KNOWLEDGE REPRESENTATION n n n To solve problems

KNOWLEDGE REPRESENTATION n n n To solve problems in artificial intelligence requires large amount of knowledge and some mechanism that knowledge to create solutions to new problem. A variety of knowledge facts have been exploited in AI programs. Hence we are dealing with two different kinds of entities such as: Facts: - It is truth in some relevant world. These are things we want to represent. Representations of facts in some chosen formalism: - These are things we will actually be able to manipulate.

Knowledge n n n Knowledge is the body of facts and principles. Knowledge can be language, concepts, procedures, rules, ideas, abstractions, places, customs, and so on. study of knowledge is called Epistemology. Types of knowledge The types of knowledge include procedural knowledge, declarative knowledge and heuristic knowledge. Procedural knowledge is compiled or processed form of information. Procedural knowledge is related to the performance of some task. For example, sequence of steps to solve a problem is procedural knowledge.

Knowledge(cont. . ) n n n Declarative knowledge is passive knowledge in the form of statements of facts about the world. For example, mark statement of a student is declarative knowledge. Heuristic knowledge Heuristics knowledge are rules of thumb or tricks. Heuristic knowledge is used to make judgments and also to simplify solution of problems. It is acquired through experience. An expert uses his knowledge that he has gathered due to his experience and learning. Importance of knowledge Intelligence requires knowledge. That is, to exhibit intelligence, knowledge is required. Knowledge plays a major role in building intelligent systems.

Structured Knowledge Representations n Modeling-based representations reflect the structure of the domain, and then reason based on the model. n n Semantic Nets Frames Scripts Sometimes called associative networks

Presentation Knowledge So that one way to think o structuring these entities is as two levels: The knowledge level: - In this facts are described. The symbol level: - In this we can give representations of object at the knowledge level are defined in terms of symbol that can be manipulated by programs.

Presentation Knowledge(cont. . ) n n n Above figure shows the mapping between facts and representations. Consider the English sentence: Spot is a dog. The fact represented by that English sentence can be represented in logic as: dog (spot) We also have logical representations of fact that: All dogs have tails V x: dog (x) has tail (x) Hence we can deduce this representation: has tail (spot) So that using backward mapping we can represent or generate the English sentence: Spot has a tail

APPROACHES TO KNOWLEDGE REPRESENTATIONS n n n A good system for the representations of knowledge in a particular domain should possess the following four properties: 1) Representational adequacy: The ability to represent all of the kinds of knowledge that are needed in that domain 2) Inferential adequacy: Ability to manipulate the representational structure in such a way as to drive new structure corresponding to new knowledge inferred from old. 3) Inferential efficiency: The ability to incorporate into the knowledge structure additional information that can be use to focus the attention of the inference mechanism in the most promising direction. 4) Acquisitional efficiency: The ability to acquire new information easily. The simplest case involves direct insertion by the person of new knowledge into the database. Ideally the program itself would be able to control knowledge acquisition.

INHERITABLE KNOWLEDGE n In order to inherit the knowledge and to support properties inheritance object must be organize into classes and classes must be arranged in a generalization hierarchy. For solving the problem most of the attributes and the entire object have no general significance. Hence two-exception arc is the attribute, which is being used to show class inclusion, and attribute instant – which is being used to show class membership. These two specific attributes provide the basic for properties inheritance as an inference technique. Using this technique the knowledge based can support retrieval both of facts that have been explicitly stored and a of facts that can be derived from those that are explicitly stored.

INHERITABLE KNOWLEDGE(cont. . ) n n n The inheritance algorithm can be stated as; to retrieve a value V for attribute A of an object O: 1) Find O in the knowledge base 2) If there is a value therefore the attribute A then report that value 3) Otherwise C if there is a value for attribute instance. If not the node, corresponding that value and a look for a value for the attribute A. If not found reported. 4) Otherwise there is no value for the attribute until an answer is found.

INHERITABLE KNOWLEDGE(cont. . )

INFERENTIAL KNOWLEDGE n Properly inheritance is a powerful form of inference but it is not the only useful form. The required inference procedure is one that implements the standard logical rules of inference. The most commonly used of inference procedure is resolution, which exploits a proof by contradiction. The inference procedure is the technique, which can drive the new structure from old.

PROCEDURAL KNOWLEDGE n The procedural knowledge technique gives the complete satisfaction what data is needed and how to get it. Hence procedural knowledge specifies what to do when. Procedural knowledge represented in program in many ways. The most common way is simply code for doing something. The machine uses the knowledge when it executes the code to perform the task. Another most commonly used technique for representing procedure knowledge in AI program is the use of production rules. The important strategy of procedural knowledge is that how to use and implement the procedural rules.