Random Forest Classification with MapReduce in Holonic Multiagent Systems

Cullinan, Michéle; Coulter, Duncan

doi:10.1007/978-3-030-82193-7_31

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…A nested hierarchy of holons is referred to as a holarchy. Ongoing work involving the implementation of holonic multi-agent systems which build upon these ideas using the Akka framework's actor systems to realize holarchies of multi-agent systems has been explored (Cullinan & Coulter, 2021). Although complete details of the work can only be made fully known following the publication of the work the basic concept is that the self-similar nature of the widely used Map Reduce approach to structuring Big Data solutions is exploited to create a holonic multi-agent system on the Akka platform.…”

Section: Holonic Multi-agency and Holarchiesmentioning

confidence: 99%

The convergence of computational thinking, computational intelligence, and multi-agency

Coulter

2023

JID

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The design of real-world industrial systems is subject to a natural tendency towards modularization in order to manage complexity. In addition, this article considers that patterns of self-similarity in many problem domains have made many such solutions naturally representable as holarchies. Likewise, the increasing need for autonomous local decision making as well as the demand to produce solutions at scale has increased the relevance of the multi-agent paradigm to the creation of modern software systems. A variety of software development patterns are explored for their compatibility with holonic multi-agency. The current skill sets required by software development workers and concomitant training activities focus on instilling computational thinking abilities, a set of related cognitive competencies useful in the development of such systems. Intelligent systems play an increasingly important role in modern development and often benefit from computational intelligence techniques for the purpose of parameter tuning. This position paper explores the intersections between holonic multi-agency, modern information systems development, the computational intelligence which train them and the computational thinking skills those developers should be trained in.

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Section: Holonic Multi-agency and Holarchiesmentioning

confidence: 99%

The convergence of computational thinking, computational intelligence, and multi-agency

Coulter

2023

JID

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“…Decision trees, neural networks, support vector machines, random forests, and other traditional machine learning algorithms are examples. This article proposes applying the RF model [ 12 , 13 ] to natural landscape animation design in order to optimize the effect of natural landscape design. This article's main contribution is as follows: RF is used to train a learning model with user evaluation as the classification result in order to guide the automatic design of natural landscape animations that satisfy users.…”

Section: Introductionmentioning

confidence: 99%

Application of a Random Forest Algorithm in Natural Landscape Animation Design

Zhao¹,

Zhang

2022

Computational Intelligence and Neuroscience

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Natural landscape simulation is one of the most popular research contents in computer graphics in the field of research simulation system. The natural landscape animation scene can immerse viewers in the scene, and it is widely used in visual simulation systems. Simulating natural scenery on a computer is a powerful method for studying the rules of the scenery's growth process as well as the mystery of life. The simulation of natural scenery is of particular importance and has far-reaching implications. The most important aspect of optimizing natural landscape design is creating a natural landscape animation that users enjoy. This article proposes a natural landscape animation design method with a self-learning function based on this concept. The random forest model (RF) is introduced in this method and applied to the entire animation design process. RF can generate a learning model with user evaluation as the classification result to guide the automatic design of natural landscape animation, resulting in user-satisfying animations. Simultaneously, the RF-based natural landscape animation design can continuously update the learning model based on user needs and is self-learning. The experimental part of this article verifies the effectiveness of the natural landscape animation design proposed in this article by comparing the selection rate of user satisfaction and dissatisfaction scenes, and further demonstrates that the method in this article can improve the natural landscape. The market application value of user satisfaction generated by animation is high.

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A Holonic Homoiconic Agent-Oriented Patricia Trie

Cullinan,

Coulter

2023

Lecture Notes in Networks and Systems

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Random Forest Classification with MapReduce in Holonic Multiagent Systems

Cited by 3 publications

References 9 publications

The convergence of computational thinking, computational intelligence, and multi-agency

The convergence of computational thinking, computational intelligence, and multi-agency

Application of a Random Forest Algorithm in Natural Landscape Animation Design

A Holonic Homoiconic Agent-Oriented Patricia Trie

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