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Goals - artificial intelligence:
One desirable way to make perfect an agent's performance is to enable it to have some details of what it is trying to complete. If it is given some explanation of the goal (e.g., some information relates the solution to a problem it is trying to solve), then it can consign to that information to see if a particular action will lead to such goal. These agents are mostly called goal-based. Two tried and trusted methods for goal-based agents are planning (where the agent puts together and executes a plan for achieving its goal) and find (where the agent looks ahead in a look for space until it finds the goal). Planning and research issues are covered later in this course.
In RHINO, there were two goals: get the robot to an exhibit chosen by the guests and, when it gets there, provide information about the display Obviously, RHINO used information about its goal of getting to an exhibit to plan its route to that exhibit.
Multi-Layer Artificial Neural Networks : However we can now look at more sophisticated ANNs that are known as multi-layer artificial neural networks it means they have hidden
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