In recent years, the development of Artificial Intelligence systems using neural network has been remarkable. However, this method has low explainability and is illogical. To solve this, there is an automatic programm...
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ISBN:
(纸本)9783031477201;9783031477218
In recent years, the development of Artificial Intelligence systems using neural network has been remarkable. However, this method has low explainability and is illogical. To solve this, there is an automatic programming method based on inductive inference. However, this method has also the problem of low versatility. In this study, we propose NP4G: Network programming for Generalization, which can automatically generate programs by inductive inference. Because the proposed method can realize "sequence", "selection", and "iteration" in programming and can satisfy the conditions of the structured program theorem, it is expected that NP4G is a method that automatically acquires any programs by inductive inference. As an example, we automatically construct a bitwise NOT operation program from several training data by generalization using NP4G. Although NP4G only randomly selects and connects nodes, by adjusting the number of nodes and the number of phase of "Phased Learning", we show the bitwise NOT operation programs are acquired in a comparatively short time and at a rate of about 7 in 10 running. The source code of NP4G is available on GitHub as a public repository.
automatic programming has been researched for a long time. A variety of methodologies have been proposed. However, they have limited applicability, or they can generate only a few lines of code. In this research, the ...
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ISBN:
(纸本)9781538664308
automatic programming has been researched for a long time. A variety of methodologies have been proposed. However, they have limited applicability, or they can generate only a few lines of code. In this research, the authors are trying to generate source code of Java methods based on their specifications. In this paper, we propose a reuse-based code generation technique with method signature and test cases. First, our technique searches existing Java methods whose signature are the same as the one input by a user. Then, our technique reworks each of them by using test cases input by the user. Methods passing all the test cases are given to the user. At this moment, the authors have implemented a naive prototype and conducted experiments with four open source software. In total, our technique succeeded to generate 18 Java methods. In this paper, we also introduce some actual examples of generated Java methods and some ideas to enhance our technique.
The Colebrook equation, which calculates the flow friction, is used to calculate pressure loss in ventilation ducts with turbulent flow, pipes with water or oil. The computational complexity of the equation increases ...
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ISBN:
(数字)9781665450928
ISBN:
(纸本)9781665450928
The Colebrook equation, which calculates the flow friction, is used to calculate pressure loss in ventilation ducts with turbulent flow, pipes with water or oil. The computational complexity of the equation increases when the friction factor occurs on both sides of the equation. In this study, a new Colebrook approach to compute flow friction with lower cost is proposed based on the Immune Plasma programming (IPP) automatic programming method based on the stages of immune plasma therapy. The success of IPP was compared with Artificial Bee Colony programming (ABCP), quick ABCP, semantic ABCP, quick semantic ABCP. The simulation results show that IPP can be used to effectively solve real-world problems.
This paper discusses the automatic generation of programs by adapting the construction process to the user currently interacting with the program. A class of such systems is investigated where such generation process ...
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ISBN:
(纸本)3540278850
This paper discusses the automatic generation of programs by adapting the construction process to the user currently interacting with the program. A class of such systems is investigated where such generation process is continuously repeated making the program design and implementation evolve according to user behaviour. By leveraging on existing technologies (software generation facilities, modelling languages, specific and general standard meta-models) an experimental proof of concept system that is able to generate itself while interacting with the user is introduced and tested. The findings are discussed and a general organization for this class of adaptive systems is briefly proposed and compared with existing literature.
End users can benefit from automatic program synthesis in a variety of applications, many of which require the user to specify the program they would like to generate. Recent advances in genetic programming allow it t...
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ISBN:
(纸本)9798400701207
End users can benefit from automatic program synthesis in a variety of applications, many of which require the user to specify the program they would like to generate. Recent advances in genetic programming allow it to generate general purpose programs similar to those humans write, but require specifications in the form of extensive, labeled training data, a barrier to using it for user-driven synthesis. Here we describe the prototype of a human-driven genetic programming system that can be used to synthesize programs from scratch. In order to address the issue of extensive training data, we draw inspiration from counterexample-driven genetic programming, allowing the user to initially provide only a few training cases and asking the user to verify the correctness of potential solutions on automatically generated potential counterexample cases. We present anecdotal experiments showing that our prototype can solve a variety of easy program synthesis problems entirely based on user input.
