Types of Multiagent Planning
- Centralized Planning: In the case of the centralized planning, one unit or the central controller decides what to do for all the agents from the whole system’s state. This method of dealing with the coordination problem makes it easier to coordinate but at the same time, it can turn into a bottleneck and a single point of failure.
- Decentralized Planning: Decentralized planning is the process where each agent makes its own decisions depending on the information available locally and the limited communication with other agents. This approach is supposed to be more robust and scalable, but it is hard to coordinate it properly.
- Distributed Planning: The so-called distributed planning is a mixed-up method where agents have to share some info and adjust their plans in order to obtain the common world objectives. This mixture of the advantages of the centralized and decentralized approaches, tries to bring the best from both these systems and to make the factors that are both necessary for coordination and autonomy.
Multiagent Planning in AI
In the vast landscape of Artificial Intelligence (AI), multiagent planning emerges as a pivotal domain that orchestrates the synergy among multiple autonomous agents to achieve collective goals. It encompasses a spectrum of strategies and methodologies aimed at coordinating the decision-making processes of diverse agents navigating dynamic environments.
Table of Content
- What is Multiagent Systems (MAS)
- Multiagent Planning Components
- Multiagent Planning System Architecture
- Types of Multiagent Planning
- Multiagent Planning Techniques
- Multiagent Planning Problem: Coordinating Multiple Robots for Warehouse Management
- Advantages of Multiagent Planning in AI
- Applications of Multi-Agent Planning in AI
- Challenges and Limitations of Multiagent Planning in AI
- Conclusion
- FAQs on Multiagent Planning in AI