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Knowledge Engineering Environment
Knowledge Engineering Environment (KEE) is a Frame language, frame-based development tool for expert systems. It was developed and sold by IntelliCorp (software), IntelliCorp, and first released in 1983. It ran on Lisp machines, and was later ported to Lucid Common Lisp with the CLX (Common Lisp), CLX Library (computing), library, an X Window System (X11) interface for Common Lisp. This version was available on several different UNIX workstations. On KEE, several extensions were offered: * Simkit, a frame-based simulation library * KEEconnection, database connection between the frame system and relational databases In KEE, frames are called ''units''. Units are used for both individual instances and classes. Frames have ''slots'' and slots have ''facets''. Facets can describe, for example, a slot's expected values, its working value, or its inheritance rule. Slots can have multiple values. Behavior can be implemented using a message passing model. KEE provides an extensive graph ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IntelliCorp (software)
IntelliCorp (IC) sold its assets including LiveCompare, LiveModel and LiveInterface to Tricentis in May 2019. History Founded in 1980, IC marketed an early expert system environment (Knowledge Engineering Environment, Knowledge Engineering Environment – KEE) for development and deployment of knowledge systems on the Lisp machines that had several advanced features, such as Truth maintenance systems, truth maintenance. KEE used the backward chaining, backward-chaining method of Mycin which had been developed at Stanford University, Stanford. While moving AI Winter#The collapse of the Lisp machine market in 1987, KEE functionality to the PC, IC created one of the early object-oriented technology, object-oriented technologies for commercial programming development environments (LiveModel). The company was also one of the UML Partners, a consortium which helped develop the standards for UML, the Unified Modeling Language. In May 2019, IC completed the sale of its assets including L ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Message Passing
In computer science, message passing is a technique for invoking behavior (i.e., running a program) on a computer. The invoking program sends a message to a process (which may be an actor or object) and relies on that process and its supporting infrastructure to then select and run some appropriate code. Message passing differs from conventional programming where a process, subroutine, or function is directly invoked by name. Message passing is key to some models of concurrency and object-oriented programming. Message passing is ubiquitous in modern computer software. It is used as a way for the objects that make up a program to work with each other and as a means for objects and systems running on different computers (e.g., the Internet) to interact. Message passing may be implemented by various mechanisms, including channels. Overview Message passing is a technique for invoking behavior (i.e., running a program) on a computer. In contrast to the traditional technique of callin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of ''reasoning'', such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. History The earliest work in computerized knowledge represe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge-based System
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine. The first part, the knowledge base, represents facts about the world, often in some form of subsumption ontology (rather than implicitly embedded in procedural code, in the way a conventional computer program does). Other common approaches in addition to a subsumption ontology include frames, conceptual graphs, and logical assertions. The second part, the inference engine, allows new knowledge to be inferred. Most commonly, it can take the form of IF-THEN rules coupled with forward chaining or backward chaining approaches ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Base
A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. Original usage of the term The original use of the term knowledge base was to describe one of the two sub-systems of an expert system. A knowledge-based system consists of a knowledge-base representing facts about the world and ways of reasoning about those facts to deduce new facts or highlight inconsistencies. Properties The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term ''database''. During the 1970s, virtually all large management information systems stored their data in some type of hierarchical or relational database. At this point in the history of information technology, the distinction between a database and a knowledge-base was clear and unambiguous. A databas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reason Maintenance
{{more footnotes, date=September 2009 Reason maintenanceDoyle, J., 1983. The ins and outs of reason maintenance, in: Proceedings of the Eighth International Joint Conference on Artificial Intelligence - Volume 1, IJCAI’83. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 349–351.Doyle, J.: Truth maintenance systems for problem solving. Tech. Rep. AI-TR-419, Dep. of Electrical Engineering and Computer Science of MIT (1978) is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture:McAllester, D.A.: Truth maintenance. AAAI90 (1990) two components—a re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inference Engine
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved. Architecture The logic that an inference engine uses is typically represented as IF-THEN rules. The ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Backward Chaining
Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications. In game theory, researchers apply it to (simpler) subgames to find a solution to the game, in a process called ''backward induction''. In chess, it is called retrograde analysis, and it is used to generate table bases for chess endgames for computer chess. Backward chaining is implemented in logic programming by SLD resolution. Both rules are based on the modus ponens inference rule. It is one of the two most commonly used methods of reasoning with inference rules and logical implications – the other is forward chaining. Backward chaining systems usually employ a depth-first search strategy, e.g. Prolog. How it works Backward chaining starts with a list of goals (or a hypothesis) and works backwards from the consequent to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Forward Chaining
Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of ''modus ponens''. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data. Inference engines will iterate through this process until a goal is reached. Example Suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks and eats ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Production System (computer Science)
A "production system " (or "production rule system") is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior but it also includes the mechanism necessary to follow those rules as the system responds to states of the world. Those rules, termed productions, are a basic representation found useful in automated planning, expert systems and action selection. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be ''triggered''. If a production's action is executed, it is said to have ''fired''. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is tri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graphical User Interface
The GUI ( "UI" by itself is still usually pronounced . or ), graphical user interface, is a form of user interface that allows users to interact with electronic devices through graphical icons and audio indicator such as primary notation, instead of text-based UIs, typed command labels or text navigation. GUIs were introduced in reaction to the perceived steep learning curve of CLIs ( command-line interfaces), which require commands to be typed on a computer keyboard. The actions in a GUI are usually performed through direct manipulation of the graphical elements. Beyond computers, GUIs are used in many handheld mobile devices such as MP3 players, portable media players, gaming devices, smartphones and smaller household, office and industrial controls. The term ''GUI'' tends not to be applied to other lower-display resolution types of interfaces, such as video games (where HUD (''head-up display'') is preferred), or not including flat screens like volumetric displays because ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relational Database
A relational database is a (most commonly digital) database based on the relational model of data, as proposed by E. F. Codd in 1970. A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using the SQL (Structured Query Language) for querying and maintaining the database. History The term "relational database" was first defined by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks". In this paper and later papers, he defined what he meant by "relational". One well-known definition of what constitutes a relational database system is composed of Codd's 12 rules. However, no commercial implementations of the relational model conform to all of Codd's rules, so the term has gradually come to describe a broader class of database systems, which at a minimum: # Present the data to the user as relati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
