Learning Agents
Types of Agents
Before reading this article you need to learn about "what are the Types of Agents ?" and about simple Reflex Agent, Model-based reflex Agent, Goal-Based Agent and Utility-Based Agent. To read those topics just click on it.
Learning Agents :
We have described agent programs with various methods for selecting actions. We have not, so far, explained how the agent programs come into being. In his famous early paper, Turing (1950) considers the idea of actually programming his intelligent machines by hand. He estimates how much work this might take and concludes, “Some more expeditious method seems desirable.” The method he proposes is to build learning machines and then to teach them. In many areas of AI, this is now the preferred method for creating state-of-the-art systems. Any type of agent (model-based, goal-based, utility-based, etc.) can be built as a learning agent (or not).
Learning has another advantage, as we noted earlier: it allows the agent to operate in initially unknown environments and to become more competent than its initial knowledge alone might allow. In this section, we briefly introduce the main ideas of learning agents. Throughout the book, we comment on opportunities and methods for learning in particular kinds of agents. In future topics we'll go much more depth into the learning algorithms themselves.
- A learning agent in AI is the type of agent which can learn from its past experiences, or it has learning capabilities.
- It starts to act with basic knowledge and then able to act and adapt automatically through learning.
- A learning agent has mainly four conceptual components, which are :
- Learning element: It is responsible for making improvements by learning from environment.
- Critic: Learning element takes feedback from critic which describes that how well the agent is doing with respect to a fixed performance standard.
- Performance element: It is responsible for selecting external action.
- Problem generator: This component is responsible for suggesting actions that will lead to new and informative experiences.
- Hence, learning agents are able to learn, analyze performance, and look for new ways to improve the performance.
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