Multi-Agent Learning (MAL) System
A Multi-Agent Learning (MAL) System is a Multi-Agent System that can solve a Multi-Agent Learning Task by implementing a Multi-Agent Learning Algorithm.
- Context:
- It can range from being a Simple Multi-Agent Learning System to being a Multi-Agent Reinforment Learning System.
- It can range from being a Cooperative Multi-Agent Learning System to being a Competitive Multi-Agent Learning System.
- Example(s):
- Counter-Example(s):
- See: Multi-Agent Learning, Learning Rate, ABM System, Nash Equilibrium.
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.