Multi-Agent Learning (MAL) Task

Context:
- It can be solved by a Multi-Agent Learning System that implements a Multi-Agent Learning Algorithms.
- …
Counter-Example(s):
- Single-Agent Learning Task.
See: Multi-Agent Game, Reinforcement Learning.

References

(Sayed, 2014) ⇒ Ali Sayed. (2014). “Adaptation, Learning, and Optimization over Networks.” In: Foundations and Trends® in Machine Learning Journal, 7(4-5). doi:10.1561/2200000051
- QUOTE: This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. ...

(Shoham & Powers, 2017) ⇒ Yoav Shoham, and Rob Powers (2017). “Multi-Agent Learning Algorithms”. In: (Sammut & Webb, 2017)
- QUOTE: Multi-agent learning (MAL) refers to settings in which multiple agents learn simultaneously. Usually defined in a game theoretic setting, specifically in repeated games or stochastic games, the key feature that distinguishes MAL from single-agent learning is that in the former the learning of one agent impacts the learning of others. As a result, neither the problem definition for multi-agent learning nor the algorithms offered follow in a straightforward way from the single-agent case. In this second of two entries on the subject, we focus on algorithms.

(Shoham & Powers, 2010) ⇒ Yoav Shoham, and Rob Powers. (2010). "Multi-Agent Learning I: Problem Definition".
- QUOTE: Multi-agent learning refers to settings in which multiple agents learn simultaneously. Usually defined in a game theoretic setting, specifically in repeated games or stochastic games, the key feature that distinguishes multi-agent learning from single-agent learning is that in the former the learning of one agent impacts the learning of others. As a result neither the problem definition for mutli-agent learning, nor the algorithms offered, follow in a straightforward way from the single-agent case. In this second of two entries on the subject we focus on algorithms.

http://www.cs.rutgers.edu/~mlittman/topics/nips02/
- QUOTE: More and more, machine learning is being explored as a vital component to address challenges in multi-agent systems. For example, many application domains are envisioned in which teams of software agents or robots learn to cooperate amongst each other and with human beings to achieve global objectives. Learning may also be essential in many non-cooperative domains such as economics and finance, where classical game-theoretic solutions are either infeasible or inappropriate.
  At the same time, multi-agent learning poses significant theoretical challenges, particularly in understanding how agents can learn and adapt in the presence of other agents that are simultaneously learning and adapting. This is a fertile area of research that seems ripe for progress: the numerous and significant theoretical developments of the 1990s, in fields such as Bayesian, game-theoretic, decision-theoretic, and evolutionary learning, can now be extended to more challenging multi-agent scenarios.
  This workshop on theory and practice in multi-agent learning is intended to be broad in scope and informal in style.