A multi-level approach and infrastructure for agent-oriented software development
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Debugging multi-agent systems using design artifacts: the case of interaction protocols
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Using Colored Petri Nets for Conversation Modeling
Issues in Agent Communication
Proceedings of the 15th International Conference on Application and Theory of Petri Nets
Concurrent Multiple-Issue Negotiation for Internet-Based Services
IEEE Internet Computing
The Concept of Autonomy in Distributed Computation and Multi-agent Systems
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Petri net plans: a formal model for representation and execution of multi-robot plans
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Modelling, analysis and execution of multi-robot tasks using petri nets
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Representing conversations for scalable overhearing
Journal of Artificial Intelligence Research
Concurrent architecture for a multi-agent platform
AOSE'02 Proceedings of the 3rd international conference on Agent-oriented software engineering III
Quantitative and qualitative coordination for multi-robot systems
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
| Hi-index | 0.00 |
This work introduces a multi-agent framework that facilitates cooperation in multi-agent robotic systems. It uses a layered approach based on Coloured Petri Nets for modelling complex, concurrent conversations among agents. In this approach each agent employs a Coloured Petri Net model that allows agents to follow a plan specifying their interactions. It also allows programmers to plan for the concurrent feature of the conversation and make sure that all possible states of the problem space are considered. The framework assists the agents to identify and adapt different strategies for teammates and task selection dynamically. The agents can change their strategies in the course of dynamic environments to improve their performance. We have examined the performance of the agents in this framework by developing some task selection and teammate selection strategies for agents in a disaster scenario.