Transcript Knowledge
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] KNOWLEDGE TYPES AND REPRESENTATION Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design E-mail: [email protected] What Is Knowledge? • Knowledge is an abstract term that attempts to capture an individual’s understanding of a given subject. • In the world of intelligent systems the domain-specific knowledge is captured. • Domain is a well-focused subject area. Types of Knowledge Declarative Concepts Describes what is known knowledge Facts about a problem. This includes Objects simple statements that are asserted to be either true or false. This also includes a list of statements that more fully describes some object or concept (object-attribute-value triplet). Types of Knowledge Procedural Rules knowledge Strategies Agendas Procedures Describes how a problem is solved. This type of knowledge provides direction on how to do something. Types of Knowledge Heuristic Rules of Describes a rule-of-thumb that guides the reasoning Thumb Knowledge process. Heuristic knowledge is often called shallow knowledge. It is empirical and represents the knowledge compiled by an expert through the experience of solving past problems. Types of Knowledge Knowledge MetaKnowledge about the other types of knowledge and how to use them Describes knowledge about knowledge. This type of knowledge is used to pick other knowledge that is best suited for solving a problem. Experts use this type of knowledge to enhance the efficiency of problem solving by directing their reasoning in the most promising area. Types of Knowledge Structural Rule sets Knowledge Concept relationships Concept to object relationships Describes knowledge structures. This type of knowledge describes an expert’s overall mental model of the problem. The expert’s mental model of concepts, sub-concepts, and objects is typical of this type of knowledge. Knowledge Based Systems • The central component of a knowledge-based system is its knowledge base • Informally, a knowledge base is a set of representations of facts about the world • Each individual representation is called a sentence • The sentences are expressed in a language called a knowledge representation language Knowledge Representation • In general, a representation is a set of conventions about how to describe a class of things. • A description makes use of the conventions of a representation to describe some particular thing. • The function of any representation scheme is to capture essential features of a problem domain and make that information available to a problem solving procedure. Knowledge Representation • Knowledge representation is the method used to encode knowledge in an intelligent system’s knowledge base. • The object of knowledge representation is to express knowledge in computer-tractable form, such that it can be used to help intelligent system perform well. Knowledge Representation It is obvious that a representation language must allow the programmer to express the knowledge needed for a problem solution. Knowledge representation languages should provide a natural framework for expressing problem-solving knowledge. Such framework should make that knowledge available to the computer and assist the programmer in its organization. Knowledge Representation A knowledge representation language is defined by two aspects: • The syntax of a language describes the possible configurations that can constitute sentences. • The semantics determines the facts in the world which the sentences refer. Knowledge Representation Without semantics, a sentence is, for instance, just a collection of marks on a page. With semantics, each sentence makes a claim about the world. Knowledge Representation For example, the syntax of the language of arithmetic expressions says that if x and y are expressions denoting numbers, then x < y is a sentence about numbers. The semantics of the language says that x < y is false when x is an equal or a bigger number than y, and true otherwise. Knowledge Representation • Cognitive psychologists have formed a number of theories to explain how humans solve problems This work uncovered the types of knowledge humans commonly use, how they mentally organize this knowledge, and how they use it efficiently to solve a problem. • Researchers in artificial intelligence have used the results of these studies to develop techniques to best represent different knowledge types in the computer. • Intelligent systems during problem solving must process knowledge. Knowledge Representation • To process knowledge, the knowledge is represented in some symbolic form that can be manipulated by an intelligent system. • Just as there is no single theory to explain human knowledge organization or a best technique for structuring data, no single knowledge representation structure is ideal. • One of more important responsibilities of knowledge engineer is to choose the knowledge representation technique best suited for the given application. Knowledge Representation A representation consists of four fundamental parts: • A lexical part that determines which symbols are allowed in the representation’s vocabulary. • A structural part that describes constraints on how the symbols can be arranged. Knowledge Representation • A procedural part that specifies access procedures that enable to create descriptions, to modify them, and to answer questions using them. • A semantic part that establishes a way of associating meaning with the description. The Quality of Representation Good representations are the key to good problem solving: • Good representations make the important objects and relations explicit: it is possible to see what is going on at a glance. • They expose natural constraints: it is possible to express the way one object or relation influences another. The Quality of Representation • They bring objects and relations together: it is possible to see all needed at one time. • They suppress irrelevant details: it is possible to keep rarely used details out of sight, but still get to them when necessary. The Quality of Representation • They are transparent: it is possible to understand what is being said. • They are complete: it is possible to say all that needs to be said. • They are concise: it is possible to say what is needed to say efficiently. The Quality of Representation • They are fast: it is possible to store and retrieve information rapidly. • They are computable: it is possible to create them with an existing procedure.