Relevant bibliographies by topics / Artificial intelligence. Computer programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial intelligence. Computer programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Artificial intelligence. Computer programming"

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Lloyd, John W. "Declarative programming for artificial intelligence applications." ACM SIGPLAN Notices 42, no. 9 (2007): 123–24. http://dx.doi.org/10.1145/1291220.1291152.

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Gini, Maria. "The future of robot programming." Robotica 5, no. 3 (1987): 235–46. http://dx.doi.org/10.1017/s0263574700015897.

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SUMMARYThis paper presents current trends in robot programming. The open problems with current robot programming systems are outlined and indications for solutions are given. Since computer controlled robots have been introduced, the methodology of robot programming has seen a great deal of development. Two completely different approaches to robot programming have been considered in the past. On the one hand within the Artificial Intelligence community a lot of research has been done to provide robots with autonomous reasoning capabilities. On the other hand, the need to control industrial robots has pushed the development of simple but effective methods for robot programming. To put it simply, Artificial Intelligence researchers have taken a top-down approach trying to solve the difficult problem of reasoning and have assumed that all the rest was easy. Others have taken a bottom-up approach first trying to control robots and only later trying to incorporate intelligence. The complexity of industrial automation tasks requires programming systems more sophisticated that those in use today. Artificial Intelligence is the best candidate to create the next generation of robot programming systems.
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Xiao, Shu Qiang, and Jian Chun Peng. "The Application of Artificial Intelligence Technology in Electrical Automation Control." Applied Mechanics and Materials 530-531 (February 2014): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.1049.

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Computer technology has become the world's most popular information technology, computer programming software technology continues to progress, leading people to the level of economic life. The human brain is the most advanced machines, all computer programming is to emulate human computer, the computer program to imitate the human brain as the main purpose to achieve our automation development. For electrical automation of the whole control process, it is through automation equipment to complete the entire process of production, distribution, etc. Thus to a large extent, reducing the cost and working efficiency are also increased accordingly. With the development of information technology, it constantly have new technology into engineering, and the transition phase, the automatic control technology put forward new challenges, promoting the theory of intelligence in the application of control technology, in order to solve the problem with traditional methods.
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Zhang, Du, and Michael D. Kramer. "GAPS: A Genetic Programming System." International Journal on Artificial Intelligence Tools 12, no. 02 (2003): 187–206. http://dx.doi.org/10.1142/s0218213003001198.

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One of the major approaches in the field of evolutionary computation is genetic programming. Genetic programming tackles the issue of how to automatically create a computer program for a given problem from some initial problem statement. The goal is accomplished by genetically breeding a population of computer programs in terms of genetic operations. In this paper, we describe a genetic programming system called GAPS. GAPS has the following features: (1) It implements the standard generational algorithm for genetic programming with some refinement on controlling introns growth during evolution process and improved termination criteria. (2) It includes an extensible language tailored to the needs of genetic programming. And (3) It is a complete, standalone system that allows for genetic programming tasks to be carried out without requiring other tools such as compilers. Results with GAPS have been satisfactory.
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Lian, Fei, and Guang Tian Zou. "Theory and Methods on Tactics Generation of Extension Architectural Programming Facing to Artificial Intelligence." Applied Mechanics and Materials 236-237 (November 2012): 659–65. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.659.

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Theories and methods of tactics generation are not developed as well as other aspects in Architectural Programming. Rules and methods of solving problems and innovating in the process of generating tactics are deficient. This research which proposes the theory and methods facing to intelligent tactics generation in Architectural Programming is an applying basic study on the intersection of Extenics, Architectural Programming, and Artificial Intelligence. It promotes the combination and application of Architecture, Extenics, and Artificial Intelligence. Moreover it promotes the creative thinking in Architecture to develop to the intelligent direction. At last it promotes a big progress on Computer Aided Architectural Programming.
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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.
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Weng, Juyang. "Autonomous Programming for General Purposes: Theory." International Journal of Humanoid Robotics 17, no. 04 (2020): 2050016. http://dx.doi.org/10.1142/s0219843620500164.

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The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.
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Olson, Eric T. "The Ontological Basis of Strong Artificial Life." Artificial Life 3, no. 1 (1997): 29–39. http://dx.doi.org/10.1162/artl.1997.3.1.29.

