The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Learning Systems Engineering
Abstract
Traditionally multi-agent learning is considered as the intersection of two subfields of artificial intelligence: multi-agent systems and machine learning. Conventional machine learning involves a single agent that is trying to maximise some utility function without any awareness of existence of other agents in the environment (Mitchell, 1997). Meanwhile, multi-agent systems consider mechanisms for the interaction of autonomous agents. Learning system is defined as a system where an agent learns to interact with other agents (e.g., Clouse, 1996; Crites & Barto, 1998; Parsons, Wooldridge & Amgoud, 2003). There are two problems that agents need to overcome in order to interact with each other to reach their individual or shared goals: since agents can be available/unavailable (i.e., they might appear and/or disappear at any time), they must be able to find each other, and they must be able to interact (Jennings, Sycara & Wooldridge, 1998).
Related Content
|
Christine Kosmopoulos.
© 2022.
22 pages.
|
|
Melkamu Beyene, Solomon Mekonnen Tekle, Daniel Gelaw Alemneh.
© 2022.
21 pages.
|
|
Rajkumari Sofia Devi, Ch. Ibohal Singh.
© 2022.
21 pages.
|
|
Ida Fajar Priyanto.
© 2022.
16 pages.
|
|
Murtala Ismail Adakawa.
© 2022.
27 pages.
|
|
Shimelis Getu Assefa.
© 2022.
17 pages.
|
|
Angela Y. Ford, Daniel Gelaw Alemneh.
© 2022.
22 pages.
|
|
|