GB/T 5271.28-2001 Information technology vocabulary Part 28: Basic concepts of artificial intelligence and expert systems

This standard is formulated to facilitate international communication in the field of artificial intelligence. This standard provides the terms and definitions of concepts related to the field of information processing and clarifies the relationship between these items. This standard defines concepts related to artificial intelligence and expert systems. GB/T 5271.28-2001 Information Technology Vocabulary Part 28: Basic Concepts of Artificial Intelligence and Expert Systems GB/T5271.28-2001 Standard download decompression password: www.bzxz.net

GB/T 5271.28-2001
This standard is equivalent to the international standard ISO/IEC2382-28:1995 "Information Technology Vocabulary Part 28: Basic Concepts of Artificial Intelligence and Expert Systems".
The purpose of formulating information technology vocabulary standards is to facilitate international communication in information processing. It gives the terms and definitions of concepts related to the field of information processing, and clarifies the relationship between each terminology. This standard defines concepts related to artificial intelligence and expert systems.
Vocabulary". This standard is
GB/T5271 series of standards, which consists of more than 30 parts, all under the general title "Information Technology GB/T5271 series of standards Part 28. This standard is proposed by the Ministry of Information Industry of the People's Republic of China. This standard is under the jurisdiction of the China Electronics Technology Standardization Institute. Drafting unit of this standard: China Electronics Technology Standardization Institute. The main drafters of this standard: Chen Ying, Zheng Hongren. 372
GB/T5271.28-2001
ISO/IEC Foreword
ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission) are specialized organizations for standardization worldwide. National member bodies (which are members of ISO or IEC) participate in the development of international standards for specific technical areas through technical committees established by the international organizations. ISO and IEC technical committees cooperate in areas of common interest. Other official and non-official international organizations in contact with ISO and IEC may also participate in the development of international standards. For information technology, ISO and IEC have established a joint technical committee, ISO/IECJTC1. Draft international standards proposed by the joint technical committee are circulated to national member bodies for voting. To publish an international standard, at least 75% of the national member bodies participating in the voting must vote in favor.
International Standard ISO/IEC2382-28 was developed by the SC1 Vocabulary Subcommittee of the ISO/IECJTC1 Joint Technical Committee on Information Technology. ISO/IEC2382 consists of more than 30 parts, all under the general title "Information Technology Vocabulary". 373
1 Overview
1.1 Scope
National Standard of the People's Republic of China
Information technology-Vocabulary-Part 28:Artificial intelligence—Basic conceptsand expert system
GB/T5271.28—2001
eqv ISO/1EC 2382-28:1995
This standard is formulated to facilitate international communication in the field of artificial intelligence. This standard gives the terms and definitions of concepts related to the field of information processing and clarifies the relationship between these items. This standard defines concepts related to artificial intelligence and expert systems. 1.2 Referenced standards
The provisions contained in the following standards constitute the provisions of this standard through reference in this standard. When this standard was published, the versions shown were valid. All standards will be revised. Parties using this standard should explore the possibility of using the latest versions of the following standards: GB/T5271.1-2000 Information technology vocabulary Part 1: Basic terms (eqvISO/IEC2382-1: 1993) GB/T5271.12-2000 Information technology vocabulary Part 12: Peripheral equipment (eqvISO/IEC2382-12: 1988) GB/T15237.1-2000 Vocabulary of terminology Part 1: Theory and application (eqvISO1087-1: 2000) 1.3 Principles and rules to follow
1.3.1 Definition of entries
Chapter 2 includes many entries. Each entry consists of several required elements, including an index number, a term or several synonyms, and a phrase that defines a concept. In addition, an entry may include examples, notes, or explanations to facilitate understanding of the concept. Sometimes the same term can be defined by different entries, or an entry can include two or more concepts, as explained in 1.3.5 and 1.3.8 respectively.