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This article concerns the claim that it is possible to create living organisms, not merely models that represent organisms, simply by programming computers (“virtual” strong alife). I ask what sort of things these computer-generated organisms are supposed to be (where are they, and what are they made of?). I consider four possible answers to this question: (a) The organisms are abstract complexes of pure information; (b) they are material objects made of bits of computer hardware; (c) they are physical processes going on inside the computer; and (d) they are denizens of an entire artificial world, different from our own, that the programmer creates. I argue that (a) could not be right, that (c) collapses into (b), and that (d) would make strong alife either absurd or uninteresting. Thus, “virtual” strong alife amounts to the claim that, by programming a computer, one can literally bring bits of its hardware to life.
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Shin, Seungki. "A Study on the Framework Design of Artificial Intelligence Thinking for Artificial Intelligence Education." International Journal of Information and Education Technology 11, no. 9 (2021): 392–97. http://dx.doi.org/10.18178/ijiet.2021.11.9.1540.

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This study aims to examine the definition and attributes of artificial intelligence (AI) thinking to support AI education, so educators can determine how such education should be conducted in grades K–12. The text mining method was conducted using text crawling and co-word analysis to design and define AI thinking using the Python programming language. The cosine similarity and word2vec techniques were used to perform co-word analysis. Cosine similarity extracts paired words by assigning a weight according to the frequency of appearance. The skip-gram of word2Vec examines the surrounding words and predicts the paired words. According to the co-word analysis results, AI thinking is using an integrated thinking process to solve decision problems by discussing, providing, demonstrating, and proving processes. Moreover, AI thinking must be considered in future research on AI education. This study aims to serve as the foundational research to move forward in AI education.
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OLTEAN, MIHAI, CRINA GROŞAN, LAURA DIOŞAN, and CRISTINA MIHĂILĂ. "GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY." International Journal on Artificial Intelligence Tools 18, no. 02 (2009): 197–238. http://dx.doi.org/10.1142/s0218213009000111.

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Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.
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Dissertations / Theses on the topic "Artificial intelligence. Computer programming"

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Hearn, Robert A. (Robert Aubrey) 1965. "Building grounded abstractions for artificial intelligence programming." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/17510.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 57-58).
Most Artificial Intelligence (AI) work can be characterized as either "high-level" (e.g., logical, symbolic) or "low-level" (e.g., connectionist, behavior-based robotics). Each approach suffers from particular drawbacks. High-level Al uses abstractions that often have no relation to the way real, biological brains work. Low-level Al, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the "ground level", I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for "creatures" controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.
by Robert A. Hearn.
S.M.
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Sommaruga, Lorenzo. "Cooperative heuristics for autonomous agents : an artificial intelligence perspective." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335853.

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Werbelow, Wayne Louis. "The application of artificial intelligence techniques to software maintenance." Thesis, Kansas State University, 1985. http://hdl.handle.net/2097/9890.

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Tout, Kifah Raafat. "Parallel applications and solutions in artificial intelligence and expert systems." Thesis, Loughborough University, 1991. https://dspace.lboro.ac.uk/2134/13692.

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The work presented in this thesis focuses on the design and implementation of parallel algorithms for problem solving tasks principally in Rule-based Expert Systems and Artificial Intelligence (AI). Rule-based Expert Systems are widely used in AI. Their use covers a wide variety of application areas. However, in most cases, these systems are computation intensive and run slowly. This increases the need for high performance and real-time response. Because of the convergence of parallelism in computer design and the wide spread use of expert system in industry, the design of Parallel Expert System has become of increasing importance. Parallel computation may prove useful in shortening the processing time of the expert systems. Expert systems are being designed for both distributed (loosely-coupled) and shared-memory (tightly-coupled) multiprocessor machines. The work presented here is an attempt to focus on the issues involved in designing a rule-based expert system for a shared memory "multiprocessor system (the Sequent Balance 8000). Eight parallel Forward Chaining models and two parallel Backward Chaining models are implemented. These models are presented in Chapter 5 and 6, together with a study of their efficiency.
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Pilon, Mathieu. "A graphic simulator for a robotic workcell programming environment /." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60085.

