Inference Corporation [1] [2] was an American software company that specialized in artificial intelligence systems. [3]
Los Angeles-based Inference was founded in 1979. [3] In the 1990s they built a case-based computer program for Compaq Computer Corporation that would enable dealing with a situation where "a computer printer turns out a blurry and smeared page" without having to call a help desk. [1] Although such software already existed, the breakthrough was that it was small enough to fit "on three floppy disks."
The company's Automated Reasoning Tool (ART), initially implemented on a mainframe, subsequently made available on PCs, has been extended to ART-IM, an Information Management package; the product line originated in 1988. [4] [5]
Ford and AOL are among the household-known corporations that use Inference software to enhance customer service. [6] [3] Inference was acquired by eGain Corporation in 2000. [7] Prior to that, Inference acquired 1981-founded Computer Mathematics Corporation, marketer of SMP (computer algebra system) ; [8] Inference made another acquisition the year before they themselves were acquired by eGain. [9]
The Automated Reasoning Tool (ART) is a system designed by Paul Haley, [10] Chuck Williams, Brad Allen, and Mark Wright, [11] to design rule-based knowledge representations with options for frame and procedural methods of knowledge base representation. [12]
ART's syntax influenced NASA's derived CLIPS in the mid-80s. [11] ART is a derivative of OPS5, with extensions, built for the Inference Corporation. [10]
Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge. The project began in July 1984 at MCC and was developed later by the Cycorp company.
In artificial intelligence (AI), 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 programming code. Expert systems were among the first truly successful forms of AI software. They were created in the 1970s and then proliferated in the 1980s, being then widely regarded as the future of AI — before the advent of successful artificial neural networks. An expert system is divided into two subsystems: 1) a knowledge base, which represents facts and rules; and 2) an inference engine, which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities.
Knowledge representation and reasoning 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.
Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture. In a sense, they were the first commercial single-user workstations. Despite being modest in number Lisp machines commercially pioneered many now-commonplace technologies, including effective garbage collection, laser printing, windowing systems, computer mice, high-resolution bit-mapped raster graphics, computer graphic rendering, and networking innovations such as Chaosnet. Several firms built and sold Lisp machines in the 1980s: Symbolics, Lisp Machines Incorporated, Texas Instruments, and Xerox. The operating systems were written in Lisp Machine Lisp, Interlisp (Xerox), and later partly in Common Lisp.
Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.
In computer science, formal methods are mathematically rigorous techniques for the specification, development, analysis, and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design.
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.
E is a high-performance theorem prover for full first-order logic with equality. It is based on the equational superposition calculus and uses a purely equational paradigm. It has been integrated into other theorem provers and it has been among the best-placed systems in several theorem proving competitions. E is developed by Stephan Schulz, originally in the Automated Reasoning Group at TU Munich, now at Baden-Württemberg Cooperative State University Stuttgart.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
Roger Carl Schank was an American artificial intelligence theorist, cognitive psychologist, learning scientist, educational reformer, and entrepreneur. Beginning in the late 1960s, he pioneered conceptual dependency theory and case-based reasoning, both of which challenged cognitivist views of memory and reasoning. He began his career teaching at Yale University and Stanford University. In 1989, Schank was granted $30 million in a ten-year commitment to his research and development by Andersen Consulting, through which he founded the Institute for the Learning Sciences (ILS) at Northwestern University in Chicago.
Logic in computer science covers the overlap between the field of logic and that of computer science. The topic can essentially be divided into three main areas:
A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.
In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science and philosophy.
SNARK, , is a theorem prover for multi-sorted first-order logic intended for applications in artificial intelligence and software engineering, developed at SRI International.
Spacecraft Health Inference Engine (SHINE) is a software-development tool for knowledge-based systems, created by the Artificial intelligence Group, Information Systems Technology Section at NASA/JPL. The system is in use in basic and applied AI research at JPL. SHINE was designed to operate in a real-time environment. It is written in Common LISP, but able to be utilized by non-LISP applications written in conventional programming languages such as C and C++. These non-LISP applications can be running in a distributed computing environment on remote computers or on a computer that supports multiple programming languages. SHINE provides a variety of facilities for the development of software modules for the primary functions in knowledge-based reasoning engines. The system may be used to develop artificial intelligence applications as well as specialized tools for research efforts.
Brian R. Gaines is a British scientist, engineer, and Professor Emeritus at the University of Calgary.
In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.
eGain Corporation is a cloud-based software company with its headquarters at Sunnyvale, California. eGain provides applications for customer service, knowledge management, and analytics, that businesses use to serve and sell to their customers. The company is listed on NASDAQ.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
developed for Compaq by the Inference Corporation
ART evolved from an expert system used to interpret radar signals from space flight operation at NASA.