This standard uses other terms, such as vocabulary, concepts, terms and definitions, whose meanings are defined in GB/T15237.1. 1.3.2 Composition of entries
Each entry includes the necessary elements specified in 1.3.1, and some additional elements may be added if necessary. The entry includes the following elements in the following order:
a) Index number:
b) The concept of the term in a language, if there is no preferred term, is represented by a five-point symbol (.··.); in a term, a row of dots is used to indicate a word selected in each specific case; c) Preferred term (indicated according to the rules of GB/T2659); d) Abbreviation of the term;
) Permitted synonymous terms:
1) The text of the definition (see 1.3.4);
Approved by the General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China on July 16, 2001 37.1
Implementation on March 1, 2002
GB/T 5271.28--2001
g) one or more examples beginning with "Example"; h) one or more notes beginning with "Note" indicating special cases; i) pictures, diagrams or tables common to the terms. 1.3.3 Classification of terms
Each part of this series of standards is assigned a two-digit serial number, starting with 01, which indicates "basic terms". The terms are classified into groups, each of which is assigned a four-digit serial number, the first two digits indicating the part of the standard in which the group is located.
Each term is assigned a six-digit index number; the first four digits indicate the standard part and group in which the term is located. 1.3.4 Choice of terms and definitions
The terms and definitions are selected in accordance with established usage as much as possible. In case of conflict, the majority rule shall prevail. 1.3.5 Polysemy
If a given term has several meanings in a working language, each meaning is given a separate entry to facilitate translation into other languages.
1.3.6 Abbreviations
As indicated in 1.3.2, currently used abbreviations are assigned to some terms. These abbreviations are not used in the text of definitions, examples or notes.
1.3.7 Use of parentheses
In some terms, a word or words printed in bold are placed in parentheses. These words are part of the complete term. When the use of abbreviated terms in technical texts does not affect the meaning of the context, these words may be omitted. In the body of definitions, examples or notes of GB/T 5271, these terms are used in full form. In some entries, the term is followed by words in normal font placed in parentheses. These words are not part of the term, but indicate relevant information about the use of the term, such as its special scope of application, or its grammatical form. 1.3.8 Use of square brackets
If the definitions of several closely related terms differ by only a few words, these terms and their definitions are grouped together in one entry. Alternative words to indicate different meanings are placed in square brackets in the same order as in the term and in the definition. To avoid ambiguity of the replaced word, the last word placed before the brackets according to the above rules may be placed inside the square brackets and repeated for each change. 1.3.9 Use of boldface terms and use of asterisks in definitions When a term is printed in boldface in a definition, example or note, it means that the term has been defined in another entry of this vocabulary. However, the term is printed in boldface only when it first appears in each entry. Boldface is also used for other grammatical forms of a term, such as plural nouns and participle forms of verbs. The basic forms of all terms that appear in boldface in GB/T 5271 are listed in the index at the end of this part (see 1.3.10). When two boldface terms are cited in different entries one immediately after the other, separate them with an asterisk (or simply with punctuation).
Words or terms appearing in ordinary font are to be understood as defined in general dictionaries or authoritative technical vocabulary. 1.3.10 Compilation of index tables
For each language used, an alphabetical index is provided at the end of each part. The index includes all terms defined in that part. Multi-word terms appear in alphabetical order after each keyword. 2 Terms and definitions
28 Basic concepts of artificial intelligence and expert systems 28.01 General terms
28.01.01 Artificial intelligence (1) artificial intelligence (1) 373
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Al(abbreviation)Al(abbreviation)
: An interdisciplinary subject, usually regarded as a branch of computer science, that studies models and systems that exhibit various functions related to human intelligence (such as reasoning and learning).
