Multi-Agent Systems

In classical AI, an agent, having a global vision of the problem, has all the necessary skills, knowledge and resources for individually solving a task. In contrast, multi-agent systems (MAS) assume that a single agent can have only a partial understanding of the task and can solve only some of its subtasks. MAS are used to solve complex problems that are difficult to solve by an individual agent and require interacting agents. The activity and organization of artificial systems and their collaborative approach to the concerted solution of tasks are fundamental characteristics of advanced information technology and network organizations, built on principles of MAS. Synergy within MAS is based on processes of interactions between collective agents, leading to the formation of artificial communities with fundamentally new features. The tasks in MAS are distributed between the agents, each of which is considered as a member of the organization. Distribution of tasks involves assigning roles to each of the agents. Depending on whether the distribution comes from the task set or the ability of each agent, one can distinguish between systems of distributed AI and systems of decentralized AI.

In distributed AI, the process that decomposes original tasks and combines task outputs to obtain solutions is centralized. MAS is rigidly projected downward on the basis of partitioning the general task into separate, relatively independent subtasks and preliminary determination of the agents’ roles. In decentralized AI, the distribution of tasks happens largely spontaneously, directly in process of the interaction and communication between the agents.

Distributed AI requires the development of organizations capable of solving tasks in unison. These organizations consist of a set of individual agents, each with its well-defined subtask. Distributed AI systems exhibit three main characteristics: distribution of tasks, distribution of power, and communication between agents. The typical scheme of a MAS based distributed solution for a task includes these steps:

1. Task decomposition — decompose the original problem into separate subtasks

2. Subtask distribution — distribute the subtasks to various agents that solve or divide them

3. Subtask composition — combine the results from each of the subtasks for the overall result

The ideology of distributed task solving assumes the separation of knowledge and resources between the agents and distribution of management and power. Ideally, a governing body provides a common model with global criteria for achieving goals.

In fully decentralized systems, management takes place only because of local interactions between agents, not a distributed solution of some general task, but a coordination of autonomous agents in a dynamic multi-agent world. Local tasks of individual agents, solved on the basis of local models and criteria, are described along with the distributed knowledge and resources.

Distributed AI can solve a range of tasks, but it is strictly centralized and limited by design pattern. Decentralized AI removes points of failure and creates a trustless, autonomous environment, but it is limited in goal complexity and adaptability. Combining distributed AI with decentralized AI into a new AI system based on existing models of multi-agent systems compensates their shortcomings while enhancing their benefits.