Multi-Agent System (MAS)
A Multi-Agent System (MAS) is a distributed system that is an agent-based computing system composed of multiple interacting agents who can perform multi-agent tasks.
- Context:
- It can (typically) involve Autonomous Agents (capable of independent decision-making).
- It can range from being a Simple Communication Multi-Agent System to being a Complex Communication Multi-Agent System (with multi-agent dialogues).
- It can range from being a Simple Environment Multi-Agent System to being a Complex Environment Multi-Agent System.
- It can range from being a Simple Agents Multi-Agent System to being a Complex Agents Multi-Agent System (e.g. with perception, learning, and problem-solving).
- It can range from being a Cooperative Multi-Agent System to being a Competitive Multi-Agent System.
- It can involve Negotiation Acts.
- ...
- Example(s):
- Learning and Optimization:
- Multi-Agent Inventory Management System: Coordinating multiple robots or systems in automated warehouses to manage inventory, sort packages, and optimize logistics.
- Healthcare Operations Optimization System: Applying agent-based models to manage hospital resources, staff, and patient care more efficiently and effectively.
- Simulation and Analysis:
- Citizen Interaction Simulation System: Simulating interactions of citizens in urban planning models or social science experiments to understand social behavior and optimize city policies.
- Economic Market Simulation System: Utilizing agents to represent various economic entities in market simulations, studying economic theories or forecasting market behaviors.
- Robotics and Autonomous Coordination:
- Autonomous Traffic Coordination System: Managing the flow of autonomous vehicles in traffic systems, optimizing for efficiency and safety in urban or highway settings.
- Swarm Robotics in Search and Rescue System: Deploying groups of robots in coordination for tasks like search and rescue operations, environmental monitoring, or other collaborative activities.
- Advanced Applications:
- Multi-Agent Space Exploration System: Coordinating planetary rovers, drones, or spacecraft in space exploration missions, involving tasks like surface exploration or astrophysical observations.
- Intelligent Energy Distribution System in Smart Grids: Agents managing the distribution and consumption of energy in smart grid systems to enhance efficiency and reliability.
- …
- Learning and Optimization:
- Counter-Example(s):
- See: Agent-Based Model, Distributed Computing, Intelligent Agents, Multi-Agent Learning, Agent-based Computing Model, Evolutionary computation, Game Theory.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Multi-agent_system Retrieved:2019-2-3.
- A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM). The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which don't necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. The terminology of ABM tends to be used more often in the sciences, and MAS in engineering and technology [1]. Applications where multi-agent systems research may deliver an appropriate approach include online trading, disaster response and social structure modelling.
- A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
2014
- (Panisson et al., 2014) ⇒ Alison R. Panisson, Felipe Meneguzzi, Moser Silva Fagundes, Renata Vieira, and Rafael H. Bordini. (2014). “Formal Semantics of Speech Acts for Argumentative Dialogues.” In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems. ISBN:978-1-4503-2738-1
- QUOTE: Communication is one of the key issues in building multi-agent systems, where the agents need to communicate in order to resolve differences of opinion or conflicts of interest, to work coordinately, to resolve dilemmas, and to reach agreements. Many of these communication requirements cannot be fulfilled by the exchange of single messages. They require the exchange of sequences of messages upon related statements. Therefore, agents need the ability to engage in multi-agent dialogues
2011
- (Niazi & Hussain, 2011) ⇒ Muaz Niazi, and Amir Hussain. (2011). “Agent-based Computing from Multi-agent Systems to Agent-based Models: A Visual Survey.” In: Scientometrics Journal, 89(2). doi:10.1007/s11192-011-0468-9
- QUOTE: This broad base of applications of this research area thus often leads to confusions regarding the exact semantics of various terms in the literature. This is perhaps tied closely to the evolution of “agent-based computing” into a wide assortment of communities. These communities have at times, perhaps nothing other than the notion of an “agent” in common with each other.
What makes the study of this domain even harder is related closely with the keywords used by the researchers. Not only are the application domains varied, Agent-based modeling (Axelrod 1997) is at times confused with similar but actually somewhat different sub-domains such as multiagent systems (Lesser 2007) in the domain of Artificial Intelligence. While at other times, agent-based modeling is referred by completely different keywords but with synonymous meanings such as “Individual-based modeling”. However, all of these eventually tie in together in the domain of agent-based computing (Wooldridge 1998; Jennings 1999b).
(...) A thorough topic search for data was devised to cater for various aspects and keywords used in agent-based computing in the following three sub-domains:
- QUOTE: This broad base of applications of this research area thus often leads to confusions regarding the exact semantics of various terms in the literature. This is perhaps tied closely to the evolution of “agent-based computing” into a wide assortment of communities. These communities have at times, perhaps nothing other than the notion of an “agent” in common with each other.
2009
- (Wooldridge, 2009) ⇒ Michael Wooldridge. (2009). “An Introduction to MultiAgent Systems, 2nd edition.” In: Wiley Publishing. ISBN:0470519460, ISBN:9780470519462.
- QUOTE: The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). … Multiagent systems are systems composed of multiple interacting computing elements, known as agents.
2002
- (Parson et al., 2002) ⇒ Simon Parsons, Michael Wooldridge, Leila Amgoud. (2002). “An Analysis of Formal Inter-agent Dialogues". In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems doi:10.1145/544741.544835
2000
- (Amgoud et al., 2000) ⇒ Leila Amgoud, Nicolas Maudet, and Simon Parsons. (2000). “Modeling Dialogues Using Argumentation". In: Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
- (Amgoud et al., 2000) ⇒ Leila Amgoud, Simon Parsons, and Nicolas Maudet. (2000). “Arguments, Dialogue, and Negotiation.” In: Journal of Artificial Intelligence Research, 10(11).
- QUOTE: Negotiation is widely regarded as a key issue in building multi-agent systems. … All mechanisms for negotiation have at their heart an exchange of offers. Agents make offers that they find acceptable and respond to offers made to them.