SOAR: an architecture for general intelligence
Artificial Intelligence
A design for the ICARUS architecture
ACM SIGART Bulletin
Technical Note: \cal Q-Learning
Machine Learning
Managing multiple tasks in complex, dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Controlling physical agents through reactive logic programming
Proceedings of the third annual conference on Autonomous Agents
Using background knowledge to speed reinforcement learning in physical agents
Proceedings of the fifth international conference on Autonomous agents
IEEE Transactions on Knowledge and Data Engineering
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Cognitive architectures and general intelligent systems
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Learning Recursive Control Programs from Problem Solving
The Journal of Machine Learning Research
IODA: an interaction-oriented approach for multi-agent based simulations
Autonomous Agents and Multi-Agent Systems
Guiding inference through relational reinforcement learning
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Learning teleoreactive logic programs from problem solving
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Acquisition of hierarchical reactive skills in a unified cognitive architecture
Cognitive Systems Research
Reactive goal management in a cognitive architecture
Cognitive Systems Research
| Hi-index | 0.00 |
In this paper we describe ICARUS, an integrated architecture for intelligent physical agents. The framework supports long-term memories for hierarchical concepts and skills, along with mechanisms for recognizing concepts that hold in the environment, determining which skills are applicable, and selecting for execution the skill with the highest expected value. We illustrate these processes with examples from the domain of in-city driving, and we report experimental studies on a package-delivery task that examine ICARUS' ability to combine reactive behavior with persistence over time. We conclude with a discussion of related work on agent architectures and our plans for extending the system.