Sing, Dipali · Zenodo (CERN European Organization for Nuclear Research) · 2025
The document explains inference engines, the core components of artificial intelligence systems such as expert systems. An inference engine applies logical rules from a knowledge base to input data to derive conclusions or recommendations. Key components include the knowledge base (storing rules and facts), the inference mechanism (logical engine applying rules), and working memory (holding intermediate results). The inference engine employs techniques like forward and backward chaining, pattern matching, conflict resolution, and uncertainty handling to process data and make decisions. It supports features such as transparency through explanations, adaptability via learning modules, and efficient rule application. In modern AI, inference engines complement machine learning models by providing transparent reasoning and decision-making in areas like medical diagnosis, financial analysis, and robotics, bridging the gap between rule-based logic and data-driven predictions.
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Martin H. Weik · 2000
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SpringerReference · 2011
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Saul I. Gass, Michael C. Fu · 2013
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SpringerReference · 2011
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Alan Bundy, Lincoln Wallen · 1984
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Damian Rouson, Jordan A. Welsman, Tan Nguyen · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2022
Given a pre-trained neural network, Inference-Engine performs maps network inputs to outputs by executing the forward pass through the provided network. Although the predominant programming language for machine-learning is Python, most high-performance computing (HPC) applications are written in Fortran, C, or C++. Inference-Engine aims to support HPC programs and is written in Fortran, a language with a large feature set supporting interoperability with C. This software exposes concurrency in a portable way by using standard language features that some modern Fortran compilers can exploit with various optimizations, including offloading computation to a Graphics Processing Unit (GPU). In particular, this software makes extensive use of Fortran's "do concurrent" parallel loop construct, implicitly parallel array statements, and pure procedures that can be invoked inside "do concurrent" blocks. Inference-Engine also supports dynamic choice of inference methods at runtime. Two current options include one method that uses Fortran's "dot_product" intrinsic function inside "do concurrent" blocks and another method that instead uses Fortran' "matmul" array intrinsic function. We plan to investigate automatic compiler offloading of "do concurrent" calculations to GPUs and compile-time substitution of optimized libraries such as the Basic Linear Algebra Library (BLAS) for "matmul" invocations. We also envision the potential for the choice of which method to use could happen at program launch based on in situ performance measurements on any given platform.
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Toni Bollinger, Sven Lorenz, Udo Pletat · Lecture notes in computer science · 1991
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Hoyt, Charles Tapley, Jeremy Zucker, Benjamin M. Gyori et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024
What's Changed Use variables for all LV-DAGs by @cthoyt in https://github.com/y0-causal-inference/y0/pull/219 Full Changelog: https://github.com/y0-causal-inference/y0/compare/v0.2.9...v0.2.10
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For Rule-Based Geometric, Joseph J. Pfeiffer · 2003
Isaac is a rule-based visual language for mobile robots using evidential reasoning and a fuzzy inference engine. A prototype inference engine for Isaac has been implemented, permitting experiments with the Isaac language.
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Willem De Bruijn · 2003
1 introduction 3 2 design choices 5 2.1 separation of concerns........................ 5 2.2 knowledge representation...................... 6
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Dennis Merritt · Springer compass international · 1989
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Gibbon, Dafydd · PUB – Publications at Bielefeld University (Bielefeld University) · 2009
Gibbon D. Prosodic inference with the ZDATR default inference engine. <em>Speech and Language Technology</em>. 2009;12/13 (2009--2010).
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Shahriar Pourazin, Ahmad Abdollah, Zadeh Barforoush · 2000
ABSTRACT: Previous efforts in implementation of systems with Meta-knowledge (e.g. TEIRESIAS and MOLGEN) have been limited to some degree of hard-coded features. In this paper we introduce a Flexible Engine (FlEng) as an experimental inference engine which keeps its control strategy with its object knowledge both together. The inference engine (FlEng) is a small kernel using the complex inference mechanisms stored as part of knowledge base. Open definitions let the knowledge engineer (re)write a control strategy. The architecture of FlEng has been described in this paper. It has been shown that the (1) automatic evaluation of control strategies, (2) processing the uncertainty of control strategies and also (3) learning new inference methods could be performed by FlEng.
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Caraça-Valente Hernández, Juan Pedro, Gómez-Pérez, A., Juristo Juzgado, Natalia et al. · UPM Digital Archive (Technical University of Madrid) · 1992
To date all inference engines have been based on modus ponens which is, of course, a deductive inference rule. However, when making inferences, human beings do not only use modus ponens or any other deductive inference rule, they also employ inductive, abductive and deductive inference rules, as required by the circumstances. In this paper, after having studied the different kinds of inference in AI: deductive, inductive, abductive and retroductive, we present a design of an inference engine which incorporates three of these kinds of inference and uses each of them when most appropriate, depending on the data, information and knowledge with which it is supplied at any time. Thus we have a true data-driven inference system.
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So-Young Im, Ryum-Duck Oh, Yoon-Cheol Hwang · Communications in computer and information science · 2011
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Daniel E. O’Leary · Decision Support Systems · 1997
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Jin Hee Kim, Judea Pearl · IEEE Transactions on Systems Man and Cybernetics · 1987
An operational domain-independent decision-aiding system for situation assessment tasks is described. The system elicits the user's perception of a given situation through a stylized English dialogue and focuses the user's attention on the issue of highest relevancy. Elicited problems are structured as networks where nodes represent variables and directed links represent causal relationships. The system uses a Bayesian inference procedure which combines causal and diagnostic reasoning using a bidirectional propagation of evidence in the form of belief parameters. Upon completion of the dialogue the system provides a formal structure representing relevant propositions, their interrelations, and their updated belief distributions.
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Howard Shrobe, John G. Aspinall, Neil L. Mayle · 1988
Parallel processing systems offer a major improvement in capabilities to AI programmers. However, at the moment, all such systems require the programmer to manage the control of parallelism explicitly, leading to an unfortunate intermixing of knowledge-level and control-level information. Furthermore, parallel processing systems differ radically, making a control regime that is effective in one environment less so in another. We present a means for overcoming these problems within a unifying framework in which 1) Knowledge level information can be expressed effectively 2) Information regarding the control of parallelism can be factored out and 3) Different regimes of parallelism can be efficiently supported without modification of the knowledge-level information. The Protocol of Inference introduced in [Rowley et al., 19871 forms the basis for our approach.
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David S. Vaughan, Bruce M. Perrin, Robert M. Yadrick et al. · arXiv (Cornell University) · 2013
Expert systems applications that involve uncertain inference can be represented by a multidimensional contingency table. These tables offer a general approach to inferring with uncertain evidence, because they can embody any form of association between any number of pieces of evidence and conclusions. (Simpler models may be required, however, if the number of pieces of evidence bearing on a conclusion is large.) This paper presents a method of using these tables to make uncertain inferences without assumptions of conditional independence among pieces of evidence or heuristic combining rules. As evidence is accumulated, new joint probabilities are calculated so as to maintain any dependencies among the pieces of evidence that are found in the contingency table. The new conditional probability of the conclusion is then calculated directly from these new joint probabilities and the conditional probabilities in the contingency table.
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