What is Multiagent Systems (MAS)
Multiagent planning extends the traditional AI planning paradigm to scenarios where multiple agents, each possessing distinct capabilities, knowledge, and objectives, interact and collaborate towards shared or interrelated goals. These agents can be embodied in various forms, including software agents, robots, or human-AI hybrid systems.
Multiagent Systems (MAS) are made up of several interacting agents in an environment. Every agent in MAS is independent, thus it can act on its own and make decisions based on its observations and goals. The interactions among these agents can be cooperative, competitive, or neutral, depending on the system’s design and objectives. The main objective of MAS is to deal with issues that are hard or even impossible for a single agent to tackle because of the complexity, scale, or need for expertise.
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
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