Approaching Multi-Agent Systems (MAS): a Collaborative AI World

Multi-Agent System (MAS) A computing system consisting of multiple interacting Intelligent Agents. A multi-intelligent system can be used to solve problems that are difficult or impossible to solve with a single intelligent agent or a single system. Intelligents can be robots, people, or software. They can have different goals and capabilities and collaborate or compete to achieve their individual or common goals.

Multi-intelligent body systems emphasize the autonomy, interactivity, and adaptability of intelligences to make them more robust, flexible, and scalable in complex, dynamic, and open environments.

Core concepts

Agent

An Intelligent Body is the basic component of a MAS that senses the environment, reasoning, decision-making, and taking action. An Intelligence typically has the following characteristics:

• Autonomy: Intelligent bodies can act autonomously according to their state and goals without external control.
• Reactivity: Intelligent bodies are able to sense changes in the environment and respond in a timely manner.
• Proactivity: Intelligent bodies are able to act proactively to achieve goals, rather than just responding passively to their environment.
• Sociality: Intelligences are able to interact, collaborate or compete with other intelligences.

Environment

The environment is the external world in which an intelligent body is located, which provides perceptual information to the intelligent body and is affected by the actions of the intelligent body. The environment can be physical (e.g., the real world) or virtual (e.g., a computer simulation).

Interaction

Interaction refers to communication and coordination between intelligences. Intelligent bodies can interact by sharing knowledge, negotiating goals, and coordinating actions. Interaction can be cooperative or competitive.

MAS Architecture

The architecture of a MAS describes how intelligences are organized and interact. Common MAS architectures include:

1. Traditional: Intelligentsia interact with their environment through observation and action. This architecture is simple and straightforward, similar to the interaction of individual organisms with their environment.
2. Reactive: The behavior of an intelligent body is triggered directly by perceived environmental stimuli and does not involve a complex reasoning process. Intelligents in this architecture are quick to respond, but may lack long-term planning capabilities.
3. Deliberative: Intelligentsia have internal state and knowledge representations and are capable of reasoning and planning. Intelligents in this architecture are capable of making complex decisions, but may be slow to respond.
4. Hybrid: Combining the strengths of both reactive and deliberative architectures, intelligences are able to both respond quickly to environmental changes and plan for the long term.
5. Based on Belief-Desire-Intention (BDI).: A commonly used deliberative architecture in which an intelligent's behavior is driven by its beliefs (perceptions of the world), expectations (states it wishes to achieve), and intentions (actions it plans to take).
6. ReAct (Reasoning and Acting): Reasoning while acting, similar to how humans think before acting.
7. Based on the Large Language Model (LLM).: Leveraging LLM's powerful language comprehension and generation capabilities to empower intelligences with stronger reasoning and collaboration capabilities.

The following diagram shows the MAS architecture

MAS key technologies

Communication

Intelligent bodies need to communicate with each other to exchange information and coordinate their actions. Commonly used communication methods include:

• direct communication: Send and receive messages directly between intelligences.
• indirect communication: Intelligentsia communicate through a shared environment or intermediate medium.
• communications protocol: Communication between intelligences needs to follow certain protocols such as KQML (Knowledge Query and Manipulation Language) and FIPA (Foundation for Intelligent Physical Agents).

Coordination

Coordination is the collaboration between intelligences to achieve a common goal. Commonly used coordination mechanisms include:

• Negotiation: Intelligentsia negotiates an agreed-upon course of action.
• Cooperation: Intelligentsia work together to accomplish tasks and share resources and knowledge.
• Competition: Intelligentsia compete for limited resources.

Learning

Learning is the ability of an intelligence to improve its behavior through interaction with the environment or other intelligences. Commonly used learning methods include:

• Reinforcement Learning: Intelligentsia learn by trial and error, adapting behavioral strategies based on feedback from the environment.
• Multi-Agent Reinforcement Learning (MARL): Multiple intelligences learn, interact and co-evolve in a shared environment.
• Evolutionary Algorithm: Simulates the process of biological evolution to optimize the behavior of intelligences through operations such as selection, crossover, and mutation.

Planning

Planning is the process by which an intelligent individual develops a plan of action to achieve a goal. Commonly used planning methods include:

• Classical Planning: Find a sequence of actions from an initial state to a goal state based on a state space search.
• Hierarchical Planning: Decompose a complex task into multiple subtasks and plan them separately.
• Multi-Agent Planning: Collaborative development of action plans by multiple intelligences.

Areas of application for MAS

The applications of MAS are very broad and cover many areas where multiple intelligences are required to work together, for example:

• Robotics: Multiple robots collaborate on tasks such as exploration, rescue, and handling.
• Distributed Control: Multiple intelligences collaborate to control complex systems, such as smart grids and intelligent transportation systems.
• E-commerce: Multiple intelligentsia represent buyers and sellers in automated negotiations and transactions.
• Games: Multiple game characters work together or against each other to provide a more realistic and challenging gaming experience.
• Simulation: To model complex social, economic, or biological systems and study their evolutionary patterns.
• code development: Intelligentsia can collaborate on code writing, testing, and review.
• Smart City/Smart Manufacturing:: Multiple intelligences control infrastructure in cities and production equipment in factories to accomplish complex control tasks.
• financial transaction:: Financial trading intelligences can mimic human traders, demonstrating capabilities beyond those of humans in high-frequency trading, and decision analysis.

Challenges and future of MAS

Although MAS has made significant progress, many challenges remain:

• Heterogeneity: How can interoperability between heterogeneous intelligences be realized when the intelligences may have different hardware, software and communication protocols?
• Scalability: How to ensure the performance and stability of the system as the number of intelligences increases?
• Robustness: How can we ensure that the system remains operational in the face of uncertainties such as failure of intelligences and changes in the environment?
• Security: How to prevent attacks and damage by malicious intelligences?
• Ethics: How can we ensure that MAS acts in an ethical and moral manner?

MAS is poised for new opportunities as artificial intelligence technology continues to evolve, especially with the rise of large-scale language modeling (LLM), whose powerful reasoning and linguistic capabilities are expected to revolutionize MAS in the following ways:

• Stronger Intelligent Body Capabilities: LLM can empower intelligences with enhanced natural language understanding and generation, enabling them to better understand human intent and human-computer interaction.
• A more efficient way to collaborate: LLM can facilitate knowledge sharing and collaborative reasoning among intelligences to improve collaboration efficiency.
• Wider range of application scenarios: LLM can expand the application areas of MAS, such as intelligent customer service, intelligent education and intelligent medical care.

precisely as Nexus Project Introductory Articlementioned in "Recent advances in the field of Large Language Modeling (LLM) are improving the MAS architecture and its application capabilities, such as near-human reasoning. When integrated into MAS architectures, LLMs can act as central reasoning intelligences, enhancing adaptability, collaboration, and decision-making in dynamic environments."

In the future, MAS will develop in the direction of smarter, more collaborative and more reliable, bringing more convenience and value to human society.