Multi-Agent Learning (MAL) Task

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A Multi-Agent Learning (MAL) Task is an agent learning task that is a joint learning task.



References

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2002

  • 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.