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
JAM: a BDI-theoretic mobile agent architecture
Proceedings of the third annual conference on Autonomous Agents
AgentSpeak(XL): efficient intention selection in BDI agents via decision-theoretic task scheduling
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
The orchestration of behaviours using resources and priority levels
Proceedings of the Eurographic workshop on Computer animation and simulation
Intention Scheduling for BDI Agent Systems
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
An agent's activities are controlled by his priorities
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Norm internalization in artificial societies
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Activity scheduling for a robotic caretaker agent for the elderly
International Journal of Intelligent Information and Database Systems
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Intention scheduling mechanism plays a critical role in the correct behaviour of BDI agents. For human-like agents, the independent intentions should be scheduled based on their priorities, which show the respective importance and urgency of the intentions. We propose to enrich the BDI agent architecture with 2 processing components, a PCF (Priority Changing Function) Selector and a Priority Controller. These enable a BDI agent to assign an initial priority value to an intention and change it with time according to the chosen PCF. The initial priority value reflects its urgency at the time of intention creation. The PCF selected defines how the priority should change with time. As an example, we design a function by simulating human behaviors when dealing with several things at the same time. The priority first increases with time according to the Gaussian function to simulate that people are more inclined to do something which has been on their mind for sometime. After a certain time, if the intention still does not get executed because of other higher priority intentions, its priority will decrease according to the Ebbinghaus forgetting curve. Experiment results show that with this mechanism, the agent can show some human-like characteristics when scheduling intention to execute. This can be used when simulating human-like agents.