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A robotic workcell is a collection of robots, sensors, and other industrial equipment grouped in a cooperative environment to perform various complex tasks. Due to their distributed nature however, the control and programming of robotic workcells is often a difficult task, for which dedicated environments have to be designed and built.
Simulation, especially graphic simulation, can greatly contribute to the development of programs for such integrated robotic applications: the simulator emulates the behavior of the workcell on a computer display, and allows the programmer to test and debug programs without requiring an immediate access to the physical equipment.
This thesis presents the design of a graphic simulator for robotic workcell applications. The simulator is based on SAGE/WRAP, an environment for the programming and run-time control of robotic workcells. Given a WRAP program as input, the simulator displays a top-view of the workcell and animates graphically the execution of the program; the coordination and the flow of operations within the workcell being shown, the programmer can quickly assess the overall validity of the program.
The simulator was developed in C under the X Window System, and is currently implemented as a standalone software; the design was made flexible and modular, to facilitate an eventual integration to the WRAP environment.
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Heaton, Jeff. "Automated Feature Engineering for Deep Neural Networks with Genetic Programming." Thesis, Nova Southeastern University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10259604.

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Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model's predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set.

This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm's engineered features.

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Glossenger, John Kenneth. "The role of planning in two artificial intelligence architectures." Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1991. http://www.kutztown.edu/library/services/remote_access.asp.

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Tebbutt, Colin Dean. "Control system design using artificial intelligence." Doctoral thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/14697.

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Includes bibliography.
Successful multivariable control system design demands knowledge, skill and creativity of the designer. The goal of the research described in this dissertation was to investigate, implement, and evaluate methods by which artificial intelligence techniques, in a broad sense, may be used in a design system to assist the user. An intelligent, interactive, control system design tool has been developed to fulfil this aim. The design tool comprises two main components; an expert system on the upper level, and a powerful CACSD package on the lower level. The expert system has been constructed to assist and guide the designer in using the facilities provided by the underlying CACSD package. Unlike other expert systems, the user is also aided in formulating and refining a comprehensive and achievable design specification, and in dealing with conflicts which may arise within this specification. The assistance is aimed at both novice and experienced designers. The CACSD package includes a synthesis program which attempts to find a controller that satisfies the design specification. The synthesis program is based upon a recent factorization theory approach, where the linear multivariable control system design problem is translated into, and techniques efficiency solved as, a quadratic programming problem, which significantly improve the time and space of this method have been developed, making it practical to solve substantial multivariable design problems using only a microcomputer. The design system has been used by students at the University of Cape Town. Designs produced using the expert system tool are compared against those produced using classical design methods.
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Quek, Hiok Chai. "The application of artificial intelligence techniques to the integrated control of complex dynamic physical systems." Thesis, Heriot-Watt University, 1990. http://hdl.handle.net/10399/924.

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Keflas, Petros. "Brave : an OR-parallel logic language and its application to search problems in artificial intelligence." Thesis, University of Essex, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.290744.

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Books on the topic "Artificial intelligence. Computer programming"

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1968-, Funge John David, ed. Artificial intelligence for games. 2nd ed. Elsevier Morgan Kaufmann, 2009.

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Calero, Pedro A. González. Artificial Intelligence for Computer Games. Springer Science+Business Media, LLC, 2011.

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Hasemer, Tony. Common LISP programming for artificial intelligence. Addison-Wesley, 1989.

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Shafer, Dan. Artificial intelligence programming for the Macintosh. H.W. Sams, 1986.

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Prolog programming for artificial intelligence. 3rd ed. Addison Wesley, 2001.

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Prolog programming for artificial intelligence. 4th ed. Addison-Wesley, 2011.

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Ivan, Bratko. Prolog programming for artificial intelligence. 2nd ed. Addison-Wesley Pub. Co., 1991.

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Prolog programming for artificial intelligence. Addison-Wesley, 1986.

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Prolog programming for artificial intelligence. 2nd ed. Addison-Wesley Pub. Co., 1990.

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Ivan, Bratko. Prolog programming for artificial intelligence. 2nd ed. Addison-Wesley Pub. Co, 1990.

