Can AI planners solve practical problems?
Computational Intelligence
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
BDI Models and Systems: Bridging the Gap
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Towards a Distributed, Environment-Centered Agent Framework
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
A Planning Component for RETSINA Agents
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Propositional planning in BDI agents
Proceedings of the 2004 ACM symposium on Applied computing
Hierarchical planning in BDI agent programming languages: a formal approach
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
First principles planning in BDI systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
An unified framework for programming autonomous, intelligent and mobile agents
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
A practical agent programming language
ProMAS'07 Proceedings of the 5th international conference on Programming multi-agent systems
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Time and uncertainty of the environment are very important aspects in the development of real world applications. Another important issue for the real world agents is, the balance between deliberation and reactivity. But most of the agent oriented programming languages ignore some or all of these important aspects. In this paper we try to fill this gap by presenting an extension to the architecture of CLAIM agent oriented programming language to endow the agents with the planning capability. We remove the assumption that agents' actions are instantaneous. We are interested in the temporal planning of on the fly goals. A coherrent framework is proposed in which agents are able to generate, monitor and repair their temporal plans. Our proposed framework creates a balance between reactivity and deliberation. This work could be considered as a first step towards a complete temporal planning solution for an AOP language.