Note: This definition is an improvement on the definition of this term in GB/T 5271.1. Artificial intelligence (2) artificial intelligence (2) Al(abbreviation)Al(abbreviation)The ability of a functional unit to exhibit various functions related to human intelligence (such as reasoning and learning). Knowledge (in artificial intelligence) knowledge (in artificial intelligence) 28.01.03
Collection of facts, events, beliefs, and rules for systematic use. Domain (in artificial intelligence) domain (in artificial intelligence) 28.01.04
Knowledge or expert experience in a particular area. 28. 01: 05
5Knowledge-based systemknowledge-based systemKBS(abbreviation)KBS(abbreviation)-an information processing system that solves problems in a specific field or application range by reasoning from a knowledge base. Notes
1 The term "knowledge-based system" is sometimes used synonymously with "expert system", but the knowledge in the latter is usually limited to expert knowledge. 2 Some knowledge-based systems have the ability to learn. 6 Expert systemexpert system
28. 01. 06
ES(abbreviation)ES(abbreviation)
A knowledge-based system that solves problems in a specific field or application range by reasoning based on a knowledge base developed by human experts' experience.
1 The term "expert system" is sometimes used synonymously with "knowledge-based system", but the former emphasizes the knowledge of experts. 2 Some expert systems can improve their knowledge base and develop new reasoning rules based on previous experience in solving problems. 3 This definition is an improvement on the definition of this term in GB/T5271.1. 28.01.07 Knowledge Engineeringknowledge engineering is the study of the acquisition of knowledge from domain experts and other knowledge sources and the incorporation of this knowledge into a knowledge base. Note: The term "knowledge engineering" is sometimes used specifically to refer to the design, construction, and maintenance of expert systems and other knowledge-based systems. 28.01.08 knowledge representation the process or result of encoding knowledge* and storing it in a knowledge base. knowledge acquisition 28.01.09
the process of finding, collecting, and refining knowledge and converting it into a form that can be further processed by a knowledge-based system.
28. 01. 10
Note: Knowledge acquisition often implies the involvement of knowledge engineers, but is also an important component of machine learning. cognitive modeling
the modeling of human perception, action, memory, and reasoning through information processing. 28.01.11 reasoning
the process by which a person or computer analyzes, classifies, or diagnoses, makes hypotheses, solves problems, or draws inferences based on known information.
problem solving solving
28. 01. 12
Determine a series of operations or actions to achieve a desired goal. Note: Problem solving is a process of starting from an initial state and then searching in the problem space to achieve the desired goal. Successful problem solving requires understanding: what the initial state is, what acceptable results will be, and determining the goals to be achieved and the requirements or operations that define the problem space.
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28.01.13 Pattern recognition
Jpattern recognition
The recognition of the physical or abstract pattern, structure and configuration of an object through functional units. Note: This definition is a refinement of the definition of this term in GB/T5271.12. 28.01.14 Image recognitionimagerecognitionThe perception and analysis of images, their constituent objects, their features and the spatial relationships between them through functional units.
Note: Image recognition includes scene analysis.
speech recognitionspeechrecognition
28. 01. 15
automatic speech recognition
automatic speech recognitionASR (abbreviation) ASR (abbreviation) The perception and analysis of information represented by human speech through functional units. Note: The information to be recognized can be a word in a predefined word sequence or a phoneme in a predefined language. Sometimes the speaker can be identified by the characteristics of his voice. 28.07.16 Synthesis (in artificial intelligence) synthesis (in artificial intelligence) The generation of artificial speech, text, music and images through functional units. Image understandingimage understanding; image comprehension28. 01.17
The generation of a description of the meaning represented by a given image through functional units. Note: Image understanding is carried out by integrating visual data with the help of geometric modeling, knowledge representation and cognitive modeling to generate information. 28.01.18 Natural-language understanding; natural-language comprehension Extract information from text or speech in natural language form that has been passed to the functional unit through the functional unit and produce a description of the given text or speech and its representation. 28.01.19
Computer vision
Artificial vision
The ability of the functional unit to acquire, process and interpret visual data. Note
1 Computer vision involves the use of visual sensors to create electronic or digital images of visual scenes. 2 It should not be confused with machine vision.
