Multi Agent Learning
Distributed artificial intelligence (DAI) systems solve problems using multiple, cooperative agents. In these systems, control and information are often distributed among the agents. This reduces the complexity of each agent and allows agents to work in parallel and increases problem solving speed. Also each agent has resource limitations which could limit the ability of a single agent system to solve large, complex problems. Allowing multiple agents to work on these types of problems may be the only way to realistically solve them. In general, multiple agent learning involves improving the performance of the group of agents as a whole or increasing the domain knowledge of the group. It also includes increasing communication knowledge. An increase in communication knowledge can lead to an increase in performance by allowing the agents to communicate in a more efficient manner. In the context of improving the performance of a group of agents, allowing individual agents to improve their performance may not be enough to improve the performance of the group. To apply learning to the overall group performance, the agents need to adapt and learn to work with the each other. The agents may not need to learn more about the domain, as in the traditional sense of machine learning, to improve group performance. In fact to improve the performance of the group, the agents may only need to learn to work together and not necessarily improve their individual performance. In addition, not all the agents must be able to learn or adapt to allow the group to improve.
Learning and adapting to work with other agents involves adjusting the control of each agent’s problem solving plan. Different tasks may have to be solved in a specific sequence. If the tasks are assigned to separate agents, the agents must work together to solve the tasks. Learning which agents are typically assigned different types of tasks will allow each agent to select other agents to work with on different tasks. Teams can be formed based on the type of task to be solved. Some of the issues involved are the type, immediacy and importance of task, as well as each agent’s task solving ability, capability, reliability and past task assignments. Each team member’s plan would be adjusted according to the other agent’s plans.
Learning what type of information and knowledge each agent possesses allows for an increase in performance by specifying the long term responsibilities of each agent. By assigning different agents different responsibilities, the group of agents can improve group performance by providing a global strategy. Organizing the responsibilities reduces the working complexity of each agent.
Learning what type of information, knowledge reliability and capability each agent possesses allows for an increase in performance by allowing improved communication. Directly addressing the best agent for needed information or knowledge allows for more efficient communication among he agents.
Group Observation and Discovery Learning
Individual agents incorporate different information and knowledge. Combining this differing information and knowledge may assist in the process of learning new class descriptions or concepts that could not have been learned by the agents separately. This type of learning is more effective than the others. The observation towards the procedure will be focused by a group of agents. When a group of different visions will reach, at that point of view a newly interactive procedure will be found out; which is the discovery of all the agents.
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