Are Parallel BDI Agents Really Better?

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.

[1]  J. Rice Mathematical Statistics and Data Analysis , 1988 .

[2]  Innes A. Ferguson TouringMachines: an architecture for dynamic, rational, mobile agents , 1992 .

[3]  Pattie Maes,et al.  Designing autonomous agents: Theory and practice from biology to engineering and back , 1990, Robotics Auton. Syst..

[4]  A. S. Roa,et al.  AgentSpeak(L): BDI agents speak out in a logical computable language , 1996 .

[5]  Anand S. Rao,et al.  AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language , 1996, MAAMAW.

[6]  Michael Wooldridge,et al.  Reasoning about rational agents , 2000, Intelligent robots and autonomous agents.

[7]  Anand S. Rao,et al.  An architecture for real-time reasoning and system control , 1992, IEEE Expert.

[8]  Nicholas R. Jennings,et al.  Agent Theories, Architectures, and Languages: A Survey , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[9]  James A. Hendler,et al.  HTN Planning: Complexity and Expressivity , 1994, AAAI.

[10]  Michael Wooldridge,et al.  The Belief-Desire-Intention Model of Agency , 1998, ATAL.

[11]  Huosheng Hu,et al.  A Multi-threaded Approach to Simulated Soccer Agents for the RoboCup Competition , 1999, RoboCup.

[12]  Huang Shell Ying,et al.  A parallel BDI agent architecture , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[13]  Edmund H. Durfee,et al.  A Survey of Research in Distributed, Continual Planning , 1999, AI Mag..

[14]  Rodney A. Brooks,et al.  Elephants don't play chess , 1990, Robotics Auton. Syst..

[15]  Shell-Ying Huang,et al.  A Parallel BDI Agent Architecture , 2005, IAT.

[16]  Amal El Fallah Seghrouchni,et al.  Learning in BDI Multi-agent Systems , 2004, CLIMA.

[17]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[18]  Karen L. Myers CPEF: A Continuous Planning and Execution Framework , 1999, AI Mag..

[19]  Dana S. Nau,et al.  Semantics for hierarchical task-network planning , 1994 .