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Contents. Knowledge Acquisition Machine Learning. Knowledge Acquisition. The transfer and transformation of potential problem solving expertise from some knowledge source to a program. Buchanan, 1983. Knowledge Acquisition.
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Contents • Knowledge Acquisition • Machine Learning
Knowledge Acquisition • The transfer and transformation of potential problem solving expertise from some knowledge source to a program. Buchanan, 1983
Knowledge Acquisition • The process of acquiring, studying and organizing knowledge, so that it can be used in a knowledge-based system. • Expert may provide irrelevant, incomplete or inconsistent information. • Data and knowledge acquisition • Collect and analyze data and knowledge • Make key concepts of the system design more explicit
Knowledge Acquisition (Cont.) • Acquired knowledge may consist facts, rules, concept, procedures, heuristics, formulas, relationships, statistics, or other useful information • Sources • Documented • Written, viewed, sensory, behavior • Undocumented • Memory • Acquired from • Human senses • Machines
Knowledge Engineers • Professionals who elicit knowledge from experts • Empathetic, patient • Broad range of understanding, capabilities • Integrate knowledge from various sources • Creates and edits code • Operates tools • Build knowledge base • Validates information • Trains users
Machine Learning • Machine learning is a specialized form of autonomous knowledge acquisition. • Autonomous knowledge creation or refinement through the use of computer programs.
Why is Machine Learning Important? • Some tasks cannot be defined well, except by examples (e.g., recognizing people). • Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships. • Human designers often produce machines that do not work as well as desired in the environments in which they are used.
Why is Machine Learning Important? • The amount of knowledge available about certain tasks might be too large for explicit encoding by humans (e.g., medical diagnostic). • Environments change over time. • New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems “by hand”.
Types of Learning • Learning by memorization • Direct instruction • Analogy • Induction • Deduction
Learning by Memorization • It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base.
Learning by Direct Instruction • The knowledge must be transformed into an operational form before being integrated into the knowledge base • This type of learning used when a teacher presents a number of facts directly to us in a well organized manner.
Analogical Learning • Is the process of learning a new concept or solution through the use of similar known concepts or solutions. • Here, previously learn examples serve as a guide. • Driving a truck using experience of driving a car.
Learning by Induction • This form of learning requires the use of inductive inference • We use inductive learning when we formulate a general concept after seeing a number of instances or examples of the concept. Example: we learn the concepts of color or sweet taste after experiencing the sensations associated with several examples of color example objects or sweet foods
Deductive Learning • It is accomplished through a sequence of deductive inference steps using known facts. • From the known facts, new facts and relationships are logically derived. • Example:(father X of Y), (father Y of Z); (Grandfather X of Z)
