Guest Editorial about Multiagent Learning
نویسندگان
چکیده
This special issue is on multiagent learning, that is, on learning that relies on or even requires the interaction among several intelligent agents. An agent is commonly understood as a computational or natural entity that can be viewed as perceiving in and acting upon its environment, as being autonomous in that its behavior is at least partially determined by its own experience, and as pursuing goals or carrying out tasks (see, e.g., (Huhns & Singh, 1998) for a contemporary collection of articles on agents and multiagent systems). Multiagent learning emerged as a topic of active research in the late 1980s and early 1990s, and since then has attracted steadily increasing attention in both the multiagent systems and distributed arti-cial intelligence community (e. and the machine learning community. This attention can be attributed to two primary insights: 1. There is a strong need for learning techniques and methods in the area of multiagent systems. These systems show several characteristics that make it particularly diicult to specify them correctly and completely: for instance, there is no global system control, each agent usually has just incomplete information , the information owned by diierent agents can be contradictory, and typically the agents are intended to operate in complex|open, large, dynamic, and unpredictable|environments. Because of these characteristics, it is obviously desirable that the agents themselves are capable of improving their own behavior, in addition to the overall system's behavior. 2. The machine learning area can proot from an extended view capturing both single-agent and multiagent learning. It is one of the primary concerns of this area to understand the principles and mechanisms of learning, whether it occurs
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تاریخ انتشار 2007