Semantics for hierarchical task-network planning
Semantics for hierarchical task-network planning
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Agent theories, architectures, and languages: a survey
ECAI-94 Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents
AgentSpeak(L): BDI agents speak out in a logical computable language
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
A Multi-threaded Approach to Simulated Soccer Agents for the RoboCup Competition
RoboCup-99: Robot Soccer World Cup III
A Parallel BDI Agent Architecture
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Learning in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
A General Framework for Parallel BDI Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A general framework for parallel BDI agents in dynamic environments
Web Intelligence and Agent Systems
Predicting Responsiveness of BDI Agent
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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The traditional BDI agent has 3 basic computational components that generate beliefs, generate intentions and execute intentions. They run in a sequential and cyclic manner. This may introduce several problems. Among them, the inability to watch the environment continuously in dynamic environments may be disastrous and makes an agent less rational --the agent may endanger itself. Two possible solutions are by parallelism and by controlling and managing the 3 components in suitable ways. We examine a parallel architecture with three parallel running components which are the belief manager, the intention generator and the intention executor. The agent built with this architecture will have the ability of performing several actions at once. To evaluate the parallel BDI agent, we compare the parallel agent against four versions of sequential agents where the 3 components of the BDI agent are controlled and managed in different ways and different time resources are allocated to them. Experiments are designed to simulate agents based on the sequential and parallel BDI architectures respectively and the ability of the agents to respond to the same sequences of external events of various priorities are assessed. The comparison results show that the parallel BDI agent has quicker response, react to emergencies immediately and its behaviour is more rational.