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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] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and G ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OPS5
OPS5 is a rule-based or production system computer language, notable as the first such language to be used in a successful expert system, the R1/XCON system used to configure VAX computers. The OPS (said to be short for "Official Production System") family was developed in the late 1970s by Charles Forgy while at Carnegie Mellon University. Allen Newell's research group in artificial intelligence had been working on production systems for some time, but Forgy's implementation, based on his Rete algorithm, was especially efficient, sufficiently so that it was possible to scale up to larger problems involving hundreds or thousands of rules. OPS5 uses a forward chaining inference engine; programs execute by scanning "working memory elements" (which are vaguely object-like, with classes and attributes) looking for matches with the rules in "production memory". Rules have actions that may modify or remove the matched element, create new ones, perform side effects such as output, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inductive Inference
Inductive reasoning is a method of reasoning in which a general principle is derived from a body of observations. It consists of making broad generalizations based on specific observations. Inductive reasoning is distinct from ''deductive'' reasoning. If the premises are correct, the conclusion of a deductive argument is ''certain''; in contrast, the truth of the conclusion of an inductive argument is '' probable'', based upon the evidence given. Types The types of inductive reasoning include generalization, prediction, statistical syllogism, argument from analogy, and causal inference. Inductive generalization A generalization (more accurately, an ''inductive generalization'') proceeds from a premise about a sample to a conclusion about the population. The observation obtained from this sample is projected onto the broader population. : The proportion Q of the sample has attribute A. : Therefore, the proportion Q of the population has attribute A. For example, say there ... [...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 an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expert System
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. History Early development Soon after the dawn of modern computers in the late 1940s and early 1950s, researchers started realizing the immense potential these machines had for modern society. One ... [...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] |
Action Selection
Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior. One problem for understanding action selection is determining the level of abstraction used for specifying an "act". At the most basic level of abstraction, an atomic act could be anything from ''contracting a muscle cell'' to ''provoking a war''. Typically for any one action-selection mechanism, the set of possible actions is predefined and fixed. Most researchers working in this field place high demands on their agents: * The acting agent typically must select its action in dynamic and unpredictable environments. * The agents typically act in real time; therefore they must make de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Geometric And Topological Inference
''Geometric and Topological Inference'' is a monograph in computational geometry, computational topology, geometry processing, and topological data analysis, on the problem of inferring properties of an unknown space from a finite point cloud of noisy samples from the space. It was written by Jean-Daniel Boissonnat, Frédéric Chazal, and Mariette Yvinec, and published in 2018 by the Cambridge University Press in their Cambridge Texts in Applied Mathematics book series. The Basic Library List Committee of the Mathematical Association of America has suggested its inclusion in undergraduate mathematics libraries. Topics The book is subdivided into four parts and 11 chapters. The first part covers basic tools from topology needed in the study, including simplicial complexes, Čech complexes and Vietoris–Rips complex, homotopy equivalence of topological spaces to their nerves, filtrations of complexes, and the data structures needed to represent these concepts efficiently ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Personal Computer
A personal computer (PC) is a multi-purpose microcomputer whose size, capabilities, and price make it feasible for individual use. Personal computers are intended to be operated directly by an end user, rather than by a computer expert or technician. Unlike large, costly minicomputers and mainframes, time-sharing by many people at the same time is not used with personal computers. Primarily in the late 1970s and 1980s, the term home computer was also used. Institutional or corporate computer owners in the 1960s had to write their own programs to do any useful work with the machines. While personal computer users may develop their own applications, usually these systems run commercial software, free-of-charge software (" freeware"), which is most often proprietary, or free and open-source software, which is provided in "ready-to-run", or binary, form. Software for personal computers is typically developed and distributed independently from the hardware or operating system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Edward Feigenbaum
Edward Albert Feigenbaum (born January 20, 1936) is a computer scientist working in the field of artificial intelligence, and joint winner of the 1994 ACM Turing Award. He is often called the "father of expert systems." Education and early life Feigenbaum was born in Weehawken, New Jersey in 1936 to a culturally Jewish family, and moved to nearby North Bergen, where he lived until the age of 16, when he left to start college. Knuth, Don"Oral History of Edward Feigenbaum'' Computer History Museum, 2007. Accessed October 23, 2015. "I was born in Weehawken, New Jersey, which is a town on the Palisades opposite New York. In fact, it’s the place where the Lincoln Tunnel dives under the water and comes up in New York. Then my parents moved up the Palisades four miles to a town called North Bergen, and there I lived until I was 16 and went off to Carnegie Tech." His hometown did not have a secondary school of its own, and so he chose Weehawken High School for its college preparatory p ... [...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 – KEE) for development and deployment of knowledge systems on the Lisp machines that had several advanced features, such as truth maintenance. KEE used the backward-chaining method of Mycin which had been developed at Stanford. While moving KEE functionality to the PC, IC created one of the early 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 The Unified Modeling Language (UML) is a general-purpose, developmental modeling language in the field of software engineering that is intended to provide a standard way to visualize the design of a system. The creation of UML was origina ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prolog
Prolog is a logic programming language associated with artificial intelligence and computational linguistics. Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program logic is expressed in terms of relations, represented as facts and rules. A computation is initiated by running a ''query'' over these relations. The language was developed and implemented in Marseille, France, in 1972 by Alain Colmerauer with Philippe Roussel, based on Robert Kowalski's procedural interpretation of Horn clauses at University of Edinburgh. Prolog was one of the first logic programming languages and remains the most popular such language today, with several free and commercial implementations available. The language has been used for theorem proving, expert systems, term rewriting, type systems, and automated planning, as well as its original intended field of use, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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