What is multi-agent programming?
A multi-agent system is composed of multiple interacting software components known as agents, which are typically capable of cooperating to solve problems that are beyond the abilities of any individual member. Multi-agent systems are important primarily because they have been found to have very wide applicability.
Which is an example multi-agent?
A good example is the expert assistant, where an agent acts like an expert assistant to a user attempting to fulfil some task on a computer. MAS is a computer-based environment made of multiple interacting intelligent agents. It can also be called a multi-agent system (MAS) or agent-based system (ABS).
What is multi-agent simulation?
In multi-agent simulation systems the MAS is used as a model to simulate some real-world domain. Typical use is in domains involving many different components, interacting in diverse and complex ways and where the system-level properties are not readily inferred from the properties of the components.
What is multi-agent in artificial intelligence?
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.
Where are multi agent systems used?
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.
What is multi-agent reinforcement learning?
Multi-agent reinforcement learning studies how multiple agents interact in a common environment. That is, when these agents interact with the environment and one another, can we observe them collaborate, coordinate, compete, or collectively learn to accomplish a particular task.
Where are multi-agent systems used?
What are multi-agent systems used for?
As reported in [50], the main application domains of multi-agent systems are ambient intelligence, grid computing, electronic business, the semantic web, bioinformatics and computational biology, monitoring and control, resource management, education, space, military and manufacturing applications, and so on.
Why is communication required in multi-agent systems?
Agents in multiagent systems are concurrent autonomous entities that need to coordinate and to cooperate so as to perform their tasks; these coordination and cooperation tasks might be achieved through communication.
What is Nash Q-learning?
A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning.
How does multi-agent reinforcement learning work?
Multi-agent reinforcement learning is the study of numerous artificial intelligence agents cohabitating in an environment, often collaborating toward some end goal. When focusing on collaboration, it derives inspiration from other social structures in the animal kingdom. It also draws heavily on game theory.
