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πŸ•΅οΈMulti-agent systems

Multi-agent systems (MAS) are a core area of research of contemporary artificial intelligence. A multi-agent system consists of multiple decision-making agents which interact in a shared environment to achieve common or conflicting goals. MAS research spans a range of technical problems, such as how to design MAS to incentivise certain behaviours in agents, how to design algorithms enabling one or more agents to achieve specified goals in a MAS, how information is communicated and propagated among agents, and how norms, conventions and roles may emerge in MAS. A vast array of applications can be addressed using MAS methodologies, including autonomous driving, multi-robot factories, automated trading, commercial games, automated tutoring, etc. [1]

Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams.

Agents can be divided into types spanning simple to complex. Categories include:

  • Passive agents or "agent without goals" (such as obstacle, apple or key in any simple simulation)

  • Active agents with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)

  • Cognitive agents (complex calculations)

Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are deigned. [2]

Benefits of the Multi Agent Systems:

  • Multiple agents interact in common environment

  • Each agent with own sensors, effectors, goals

  • Agents have to coordinate actions to achieve goals


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