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README.md
Multiagent Systems
One of the possible ways of achieving intelligence is so-called emergent (or synergetic) approach, which is based on the fact that combined behavior of many relatively simple agents can result in the overall more complex (or intelligent) behavior of the system as a whole. Theoretically, this is based on the principles of Collective Intelligence, Emergentism and Evolutionary Cybernetics, which state that higher-level systems gain some sort of added value when being properly combined from lower-level systems (so-called principle of metasystem transition).
The direction of Multi-Agent Systems has emerged in AI in 1990s as a response to growth of Internet and distributed systems. On of the classical AI textbooks, Artificial Intelligence: A Modern Approach, focuses on the view of classical AI from the point of view of Multi-agent systems.
Central to Multi-agent approach is the notion of Agent - an entity that lives in some environment, which it can perceive, and act upon. This is a very broad definition, and there could be many different types and classifications of agents:
- By their ability to reason:
- Reactive agents usually have simple request-response type of behavior
- Deliberative agents employ some sort of logical reasoning and/or planning capabilities
- By the place where agent execute its code:
- Static agents work on a dedicated network node
- Mobile agents can move their code between network nodes
- By their behavior:
- Passive agents do not have specific goals. Such agents can react to external stimuli, but will not initiate any actions themselves.
- Active agents have some goals which they pursue
- Cognitive agents involve complex planning and reasoning
Multi-agent systems are nowadays used in a number of applications:
- In games, many non-player characters employ some sort of AI, and can be considered to be intelligent agents
- In video production, rendering complex 3D scenes that involve crowds is typically done using multi-agent simulation
- In systems modeling, multi-agent approach is used to simulate the behavior of a complex model. For example, multi-agent approach has been successfully used to predict the spread of COVID-19 disease worldwide. Similar approach can be used to model traffic in the city, and see how it reacts to changes in traffic rules.
- In complex automation systems, each device can act as an independent agent, which makes the whole system less monolith and more robust.