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Chapter: Artificial Intelligence

Learning - Artificial Intelligence

Learning process is the basis of knowledge acquisition process. Knowledge acquisition is the expanding the capabilities of a system or improving its performance at some specified task.



Learning process is the basis of knowledge acquisition process. Knowledge acquisition is the expanding the capabilities of a system or improving its performance at some specified task. So we can say knowledge acquisition is the goal oriented creation and refinement of knowledge. The acquired knowledge may consist of various facts, rules, concepts, procedures, heuristics, formulas, relationships or any other useful information. Knowledge can be acquired from various sources like, domain of interests, text books, technical papers, databases, reports. The terms of increasing levels of abstraction, knowledge includes data, information and Meta knowledge. Meta knowledge includes the ability to evaluate the knowledge available, the additional knowledge required and the systematic implied by the present rules.


Learning involves generalization from experience. Computer system is said to have learning if it is able to not only do the “repetition of same task” more effe ctively, but also the similar tasks of the related domain. Learning is possible due to some factors like the skill refinement and knowledge acquisition. Skill refinement refers to the situation of improving the skill by performing the same task again and again. If machines are able to improve their skills with the handling of task, they can be said having skill of learning. On the other hand, as we are able to remember the experience or gain some knowledge by handling the task, so we can improve our skill. We would like our learning algorithms to be efficient in three respects:


(1) Computational: Number of computations during training and during recognition.


(2) Statistical: Number of examples required for good generalization, especially labeled data.


Human Involvement: Specify the prior knowledge built into the model before training. A similar machine learning architecture is given in figure .



Design of learning element is dictated by the followings.


(1) What type of performance element is used?


(2) Which functional component is to be learned?


(3) How that functional component is represented?


(4) What kind of feedback is available?


(5) How can be compared between the existing feedbacks with the new data?


(6) What are the levels of comparisons? Etc.


Any system designed to create new knowledge and thereby improve its performance must include a set of data structures that represents the system’s present level of expertise and a task algorithm that uses the rules to guide the system’s problem solving activity. The architecture of a general learning procedure is given in figure .

(NIL )

 Figure Architecture of Machine Learning

Hence the inputs may be any types of inputs, those are executed for solution of a problem. Those inputs are processed to get the corresponding results. The learning element learns some sort of knowledges by the knowledge acquisition techniques. The acquired knowledge may be required for a same problem in future, for which that problem can be easily solved.


Every learning model must contain implicit or explicit restrictions on the class of functions that can learn. Among the set of all possible functions, we are particularly interested in a subset that contains all the tasks involved in intelligent behaviour. Examples of such tasks include visual perception, auditory perception, planning, control etc. The set does not just include specific visual perception tasks, but the set of all the tasks that an intelligent agent should be able to learn. Although we may like to think that the human brain is some what general purpose, it is extremely restricted in its ability to learn high dimensional functions.

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