1. Agent Learning
Why learning in agent ?
One central element of intelligent behavior is the ability to learn from experience. (There is no way that we can know a priori all of the situations that our intelligent agent will encounter. Learn from experience makes agents get better at tasks and elevate it to a higher level of ability, So It can learn which agents to trust and cooperate with, and which ones to avoid.
A learning agent can improve its performance based on prior experience.
There are three types of learning:-
1. Supervised learning
• The most common form of learning.
• The learning agent is trained by showing it examples of the problem state or attributes along with the desired output or action.
• When make a prediction & the output differs from the desired output, then the learning agent is adapted to produce the correct output.
• Examples are back propagation NN, and a decision tree.
2. Unsupervised Learning
• Used when the learning agent needs to recognize similarities between inputs or to identify features in the input data.
• The data is presented to the agent, and it adapts so that it partitions the data into groups.
3. Reinforcement Learning
• Reinforcement learning is as a middle stage between supervised learning and unsupervised learning.
• It is a special case of supervised learning where the exact desired output is unknown.
• It is based only on the information of whether or not the actual output is correct.
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .
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