28.01.20 Machine vision The application of computer vision in machines, robots, processes or quality control. Note: The term "machine vision" is used in engineering and should not be confused with computer vision. 28. 01. 21
machine learning
automatic learning
machine learning
automatic learning
the process by which a functional unit improves its performance by acquiring new knowledge or skills, or by reorganizing existing knowledge or skills. Neural network
neural netneural net
NN(abbreviation)NN(abbreviation)A network that interconnects primitive processing elements by weighted links with adjustable weights, and that produces a value for each element by applying a nonlinear function to the input value, which is then passed to other elements or represented as an output value. Notes
1Neural networks are modeled after the role played by neurons in a nervous system. 2Nonlinear functions are usually threshold functions.
2Knowledge structure and knowledge representation
28.02.01Fact (in artificial intelligence)A statement about an entity in the real or conceptual world whose validity is generally agreed upon. 377www.bzxz.net
28. 02. 02
28. 02.03
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Note: Facts can be viewed as beliefs with a high degree of certainty. Belief (in artificial intelligence) A statement about an entity in the real or conceptual world whose validity is measured by a certain degree of certainty. Note
1 Beliefs help to derive conclusions from incomplete knowledge. 2 Beliefs with a high degree of certainty can be considered facts. Certainty factor
Confidence factor
A value that indicates the validity of a statement (such as a hypothesis, a principle of reasoning, or a conclusion of reasoning). Note: The range of certainty is from completely false to completely true. Fuzzy set
28.02. 04
A nonclassical set with the characteristic that each of its members has a corresponding number from 0 to 1, indicating the degree to which each member belongs to the set.
5 fuzzy logic
fuzzy-set logic
A nonclassical logic in which facts, inference principles, and quantifiers have given degrees of certainty. 28.02.Q6 object (in artificial intelligence) objectin artificial intelligence) a physical or conceptual entity with one or more attributes. Note: Objects are generally related to other stored objects by symbolic reasoning or relations. Schema (in artificial intelligence) 28. 02.07
A formal system of representing aspects of a concept, entity, or class of objects in knowledge by the uses to which that knowledge may be put.
Note: A schema illustrates the way in which a concept is used. It does not, however, illustrate typical instances of that concept. Pattern (in artificial intelligence) 28.02.08
-a set of features and their relationships that are used to identify an entity in a given context. Note: These features may include geometric shapes, sounds, pictures, signals, or text. 28.02.09 Template template
A reference pattern that is compared to a part or the whole of an entity to be recognized. Note: Templates are used for character recognition, object detection, language recognition, etc. 0 Semantic network semantic network 28.02.10
Semantic net semantic net
A concept-based knowledge representation in which objects or states appear as nodes connected by links that indicate the relationships between the nodes.
28.02.11 Knowledge tree knowledge tree 28.02.12
A hierarchical semantic network represented by a directed tree (graph). 2 Inheritance (in artificial intelligence) inheritance (in artificial intelligence) In a hierarchical knowledge representation, one or more subclasses acquire the characteristics of a parent class by default. 28.02.13 Frame (in artificial intelligence) A data-oriented knowledge representation associated with an object having a set of features, each of which is stored in a dedicated area called a slot.
slot (in artificial intelligence) 28.02.14
The component of a frame that stores various features, such as the name of the object, specific attributes called faces, and values ​​and pointers to other frames.
28.02. 15 Script script; scenario 378
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A knowledge representation that determines the results of interactions between known entities through a predetermined sequence of events. Notes
1 Events are represented by scene recordings, cloth tickets, thematic characters and props. 2 Compared with frames, scripts are event-oriented, while the latter are data-oriented and use time as a reference point. 28.02.16 Thematic characters role A set of functions that an entity can perform during the implementation of a script. Note: The subject role is played by an actor.
