Learning in multiagent systems

By Sandip Sen, Gerhard Weiss. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “At the end of this startup phase the first general understanding of the role of learning in multiagent settings emerged, for example, Stone and Veloso (2000) and the first textbooklike treat-ment of MAL became available (Sen and Weiss 1999).”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, background): “In addition to the above surveys, there are also a number of surveys on the topic of multiagent learning (Hernandez-Leal et al., 2017; Bloembergen et al., 2015; Tuyls and Weiss, 2012; Busoniu et al., 2008; Panait and Luke, 2005; Alonso et al., 2001; Stone and Veloso, 2000; Sen and Weiss, 1999).”
Multi-agent Reinforcement Learning: An Overview (2010, background): “Although the agents in a multi-agent system can be endowed with behaviors designed in advance, they often need to learn new behaviors online, such that the performance of the agent or of the whole multi-agent system gradually improves [106,115].”
An Overview of Cooperative and Competitive Multiagent Learning (2005, unknown): “stochastic (general-sum) games [188, 147].”
A Comprehensive Survey of Multiagent Reinforcement Learning (2008, unknown): “A reinforcement learning (RL) agent learns by trial-and-error interaction with its dynamic environment [6]–[8].”
Cooperative Multi-Agent Learning: The State of the Art (2005, background): “There are also several PhD theses investigating different aspects of the multi-agent learning field: [76, 142, 64, 52, 177, 44, 82, 158, 11, 223, 26, 257] Good pointers for news, conferences and journals, courses, people, companies, and laboratories may be found on the web sites http://www.”
Multi-objective multi-agent decision making: a utility-based analysis and survey (2019, background): “Building an intelligent distributed system is a notoriously difficult problem as it involves dealing with non-stationarity, limited resource sharing, and often requires coordination or overcoming conflicting goals [148].”
New Criteria and a New Algorithm for Learning in Multi-Agent Systems (2004, background): “There is a growing body of algorithm s proposed, and some arguments about their relative merits and domains of applicabil ity (for example, [14] and [17]).”