2010
DOI: 10.1007/978-3-642-14435-6_7
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Abstract: Abstract. Multi-agent systems can be used to address problems in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This chapter reviews a representative selection of mult… Show more

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Cited by 401 publications
(197 citation statements)
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“…At the same time, norms in the form of constraints are essential for artificial intelligence systems, especially, in relation to super intelligence. This makes the following problem especially important [19,36,37]. The third cluster of problems is related to concepts of agents and oracles, which are connected to the concept of actors.…”
Section: Resultsmentioning
confidence: 99%
“…At the same time, norms in the form of constraints are essential for artificial intelligence systems, especially, in relation to super intelligence. This makes the following problem especially important [19,36,37]. The third cluster of problems is related to concepts of agents and oracles, which are connected to the concept of actors.…”
Section: Resultsmentioning
confidence: 99%
“…Veelen and Spreij [2008] investigate these properties and show the most important relationships. Further examples of learning approaches with multiple agents can be found in Barrett et al [2014], Panait and Luke [2005], and Buşoniu et al [2010].…”
Section: Related Workmentioning
confidence: 98%
“…Learning to act in multi-agent systems offers additional challenges (see the following surveys: Shoham and LeytonBrown 2009, Chap. 7;Weiß and Sen 1996;Buşoniu et al 2010). We provide here an overview of a general idea of learning for single and multi-agent systems:…”
Section: Learning In Stochastic Gamementioning
confidence: 99%