An entity that does not take actions itself during the implementation of a script. 28.02.18 Setting
Setting
A specific background including props described by a script. Scene (in artificial intelligence) 28. 02. 19
Episode
In a script-based knowledge representation, a programmed sequence of actions or events. Note: In a script about a restaurant, the following scenes can be seen: entering the restaurant, ordering food, eating, paying, and leaving the restaurant. 28.02.20
Action (in artificial intelligence) Action (in artificial intelligence) In a script-based knowledge representation, an action performed by an actor. 28.02. 21
The entity that plays the role of the subject in a script. Examples: agent, co-agent, beneficiary, patient, etc. 28.02.22 Declarative knowledge Knowledge expressed in facts, rules, and theorems. Note: In general, declarative knowledge cannot be processed without being converted into procedural knowledge. 28.02.23 Procedural knowledge Knowledge that explicitly specifies the steps to be taken to solve a problem or achieve a goal. 28.02.24
Compiled knowledge Declarative knowledge that has been converted into procedural knowledge so that it is immediately available for computer processing. 28.02.25 Metaknowledge Knowledge about the structure, use, and control of knowledge. Note: Metaknowledge can be an effective control mechanism in expert systems and other knowledge-based systems. 28.02.26 if-then ruleif-then ruleif-then statementA formal logic rule consisting of an “if” part and a “then” part. The “if” part represents the premise or condition, and the “then” part represents the action to be taken or the goal to be achieved when the “if” part is true. 28.02.27
left-hand side
premise part
condition part
the “if” part of an if-then rule is a set of facts or statements. 28.02.28 right-hand side
conclusion part
action part
the set of facts or statements in the "then" part of an if-then rule. 28.02.29 production rule379
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an if-then rule used to represent knowledge in a rule-based system. 28.02.30 metarule
a rule that specifies the conditions, order, and manner in which another rule or a given set of rules should be used. Note: Metarules can be effective control mechanisms in expert systems and rule-based systems. Constraint ruleconstraint rule
A rule that restricts the search to a specified part of the problem space. Note: In expert systems and rule-based systems, constraint rules may be effective control mechanisms. 28.02.32
To fire
To initiate the action specified by the rule when the conditions stated in the rule are met. 3 Multiple firing
To fire a rule more than once in order to repeatedly access knowledge in the same consultation. 28.02.34
activation (in artificial intelligence)An operation that allows a rule to be fired, or a procedure or subroutine to be called. Tracing facility (in artificial intelligence)In a knowledge-oriented programming language or programming tool, a means of displaying the rules executed and the values ​​of the variables used. 28.02.365
daemondemon;daemon
A procedure that is invoked without explicit invocation whenever a change, addition, deletion, or other event occurs. Agenda
A list of priorities for pending activities. Note: In artificial intelligence, such activities consist of the application of some knowledge. 28.03 Inference and problem solving
28.03.01 Inference
A method of reasoning that derives conclusions from known premises. Note
In artificial intelligence, premises are facts or rules. 2 The term "reasoning" refers to both the process and the result. 28.03.02
2deduction
deductive inference
A method of reasoning that derives a logical conclusion from a particular set of premises. Note: Deduction is the only truth-preserving reasoning.
28.03.03induction
inductive inference
A method of reasoning that starts with known facts and ends with a general hypothesis. abduction
28. 03. 04
abductive inference
A method of reasoning that derives from particular facts to plausible explanations of those facts. 28.03.05model-driven reasoning
model-driven inference
A method of reasoning using a domain model.
Note: See also horizontal-based expert system. instantiation
The generation of a variable value, or instance, from a class. Example: A particular patient is an instantiation of the class attribute object "patient". Note: In a rule-based system, an instantiation is the result of successfully matching a rule with the contents of a knowledge base. forward chaining
GB/T 5271.28—2001
An iterative process that adjusts the order of reasoning, starting from established facts and ending when the rule-based system reaches its goal or exhausts new possibilities. 28.03.08 backward chaining Chaining is an iterative process that adjusts the order of reasoning, starting with the target rule whose truth value is to be determined, and then reasoning backwards through the system rules until the problem is solved, or a previously stored result is found, or a contradiction is encountered, or the truth value is found to be unable to be determined.
