Knowledge Engineering

Sources of Knowledge - Books - Journals - Manuals - Reports - Films - Databases - Pictures - Audio and Video Tapes - Flow Diagram - Observed Behavior - Knowledge stored in expert’s mind

Knowledge Acquisition Issues - The basic concepts and terms - The inputs to the problem, which of them are difficult? - The interrelationships between data items - The degree of certainty of the data (inputs, outputs) - Typical solutions to the problem, what are their characteristics? - Sources of knowledge - Methods of knowledge acquisition - The underlying assumptions, and constraints - The conflicts, and exception rules

Methods of Knowledge Acquisition - Interview - Observation of Experts - Questionnaires - Analysis of Documented Knowledge - Computer-Aided Elicitation

Methods of Knowledge Representation - Propositional Logic - Rules - Frames - Semantic Networks

Propositional Logic Knowledge is presented in propositions that make logical inference possible. Example: Proposition 1 - If we increase marketing budget, sales will increase. Proposition 2 - We have increased marketing budget. Inference - Sales will increase.

Production Rules Knowledge is presented as rules, called productions, in the following form: IF (condition, or premise) THEN (result, action, or consequent) Example: IF a person’s right hand is larger than his left hand THEN his job type is/has been manual labor.

Frames A frame is a description of an object that contains slots for all the information associated with the object. Frames are arranged in hierarchies.

Example. (Frame) Human (Slot) Weight Default 150 (Slot) # Legs Value2 (Frame) Man (Frame) AKO Value Human (Slot) Sex Default Value Male (Slot) Age 35

More on Example. (Frame) Man (Frame) AKO ValueHuman (Slot) Sex ValueMale (Frame) Bob (Frame) AKO ValueMan (Slot) Height If Needed (Weight/Age) (Slot) Age Default35

Semantic Networks A semantic network is a collection of objects called nodes. The nodes are connected together by arcs or links. Nodes describe facts like physical objects, concepts or situations, where arcs define the relevant relationships among objects.

Fragments of a Computer Semantic Network MICROCOMPUTER is-a COMPUTER DISKDRIVE is-part MICROCOMPUTER IBM PC is-a MICROCOMPUTER IBM PC color BEIGE IBM PC is-attached HP LASER JET IBM PC owner JOHN HP LASER JET is-a PRINTER JOHN is-a PERSON

Semantic Network COMPUTER is-a is-part PERSON MICROCOMPUTER DISK is-a is-a DRIVE JOHN IBM PC HP LASER JET owner is-attached color is-a BEIGE PRINTER

Advantages of Different Representations Production Rules Simple syntax, easy to understand, easy to add or modify Semantic Networks Easy to follow hierarchy, easy to trace association, flexible Frames Expressive power, easy to set up slots for new properties, easy to include default information and detect missing values Formal Logic Facts asserted independently of use, guarantee that all and only valid consequences are asserted

Disadvantages of Different Representations Production Rules Hard to follow hierarchies, inefficient for large systems, not all knowledge can be represented as rules Semantic Networks Meaning attached to nodes might be ambiguous, exception handling is hard Frames Difficult to program, difficult for inference, lack of inexpensive software Formal Logic Inefficient with large data sets, very slow with large knowledge base

Inference Control Strategies Backward Chaining - This method, also called goal directed reasoning, entails having a goal or a hypothesis as a starting point and then “working backward” along some paths to see if the conclusion is correct.

Inference Control Strategies Forward Chaining - Is used for problem solving when data or basic ideas are a starting point. Under this method, the system does not start with any particular goals defined for it. It works from facts to conclusions. It reasons forward starting with data supplied by the user and applies all rules whose “if” parts are satisfied. Forward chaining often is described as data-driven or event-driven reasoning.