A general framework for parallel BDI agents in dynamic environments

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 m...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Huang, Shell Ying, Zhang, Huiliang
مؤلفون آخرون: School of Computer Engineering
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2011
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/90707
http://hdl.handle.net/10220/6759
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص: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. There is also no support for goal and intention reconsideration and consideration of relationships between goals at the architecture level. A parallel BDI agent architecture was proposed in [15] and evaluated in [16]. Based on the work in [15] and [16], we propose in this paper, a general framework for the parallel BDI agent model. Under this general framework, parallel BDI agents with different configurations depending on the availability of physical resources may be built. These agents have a number of advantages over the sequential one: 1. changes in the agent's environment can be detected immediately; 2. emergencies will be dealt with immediately; 3. the support is provided at the architecture level for reconsideration of desires/intentions and the consideration of goal relationships when a new belief/desire is generated. We show some example parallel BDI agents with different configurations under the framework and their performance in a set of experiments.