heuristic rule heuristic rule
A special written rule that formalizes the knowledge and experience that experts use to solve problems. 0 state (used in artificial intelligence) state (in artificial intelligence) 28.03.10
A snapshot description of the problem at a certain stage in the problem solving process. 28.03.11 search space search space In the process of problem solving, the set of possible steps that can be taken to lead from the initial state to the target state. 28. 03. 12
problem space problem space
The conceptual or formal definition of the range of all possible states that can be used to analyze the interactions between elements and operations that are considered in solving a particular problem. 28.03.13
3 Solution spacesolutionspace
The conceptual or formal definition of the range of all states that constitute the answer to a particular problem. 28.03.14 Evaluation functionevaluation functionA function that determines the value or weight of an intermediate state in the problem space when searching for an answer. 28.03. 15
Problem reduction
A problem-solving approach that uses various operations to decompose a problem into several subproblems that are generally easier to solve than the original problem.
5 Means-ends analysis ;means-end analysis28. 03. 16
A problem-solving approach that searches for operations that minimize the gap between the current state and the known target state.
28. 03. 17
generate-and-test
generate-and-test
A problem-solving approach that generates possible answers, removing those that do not satisfy given criteria by pruning. 28.03.18blackboard modelblackboard modelA problem-solving model that has a shared working memory, called a blackboard, that is accessible to several knowledge sources and used to communicate intermediate results or new data. pattern matchingpatternmatching
The identification of a pattern by comparing it to a predetermined set of patterns and selecting the closest one according to given criteria. template matchingtemplate matching
28. 03.204
Pattern matching using templates.
rule matchingrule matching
Matching the elements of a given problem to a target by cascading a series of if-then rules whose assumptions are all true. 28. 03. 22
Conflict resolution
In rule-based systems, the problem of multiple matches is solved by selecting the most appropriate rule. Note: Multiple matches can occur in pattern matching or on the left side of a rule, so that two rules produce conflicting solutions. 28.03.23 Search tree
A tree diagram that illustrates the rules used in the search, the nodes examined, and the results obtained. 28.03.24 Depth-first search A search that selects a possible branch from the highest level of the search tree and descends layer by layer along the selected branch until the target, or a predetermined depth or final target is reached.
GB/T5271.28—2001
Note: If the target is not reached, backtrack to the previously unevaluated branch and then proceed as above. 5 breadth-first search 28.03.25
A search that proceeds from the top of the search tree to the bottom, checking all possible candidate nodes at one level, and then checking the next level until the goal or a predetermined state is reached. bidirectional search 28.03.26
A search that starts with both forward and backward links and stops when the search path reaches the solution space or exhausts all possibilities.
28.03.27 heuristic search A search based on experience and judgment, used to obtain acceptable results without guaranteeing success. 28.03.28 best-first search A search that, at each step in the search sequence, evaluates all possible branches to the goal according to a predetermined set of criteria and selects the best search path based on the evaluation results. 9 backtracking
A search process in which, when the choice made produces an unacceptable result, the search returns to the previous state to make another choice.
Note: Because some executed instructions may have irreversible side effects, it is not always possible to reconstruct the previous state. 28.03. 30 Pruning; cut-off A problem-solving optimization technique used to ignore one or more branches of the search tree. Planning (in artificial intelligence) 28.03.31
The process of deciding in advance the manner and order of actions to be taken in order to achieve the desired goal. Note: The purpose of planning is to improve search efficiency and resolve conflicting goals. 28.03.32 Hierarchical planning A type of planning that breaks down the ambiguous parts of a plan into more detailed sub-plans by generating a hierarchical representation. 28.03.33 Nonhierarchical planning A type of planning that selects a framework plan from a set of predetermined plans and instantiates these plans through problem-solving operations for a specific problem context.
28.03.34 Opportunistic planning A type of planning that includes timely problem-solving actions for the plan being specified. 28.04 Expert system
Knowledge engineer knowledge engineer 28.04.01
A person who obtains knowledge from domain experts and other knowledge sources and organizes this knowledge into a knowledge base. Note: See also knowledge engineering.
