Editing Multi-agent system (section)

== Concept ==

Multi-agent systems consist of agents and their [[Biophysical environment|environment]]. Typically multi-agent systems research refers to [[software agent]]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<ref name=yoann2010>{{citation |first1=Yoann |last1=Kubera |first2=Philippe |last2=Mathieu |first3=Sébastien |last3=Picault |url=http://www.lifl.fr/SMAC/publications/pdf/aamas2010-everything.pdf |title=Everything can be Agent! |journal=Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010) |pages=1547–1548 |year=2010 }}</ref> or "agent without goals" (such as obstacle, apple or key in any simple simulation)
* Active agents<ref name=yoann2010/> with simple goals (like birds in flocking, or wolf–sheep in [[Lotka–Volterra|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),<ref>{{Russell Norvig 2003}}</ref> and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making).<ref name="Salamon2011">{{cite book | last1 = Salamon | first1 = Tomas | title = Design of Agent-Based Models | location = Repin | publisher = Bruckner Publishing | year= 2011 | page = 22 | isbn = 978-80-904661-1-1 | url=http://www.designofagentbasedmodels.info/}}</ref> 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.<ref>{{ cite journal |first1=Danny |last1=Weyns |first2=Amdrea |last2=Omicini |first3=James |last3=Odell |year=2007 |title=Environment as a first-class abstraction in multiagent systems |journal=Autonomous Agents and Multi-Agent Systems |volume=14 |issue=1 |pages=5–30 |doi=10.1007/s10458-006-0012-0 |citeseerx=10.1.1.154.4480 |s2cid=13347050 }}</ref>

=== Characteristics ===

The agents in a multi-agent system have several important characteristics:<ref>{{cite book |first=Michael |last=Wooldridge |title=An Introduction to MultiAgent Systems |publisher=[[John Wiley & Sons]] |year=2002 |pages=366 |isbn=978-0-471-49691-5}}</ref>

* Autonomy: agents at least partially independent, self-aware, [[Autonomous agent|autonomous]]
* 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)<ref>{{cite journal |first1=Liviu |last1=Panait |first2=Sean |last2=Luke |url=http://cs.gmu.edu/~eclab/papers/panait05cooperative.pdf|title=Cooperative Multi-Agent Learning: The State of the Art |journal=Autonomous Agents and Multi-Agent Systems |volume=11 |issue=3 |pages=387–434 |year=2005 |doi=10.1007/s10458-005-2631-2|citeseerx=10.1.1.307.6671 |s2cid=19706 }}</ref>

=== Self-organisation and self-direction ===

Multi-agent systems can manifest [[self-organisation]] as well as self-direction and other [[control theory|control paradigms]] and related complex behaviors even when the individual strategies of all their agents are simple.{{citation needed|date=December 2016}} 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 [[KQML|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&nbsp;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]]", 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.