct information from the world, and minimizes communication within the system. The control is fully distributed over all of the components, and does not use any manipulable data structures or symbolic representations. Instead, the representation of the entire system is homogeneous; it consists of simple reactive rules which encode both the control strategy and the knowledge of the system. The exchange of knowledge within the system is mostly done implicitly, by observing, and relying on, the effects of the other components. Integration is accomplished through the components' interaction with the environment. The homogeneous, behavior-based nature of the representation does not make a distinction between the reactive and deliberating parts of the system, making them all active and interactive.