2 Knowledge engineering tool knowledge engineering tool 28.04.02
A functional tool that facilitates the rapid development of knowledge-based systems. Note: Knowledge engineering tools include specific strategies for knowledge representation, inference and control, as well as basic modeling structures that facilitate the solution of typical problems. 28.04.03 Knowledge source knowledge source - a source of information based on which a knowledge base has been established for a specific type of problem. 28.04.04 Domain knowledge domain knowledge accumulated knowledge in a specific domain.
28.04.05 Domain model domain model - a model that represents a specific range of knowledge or expert experience. 28.04.06 Knowledge base knowledge base 382
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K-base (abbreviation) K-base (abbreviation) KB (abbreviation) KB (abbreviation) ... a database that includes inference rules and information about human experience and expert experience in a certain field. Notes
1 In a self-improving system, the knowledge base also includes information generated by solving previously encountered problems. 2 For GB/T5271.1., a new abbreviation has been added here. Inference engine inference engine
A program in an expert system that can derive conclusions from information representations stored in a knowledge base based on inference principles. 28.04.08 Expert system shell; shellexpert system shell; shellAn empty expert system in which expert experience in a certain subject can be established. Note: An expert system shell generally consists of a high-level language for knowledge representation, one or more inference engines, and various interface* programs.
9 explanation facility28.04.09
The component of a knowledge-based system that explains how the answer was obtained and evaluates each step in obtaining the answer. 28.04.10 dialog componentdialog componentThe component of a knowledge-based system that communicates with the user in a dialog mode. consultation (in artificial intelligence)28.04.11
Online interaction between a knowledge-based system and a user seeking help, usually consisting of a question-and-answer dialogue. 28.04. 12 truth maintenance systemTMS(abbreviation)TMS(abbreviation)A knowledge-based system that maintains the truth of a knowledge base by following the dependencies between beliefs. Note: Truth maintenance primarily involves removing knowledge items that cause contradictions. 28.04.13 uncertainty
The degree to which a value in a consultation is uncertain, or to which there is doubt about a fact or rule in the knowledge base. 28.04.14 rule-based systemrule-based systemproduction system
A knowledge-based system that makes inferences by applying a set of if-then rules to a set of facts following a given procedure.
5 Model-based expert system28.04.15
Model-based systemAn expert system that integrates the structure and functionality of a domain model. Examples: the "student model" in some intelligent tutoring systems, the templates used to build diagnostic systems. Advisory system28. 04. 16
An expert system that emphasizes the use of advice rather than instructions. 38312 truth maintenance system truth maintenance system TMS (abbreviation) TMS (abbreviation) A knowledge-based system that maintains the truth of a knowledge base based on the dependencies between beliefs. Note: Truth maintenance mainly involves removing knowledge items that cause contradictions. 28.04.13 Uncertainty
The degree to which a value in a consultation process is uncertain, or there is doubt about the facts or rules in the knowledge base. 28.04.14 Rule-based system rule-based system production system
~A knowledge-based system that makes inferences by applying a set of if-then rules to a set of facts following a given process.
5 Model-based expert system model-based expert system 28.04.15
Model-based system model-based system An expert system that integrates the structure and functionality of a domain model. Examples: "student models" in some intelligent tutoring systems, templates built in diagnostic systems. Advisory system: advisory system 28. 04. 16
An expert system that emphasizes the use of advice rather than instructions. 38312 truth maintenance system truth maintenance system TMS (abbreviation) TMS (abbreviation) A knowledge-based system that maintains the truth of a knowledge base based on the dependencies between beliefs. Note: Truth maintenance mainly involves removing knowledge items that cause contradictions. 28.04.13 Uncertainty
The degree to which a value in a consultation process is uncertain, or there is doubt about the facts or rules in the knowledge base. 28.04.14 Rule-based system rule-based system production system
~A knowledge-based system that makes inferences by applying a set of if-then rules to a set of facts following a given process.
5 Model-based expert system model-based expert system 28.04.15
Model-based system model-based system An expert system that integrates the structure and functionality of a domain model. Examples: "student models" in some intelligent tutoring systems, templates built in diagnostic systems. Advisory system: advisory system 28. 04. 16
An expert system that emphasizes the use of advice rather than instructions. 383
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