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Book chapters on the topic "Artificial intelligence. Computer programming"

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Arvind, Steve Heller, and Rishiyur S. Nikhil. "Programming Generality and Parallel Computers." In Biological and Artificial Intelligence Systems. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-3117-6_16.

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de Souza, Gabriel Henrique, Heder Soares Bernardino, Alex Borges Vieira, and Helio José Corrêa Barbosa. "Genetic Programming for Feature Extraction in Motor Imagery Brain-Computer Interface." In Progress in Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86230-5_18.

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McMath, David, Marianna Rozenfeld, and Richard Sommer. "A Computer Environment for Writing Ordinary Mathematical Proofs." In Logic for Programming, Artificial Intelligence, and Reasoning. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45653-8_35.

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Coltell, Óscar, and José Ma Ordovás. "Applying object logic programming to design computer strategies in gene scanning." In Tasks and Methods in Applied Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_448.

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Lenat, Doug. "Scaling Up: Computers vs. Common Sense." In Logic for Programming, Artificial Intelligence, and Reasoning. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11591191_4.

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Llerena-Izquierdo, Joe, and Jonathan Zamora-Galindo. "Using H5P Services to Enhance the Student Evaluation Process in Programming Courses at the Universidad Politécnica Salesiana (Guayaquil, Ecuador)." In Artificial Intelligence, Computer and Software Engineering Advances. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68080-0_16.

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Bengio, Yoshua, Emma Frejinger, Andrea Lodi, Rahul Patel, and Sriram Sankaranarayanan. "A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for Stochastic Integer Programs." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58942-4_7.

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Galindo, Mauricio Javier Osorio, and Luis Angel Montiel Moreno. "Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_4.

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AbstractContribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.
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Balducelli, Claudio, and Massimo Gallanti. "Developing an Expert System for Fault Diagnosis of a Turbo Generator Group Using an OPS5 Production Rules Programming Environment." In Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1009-9_68.

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Pérez Castaño, Arnaldo. "Game Programming." In Practical Artificial Intelligence. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_15.

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Conference papers on the topic "Artificial intelligence. Computer programming"

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Zhao, Xin, Kai Lu, and Xiao-Ping Wang. "A Programming Abstraction for R in Distributed Parallel System." In International Conference on Computer Science and Artificial Intelligence (CSAI2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813220294_0047.

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Shao, Qing, Jianbo Wang, Qingbin Yu, Tao Xu, and Yoshino Tatsuo. "An IB-PSO algorithm for unconstrained nonlinear programming problems." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498010.

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Jinhui, Yuan, Zhou Hongwei, and Zhang Laisun. "RSGX: Defeating SGX Side Channel Attack with Return Oriented Programming." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498147.

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Huo, Manyan, Yue Yu, Zhixing Li, and Junsheng Chang. "Predicting Programming Behavior in OSS Communities: A Case Study of NLP-based Approach." In 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 2020. http://dx.doi.org/10.1109/icaice51518.2020.00091.

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Tarzariol, Alice, Martin Gebser, and Konstantin Schekotihin. "Lifting Symmetry Breaking Constraints with Inductive Logic Programming." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/284.

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Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic. Moreover, the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.
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Zhu, Hongyuan, Xi Peng, Vijay Chandrasekhar, Liyuan Li, and Joo-Hwee Lim. "DehazeGAN: When Image Dehazing Meets Differential Programming." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/172.

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Single image dehazing has been a classic topic in computer vision for years. Motivated by the atmospheric scattering model, the key to satisfactory single image dehazing relies on an estimation of two physical parameters, i.e., the global atmospheric light and the transmission coefficient. Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality. Inspired by differentiable programming, we re-formulate the atmospheric scattering model into a novel generative adversarial network (DehazeGAN). Such a reformulation and adversarial learning allow the two parameters to be learned simultaneously and automatically from data by optimizing the final dehazing performance so that clean images with faithful color and structures are directly produced. Moreover, our reformulation also greatly improves the GAN’s interpretability and quality for single image dehazing. To the best of our knowledge, our method is one of the first works to explore the connection among generative adversarial models, image dehazing, and differentiable programming, which advance the theories and application of these areas. Extensive experiments on synthetic and realistic data show that our method outperforms state-of-the-art methods in terms of PSNR, SSIM, and subjective visual quality.
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Du, Qing, Wei Zheng, and Chuanlin Xia. "Question Difficulty Priori Evaluation Based on Fuzzy Logic in Programming System." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498258.

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Wolfer, James. "Topical tapestry: Weaving threads of parallel programming, computer graphics, and artificial intelligence into undergraduate CS courses." In 2014 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2014. http://dx.doi.org/10.1109/educon.2014.6826219.

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Schmoll, Sebastian, and Matthias Schubert. "Dynamic Resource Routing using Real-Time Dynamic Programming." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/670.

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Acquiring available resources in stochastic environments becomes more and more important to future mobility. For instance, cities like Melbourne, Canberra and San Francisco install sensors that detect in real-time whether a parking spot (resource) is available or not. In such environments, the current state of the resources may be fully observable, although the future development is stochastic. In order to reduce the traffic, such cities want to fully exploit parking spots, such that the amount of searching cars is minimized. Thus, we formulate a problem setting where the expected seek time for each driver is minimized. This problem can be modeled by a Markov Decision Process (MDP) and solved using standard algorithms. In this paper, we focus on the setting, where pre-computation is not possible and search policies have to be computed on the fly. Our approach is based on state-of-the-art Real-Time Dynamic Programming (RTDP) approaches. However, standard RTDP approaches do not perform well on this specific problem setting as shown in our experiments. We introduce adapted bounds and approximations that exploit the specific nature of the problem in order to improve the performance significantly.
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Ene, Alexandru, and Cosmin Stirbu. "Automatic generation of quizzes for Java programming language." In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9042052.

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Reports on the topic "Artificial intelligence. Computer programming"

1

Fogel, Lawrence J., and David Fogel. Artificial Intelligence through Evolutionary Programming: Prediction and Identification. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada171544.

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Danner, William F. The use of artificial intelligence programming techniques for communication between incompatible building information systems. National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.ir.87-3529.

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Hofer, Martin, Tomas Sako, Arturo Martinez Jr., et al. Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200432-2.

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This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates, citing data from the Philippines and Thailand. Computer vision techniques were applied on publicly available medium resolution satellite imagery, household surveys, and census data from the two countries. The results suggest that even using publicly accessible satellite imagery, predictions generally aligned with the distributional structure of government-published poverty estimates after calibration. The study further examines the robustness of the resulting estimates to user-specified algorithmic parameters and model specifications.
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Rodriguez, Simon, Autumn Toney, and Melissa Flagg. Patent Landscape for Computer Vision: United States and China. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20200054.

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China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.
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Murdick, Dewey, Daniel Chou, Ryan Fedasiuk, and Emily Weinstein. The Public AI Research Portfolio of China’s Security Forces. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200057.

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New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.
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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve" the necessary actions for the body in the external environment, for example, to take a cup of coffee or turn on your favorite music. At the same time, an artificial neural network that analyzes the flow of nerve impulses must also be directed at the brain, trying to guess the body's needs at the moment with a minimum number of movements. The most important obstacle to further progress is the problem of biocompatibility, which has not yet been resolved. This is even more important than the number of electrodes and the power of the processors on the chip. When you insert a foreign object into your brain, it tries to isolate itself from it. This is a multidisciplinary topic not only for doctors and psychophysiologists, but also for engineers, programmers, mathematicians. Of course, the problem is complex and it will be possible to overcome it only with joint efforts.
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Ambitious Mashups: Reflections on a Decade of Cyberlearning Research. Digital Promise, 2020. http://dx.doi.org/10.51388/20.500.12265/105.

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This report reflects on progress from over eight years of research projects in the cyberlearning community. The community involved computer scientists and learning scientists who received NSF awards to investigate the design of more equitable learning experiences with emerging technology—focusing on developing the learning theories and technologies that are likely to become important within 5-10 years. In early 2020, the Center for Innovative Research in Cyberlearning's team analyzed the portfolio of past and current project in this community and convened a panel of experts to reflect on important trends and issues, including artificial intelligence and learning; learning theories; research methods; out-of-school-time learning; and trends at NSF and beyond.
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