Self-assembly is a ubiquitous process in nature in which a disordered set of components autonomously assemble into a complex and more ordered structure. Components interact with each other without the presence of cent...
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Self-assembly is a ubiquitous process in nature in which a disordered set of components autonomously assemble into a complex and more ordered structure. Components interact with each other without the presence of central control or external intervention. Self-assembly is a rapidly growing research topic and has been studied in various domains including nano-science and technology, robotics, micro-electro-mechanical systems, etc. Software self-assembly, on the other hand, has been lacking in research efforts. In this research, I introduced Automated Self-Assembly programming Paradigm (ASAP ), a software self-assembly system whereby a set of human made components are collected in a software repository and later integrated through self-assembly into a specific software architecture. The goal of this research is to push the understanding of software self-assembly and investigate if it can complement current automatic programming approaches such as Genetic programming. The research begins by studying the behaviour of unguided software self-assembly, a process loosely inspired by ideal gases. The effect of the externally defined environmental parameters are then examined against the diversity of the assembled programs and the time needed for the system to reach its equilibrium. These analysis on software self-assembly then leads to a further investigation by using a particle swarm optimization based embodiment for ASAP. In addition, a family of network structures is studied to examine how various network properties affect the course and result of software self-assembly. The thesis ends by examining software self-assembly far from equilibrium, embedded in assorted network structures. The main contributions of this thesis are: (1) a literature review on various approaches to the design of self-assembly systems, as well as some popular automatic programming approaches such as Genetic programming; (2) a software self-assembly model in which software components move and intera
XIS1 is a R&D project which main mission is to analyze, develop and evaluate mechanisms and tools to produce information systems from a more abstract, high-level, efficient and productive way than it is done curre...
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ISBN:
(纸本)0769520200
XIS1 is a R&D project which main mission is to analyze, develop and evaluate mechanisms and tools to produce information systems from a more abstract, high-level, efficient and productive way than it is done currently. XIS project is influenced by MDA reference model, and is mainly based on three principles: namely high-level models specification;generative programming techniques;and it is component-based architecture-centric. XIS is not a conceptual research plan, it is a working on project with concrete results and produced systems. In this paper we detail the generative programming techniques used in the XIS project as well as the discussions and main decisions tackled on. Finally, we present the main conclusions, the relationship between XIS and MDA and the work that will be handled in the near future.
This paper describes a probability based genetic programming (GP) approach to multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output ...
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ISBN:
(纸本)3540228179
This paper describes a probability based genetic programming (GP) approach to multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. The approach is examined on three multiclass object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. While the area measure was a bit more effective than the distance measure in most cases, the distance measure was more efficient to learn good program classifiers.
In recent years, Cartesian Ant programming (CAP) has been proposed as a swarm-based automatic programming method, which combines graph representations in Cartesian Genetic programming with search mechanism of Ant Colo...
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ISBN:
(纸本)9781509027750
In recent years, Cartesian Ant programming (CAP) has been proposed as a swarm-based automatic programming method, which combines graph representations in Cartesian Genetic programming with search mechanism of Ant Colony Optimization. In CAP, once an ant jumps a number of nodes, the skipped nodes are not utilized and wasted in search. To make the use frequency of nodes uniform, we propose CAP with transition rule considering internode distance. We focus on the distance at the beginning of search to utilize a large number of nodes for exploration of search. As the search proceeds, the search comes to depend on the pheromone information for exploitation of search. In addition, to prevent the excessive use of the unnecessary nodes, we modify the method of dynamic symbol assignments to nodes so that not only functional symbols but also terminal symbols can be assigned to the respective nodes. We examined the effectiveness of our proposed method by applying it to symbolic regression problems. From the experimental results, we confirmed the improvement of performance and the relief of bias in use frequency of nodes by our proposed method.
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