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About 4,297 papers for “Symbolic programming”
N Rochester · Transactions of the I R E Professional Group on Electronic Computers · 1953
Automatic calculators can be programmed to interpret programs which have been written with symbolic instead of actual addresses. This method allows the calculator to assume much of the clerical burden which must otherwise be borne by the programmer.
Cited by 13
SpringerReference · 2012
Cited by 6
Olga Skvortsova · 2007
A symbolic dynamic programming approach for solving first-order Markov decision processes within the situation calculus is presented. As an alternative specification language for dynamic worlds the fluent calculus is chosen and the fluent calculus formalization of the symbolic dynamic programming approach is provided. The major constructs of Markov decision processes such as the optimal value function and the policy are logically represented. The technique produces a set of first-order formulae that minimally partitions the state space. Consequently, the symbolic dynamic programming algorithm presented here does not require neither state nor action space enumeration, thereby solving the drawback of classical dynamic programming methods.
Cited by 0
U. Shmueli · 1988
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M. Balátě · Digital Repository (National Repository of Grey Literature) · 1999
Cited by 0
Vladislavleva, Catherine J. · Data Archiving and Networked Services (DANS) · 2005
Cited by 0
Isasa Martín, Carlos Ignacio · Biblos-e Archivo (Universidad Autónoma de Madrid) · 2021
Explainable artificial intelligence (XAI) is a field blooming right now. With the popularity of opaque systems, the need of explanation methods that shed light on how this systems works has risen as well. In this work, we propose the usage of symbolic machine learning systems as explanation methods, a line that is yet to be fully explored. We will do this by reviewing this symbolic systems, analyzing the existing taxonomies of explanation methods and fitting the systems within the taxonomies. Finally, we will also do some testing on solving numerical problems with symbolic systems.
Cited by 0Open access
Carlos Olarte, Frank D. Valencia · 2008
Cited by 0
Douglas A. Augusto, Hélio J. C. Barbosa · 2002
Presents an implementation of symbolic regression which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments which are summarized in the paper.
Cited by 109
Isolde Mazzucco · 2001
Cited by 2
K. E. J. W. Szameitat, Peter Wegner · Revue de l Institut International de Statistique / Review of the International Statistical Institute · 1964
Cited by 0
Simone Naldi, Daniel Plaumann · Journal of Algebra and Its Applications · 2017
Hyperbolic programming is the problem of computing the infimum of a linear function when restricted to the hyperbolicity cone of a hyperbolic polynomial, a generalization of semidefinite programming (SDP). We propose an approach based on symbolic computation, relying on the multiplicity structure of the algebraic boundary of the cone, without the assumption of determinantal representability. This allows us to design exact algorithms able to certify the multiplicity of the solution and the optimal value of the linear function.
Cited by 4Open access
Monteiro, Adriana Alexandra Lopes · Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa) · 2025
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Cited by 0Open access
Randall Balestriero · arXiv (Cornell University) · 2020
SymJAX is a symbolic programming version of JAX simplifying graph input/output/updates and providing additional functionalities for general machine learning and deep learning applications. From an user perspective SymJAX provides a la Theano experience with fast graph optimization/compilation and broad hardware support, along with Lasagne-like deep learning functionalities.
Cited by 4Open access
Mohamed Aliwi, Selçuk Aslan, Sercan Demіrcі · 2020
Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods.
Cited by 1
Baus, Andreas · Max Planck Institute for Plasma Physics · 2004
Cited by 0
Kwok, Ko Man · Mspace (University of Manitoba) · 1993
Cited by 0Open access
Alexander Frink · AIP conference proceedings · 2001
GiNaC is a free framework that embeds symbolic manipulation consistently into the C++ programming language. It deliberately neglects the split-up into a low level language and a high level language, traditional in the design of computer algebra systems. The user usually interacts with GiNaC directly in C++. GiNaC was designed to provide efficient handling of multivariate polynomials, algebras and some special functions that are needed for loop calculations in HEP. But it also bears some potential to become a more general purpose symbolic system.
Cited by 3
Richard J. Fateman · ACM SIGSAM Bulletin · 1981
To illustrate the difference between numeric and symbolic processing, consider a computer program (in FORTRAN, say) which given the quantities a , b , and c , can apply the quadratic formula to approximate the roots of the quadratic about 40 equation ax 2 +bx+c=0. The names a, b , and c must of course correspond to numerical values at run-time because the program has been written to provide numerical processing. If a had as its run-time value the expression q , b had value - pq - 1 and c had value p , the FORTRAN program would not be applicable. Nevertheless, by applying the quadratic formula symbolically, the two roots[EQUATION]can be represented. By further efforts, this expression can be reduced to the set of values p , 1/ q . This substitution (in this case, into the quadratic formula) and subsequent simplification are but two of the necessary operations in an algebra system. Some of the more elaborate facilities that can be built up (and have been, in some systems), bring us to the edge of research areas in several fields of mathematics, and present us with problems in representation and communication of "knowledge" which border on the hardest problems in computer "artificial intelligence" -- natural language processing, representation of complex data, heuristic programming.
Cited by 4
Henning Christiansen, Davide Martinenghi · Applied Artificial Intelligence · 2000
L ogic programming, with its declarative bias as well as unication and the direct representation of linguistic structures, is well qualied for meta programming, i.e., programs working with representations of other programs as their data.However, constraint techniques seem necessary in order to fully exploit this paradigm.In the DEMOII system, the language of constraint handling rules (CHRs) has been used in order to provide a functionality that appears difficult to obtain without such means.For example, reversibility of a meta interpreter, which can be obtained by means of constraints, turns it into a powerful program generator ; in the same way, negation as failure implemented by means of constraints provides an incremental evaluation of integrity constraints.T his paper focuses on the design of such constraints and their implementation by means of CHR.
Cited by 15Open access