A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,
A Decentralized Cluster Formation Containment Framework for Multirobot Systems
IEEE Transactions on Robotics, 2021. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a

monolithic system Monolithic system can have different meanings in the contexts of computer software and hardware. In software A software system is called "monolithic" if it has a monolithic architecture, in which functionally distinguishable aspects (for example ...

to solve.Hu, J.; Turgut, A.; Lennox, B.; Arvin, F.,
Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments
IEEE Transactions on Circuits and Systems II: Express Briefs, 2021. Intelligence may include

method Method ( grc, μέθοδος, methodos) literally means a pursuit of knowledge, investigation, mode of prosecuting such inquiry, or system. In recent centuries it more often means a prescribed process for completing a task. It may refer to: *Scien ...

ic, functional, procedural approaches,

algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...

ic search or

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

.Hu, J.; Bhowmick, P.; Lanzon, A.,
Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation
IEEE Transactions on Vehicular Technology, 2021. 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 science, and MAS in engineering and technology. Applications where multi-agent systems research may deliver an appropriate approach include online trading, disaster response, target surveillance and social structure modelling.

Concept

Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to

software agent In computer science, a software agent or software AI is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin ''agere'' (to do): an agreement to act on one's behalf. Such "action on beha ...

s. 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) Agent environments can be divided into: * Virtual * Discrete * Continuous Agent environments can also be organized according to properties such as accessibility (whether it is possible to gather complete information about the environment), determinism (whether an action causes a definite effect), dynamics (how many entities influence the environment in the moment), discreteness (whether the number of possible actions in the environment is finite), episodicity (whether agent actions in certain time periods influence other periods), and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making). Agent actions are typically mediated via an appropriate middleware. This middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination.

Characteristics

The agents in a multi-agent system have several important characteristics: * Autonomy: agents at least partially independent, self-aware,

autonomous In developmental psychology and moral, political, and bioethical philosophy, autonomy, from , ''autonomos'', from αὐτο- ''auto-'' "self" and νόμος ''nomos'', "law", hence when combined understood to mean "one who gives oneself one's ow ...

* Local views: no agent has a full global view, or the system is too complex for an agent to exploit such knowledge * Decentralization: no agent is designated as controlling (or the system is effectively reduced to a monolithic system)

Self-organisation and self-direction

Multi-agent systems can manifest self-organisation as well as self-direction and other control paradigms and related complex behaviors even when the individual strategies of all their agents are simple. When agents can share knowledge using any agreed language, within the constraints of the system's communication protocol, the approach may lead to a common improvement. Example languages are Knowledge Query Manipulation Language (KQML) or Agent Communication Language (ACL).

System paradigms

Many MAS are implemented in computer simulations, stepping the system through discrete "time steps". The MAS components communicate typically using a weighted request matrix, e.g. Speed-VERY_IMPORTANT: min=45 mph, Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, Max-Weight-UNIMPORTANT Contract Priority-REGULAR and a weighted response matrix, e.g. Speed-min:50 but only if weather sunny, Path length:25 for sunny / 46 for rainy Contract Priority-REGULAR note – ambulance will override this priority and you'll have to wait A challenge-response-contract scheme is common in MAS systems, where * First a "Who can?" question is distributed. * Only the relevant components respond: "I can, at this price". * Finally, a contract is set up, usually in several short communication steps between sides, also considering other components, evolving "contracts" and the restriction sets of the component algorithms. Another paradigm commonly used with MAS is the "

pheromone A pheromone () is a secreted or excreted chemical factor that triggers a social response in members of the same species. Pheromones are chemicals capable of acting like hormones outside the body of the secreting individual, to affect the behavio ...

", where components leave information for other nearby components. These pheromones may evaporate/concentrate with time, that is their values may decrease (or increase).

Properties

MAS tend to find the best solution for their problems without intervention. There is high similarity here to physical phenomena, such as energy minimizing, where physical objects tend to reach the lowest energy possible within the physically constrained world. For example: many of the cars entering a metropolis in the morning will be available for leaving that same metropolis in the evening. The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.

Research

The study of multi-agent systems is "concerned with the development and analysis of sophisticated AI problem-solving and control architectures for both single-agent and multiple-agent systems." Research topics include: * agent-oriented software engineering * beliefs, desires, and intentions ( BDI) * cooperation and coordination *

distributed constraint optimization Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization. A DCOP is a problem in which a group of agents must distributedly choose values for a set of variables such that the cost of a set of cons ...

(DCOPs) * organization * communication * negotiation * distributed problem solving *

multi-agent learning ] Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does acti ...

agent mining Agent mining is an interdisciplinary area that synergizes multiagent systems with data mining and machine learning. The interaction and integration between multiagent systems and data mining have a long history. The very early work on agent mi ...

* scientific communities (e.g., on biological flocking, language evolution, and economics) * dependability and fault-tolerance * robotics, multi-robot systems (MRS), robotic clusters

Frameworks

Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF standards). These frameworks e.g. JADE, save time and aid in the standardization of MAS development. Currently though, no standard is actively maintained from FIPA or OMG. Efforts for further development of software agents in industrial context are carried out in IEEE IES technical committee on Industrial Agents.

Applications

MAS have not only been applied in academic research, but also in industry. MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films. It is widely advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability and self-healing networks. They are being used for coordinated defence systems. Other applications include

transportation Transport (in British English), or transportation (in American English), is the intentional movement of humans, animals, and goods from one location to another. Modes of transport include air, land (rail and road), water, cable, pipeline, ...

,Xiao-Feng Xie, S. Smith, G. Barlow
Schedule-driven coordination for real-time traffic network control
International Conference on Automated Planning and Scheduling (ICAPS), São Paulo, Brazil, 2012: 323–331. logistics, graphics, manufacturing, power system, smartgrids and GIS. Also, Multi-agent Systems Artificial Intelligence (MAAI) are used for simulating societies, the purpose thereof being helpful in the fields of climate, energy, epidemiology, conflict management, child abuse, ....AI can predict your future behaviour with powerful new simulations
/ref> Some organisations working on using multi-agent system models include Center for Modelling Social Systems, Centre for Research in Social Simulation, Centre for Policy Modelling, Society for Modelling and Simulation International. Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.

Waymo Waymo LLC, formerly known as the Google self-driving car project, is an American autonomous driving technology company headquartered in Mountain View, California. It is a subsidiary of Alphabet Inc, the parent company of Google. Waymo oper ...

has created a multi-agent simulation environment Carcraft to test algorithms for

self-driving car A self-driving car, also known as an autonomous car, driver-less car, or robotic car (robo-car), is a car that is capable of traveling without human input.Xie, S.; Hu, J.; Bhowmick, P.; Ding, Z.; Arvin, F.,Distributed Motion Planning for S ...

s. It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior.