Organization/fuzzy Approach to the CTO Problem

Andrade, João Sousa; Oliveira, Robson Medrado de; Silva, Thayanne F. da; Maia, José Everardo Bessa; Campos, Gustavo Augusto Lima de

doi:10.1109/bracis.2018.00083

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…To apply the FCM in CTO, similar to [7] with K-means, as proposed in [16] but without self-tuning of parameters, we consider the observers to be centers and the targets to be Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation data points and observers' destinations as the centers of the clusters. The initial cluster centers are the current positions of the observers.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation

Andrade¹,

Maia²,

Campos³

2021

RITA

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Clustering on target positions is a class of centralized algorithms used to calculate the surveillance robots' displacements in the Cooperative Target Observation (CTO) problem. This work proposes and evaluates Fuzzy C-means (FCM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with K-means (DBSk) based self-tuning clustering centralized algorithms for the CTO problem and compares its performances with that of K-means. Two random motion patterns are adopted for the targets: in free space or on a grid. As a contribution, the work allows identifying ranges of problem configuration parameters in which each algorithm shows the highest average performance. As a first conclusion, in the challenging situation in which the relative speed of the targets is high, and the relative sensor range of the surveillance is low, for which the existing algorithms present a substantial drop in performance, the FCM algorithm proposed outperforms the others. Finally, the DBSk algorithm adapts very well in low execution frequency, showing promising results in this challenging situation.

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Section: Fuzzy C-meansmentioning

confidence: 99%

Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation

Andrade¹,

Maia²,

Campos³

2021

RITA

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“…We recently improved the approach based on k-means. We introduced the notion of an organization in the team to model its functional, structural, especially the hierarchy structure, and behavioral dimensions, which must be present in a rational team of observers [Andrade et al 2018].…”

Section: Literature Reviewmentioning

confidence: 99%

Classifying Organizational Structures on Targets in the Cooperative Target Observation

Silva¹,

Araújo²,

Raimundo³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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This paper proposes an approach to classify the organizational structure of a group of moving target agents that are continuously monitored by a smaller group of moving observer agents. The approach considers that the group of target agents can organize themselves according to eight different paradigms. The agents communicate through the exchange of messages whose contents are speech-act performative. We evaluate the approach considering seven techniques to solve the problem of classifying the group of target agents' organizational structure. The results show that the approach is promising, as it obtained a good performance, measured during experiments using agent-based simulations.

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