Two-Layered Confabulation Architecture for an Artificial Creature's Behavior Selection

Kim, Jong-Hwan; Cho, Se-Hyoung; Kim, Ye-Hoon; Park, Inwon

doi:10.1109/tsmcc.2008.2001576

Cited by 14 publications

(3 citation statements)

References 13 publications

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“…In [22], [23] authors address the segmentation and classification of natural landscape images primarily for the purpose of wildfire smoke detection by using Cogent Confabulation Classifier. Furthermore, the authors in [24] suggest using a two-layered confabulation architecture for artificial creatures to select appropriate behaviours. They implemented an arbiter that decides on the most appropriate behaviour among suggested behaviours from the two confabulation layers.…”

Section: Algorithmsmentioning

confidence: 99%

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Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

Ivanda

Šerić

Braović

et al. 2023

CIT. J. comput. inf. technol

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The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool.

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Section: Algorithmsmentioning

confidence: 99%

“…• and the factor C is a positive constant, where by using it cogency can be maximised by maximising the confabulation product [24].…”

Section: Cogent Confabulationmentioning

confidence: 99%

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

Ivanda

Šerić

Braović

et al. 2023

CIT. J. comput. inf. technol

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show abstract

“…The core intelligence of Ubibot is constituted by Sobot, which is able to recognize the prevalent situation, make decisions on the course of action and implement them without directly consulting the user each time [16,19]. Due to the diversity and complexity of such system, it has a continuous interface between physical world and the virtual world for greater convenience and flexibility in user interactions and communications.…”

Section: Software Robot: Sobotmentioning

confidence: 99%

Multi-Layer Architecture of Ubiquitous Robot System for Integrated Services

Kim

Jeong

Park

et al. 2008

Int J of Soc Robotics

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Ubiquitous robot consists of various software and hardware platforms with various functions with different interfaces. In practical sense, it is impossible for software agents to hold all information related to other sensors or mobile robots. This makes it difficult to develop software agents and, at the same time, implement both modularity and scalability for the entire system. This paper proposes a novel multi-layer architecture to address the problems of interoperability between different hardware and software platforms in an ubiquitous environment offering services to the user regardless of space or time constraints. The proposed architecture is composed of five layers, which are classified according to device and environment independency, for modularity, scalability and interoperability between different hardware and software platforms. To show the general feasibility of the proposed architecture, this paper presents case studies in a simplified environment by computer simulation and experiments.

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Motivation and Context-Based Multi-Robot Architecture for Dynamic Task, Role and Behavior Selections

Lee

Kim

2009

Advances in Robotics

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Two-Layered Confabulation Architecture for an Artificial Creature's Behavior Selection

Cited by 14 publications

References 13 publications

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

Multi-Layer Architecture of Ubiquitous Robot System for Integrated Services

Motivation and Context-Based Multi-Robot Architecture for Dynamic Task, Role and Behavior Selections

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