A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

Castanedo, Federico; García, Juan Francisco Blanco; Patricio, Miguel Á.; Molina, José Manuel

doi:10.1007/s10846-010-9448-1

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…The most applications can be found in the ield of image coregistration and image fusion [24][25][26]. In the ield of object detection and delineation reported applications are still rare [27][28][29][30][31]-even more in the ield of remote sensing image analysis [32][33][34][35].…”

Section: Agent-based Image Analysismentioning

confidence: 99%

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Hofmann¹

2017

Modern Fuzzy Control Systems and Its Applications

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Recent methods of image analysis in remote sensing lack a suicient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal efects, diferent locations, atmospheric conditions, or even a slightly diferent sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents' behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm.Keywords: agent-based image analysis, fuzzy believe-desire-intention model, object-based image analysis, fuzzy control system, remote sensing IntroductionAnalyzing remote sensing data is strongly bound to methods of image processing and image analysis. In contrast to other imaging techniques, remote sensing as per deinition is a method to acquire information about the earth's surface by detecting and analyzing its relected or emited electromagnetic radiation and without being in direct contact with it. Besides radiation © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.of the visual spectrum also infrared (optical data) and microwave radiation (RADAR) is used to produce remote sensing images. The remote sensing instruments can be carried by space crafts (usually satellites) or airborne vehicles (airplanes, drones, etc.). In order to gather geo-information from remote sensing data, the produced images need to be analyzed, that is, preprocessed and classiied. In this context, image classiication means to assign pixels to meaningful object classes of the earth's surface, whereas the delineated and classiied objects are inally stored in a geographic information system (GIS) as polygons, lines, or points ( vector model). With the continuous increase of remote sensing images' spatial (and radiometric) resolution, image analysis in remote sensing became more and more complex. Until...

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Section: Agent-based Image Analysismentioning

confidence: 99%

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Hofmann¹

2017

Modern Fuzzy Control Systems and Its Applications

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“…Moreover, they can only reproduce the actions that users have frequently executed in the past and in the same manner that the users have executed them. The work of Castanedo et al [12] proposes a multi-agent system that follows a Belief-Desire-Intention model to achieve rational actions based on human reason in an ambient intelligent system. Other works, such as the ones presented in [9] and [10], propose programming event-conditionaction rules that trigger the sequential execution of actions when a certain context event is produced.…”

Section: Related Workmentioning

confidence: 99%

“…When the engine detects a context change, it queries the task model to check which routines must be executed in the new context. Both the task model and the obtrusiveness model are represented in XML Metadata Interchange standard (XMI) 12 . To query these models at runtime, the engine uses the Eclipse Modelling Framework Model Query (EMFMQ) 13 plugin.…”

Section: Implementation Detailsmentioning

confidence: 99%

Designing for user attention: A method for supporting unobtrusive routine tasks

Gil¹,

Serral²,

Valderas³

et al. 2013

Science of Computer Programming

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The automation of user routine tasks is one of the most important challenges in the development of Ambient Intelligence systems. However, this automation may be annoying since some tasks may grab users attention in inappropriate situations. Since user attention is a valuable resource, task automation must behave in a considerate manner demanding user attention only when it is required. To address this issue, this work presents a systematic method for supporting the design and automation of unobtrusive routine tasks that can adjust their obtrusiveness level at runtime according to the user attentional resources and context. This method proposes to design the routine tasks that the system must carry out and how they must interact with users in terms of obtrusiveness. The method also provides a software infrastructure that makes the execution of the tasks at the appropriate obtrusiveness degree a reality. Finally, the system has been validated by means of usefulness and performance tests and a practical case study that demonstrates the correctness and applicability of our approach without compromising system performance.

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“…As one of the main MASs, the belief-desire-intention (BDI) model has been investigated with the aim of eliciting more complex mental attitudes in AI systems (Mikic Fonte, Burguillo, and Nistal 2012;Tsai and Pan 2011;Castanedo et al 2011;Mukun and Kiang 2012).…”

Section: Introductionmentioning

confidence: 99%

Building a Belief–Desire–Intention Agent for Modeling Neural Networks

Huang

Chen

Jiang

2015

Applied Artificial Intelligence

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This article presents an innovative learning technique for modeling nonlinear systems. Our belief-desire-intention algorithm for neural networks can effectively identify the parameters of most relevance to a model for the online adjustment of weights, neurons, and layers. We present a detailed explanation of each component in the proposed agent, and successfully apply our model to describe the lateral forces on a tire under a range of test conditions. The model output is compared to test data and the output of an existing neural network model. Our results demonstrate that the belief-desireintention agent is reliable and applicable in nonlinear modeling and is superior to backpropagation neural networks.

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A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

Cited by 11 publications

References 30 publications

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Designing for user attention: A method for supporting unobtrusive routine tasks

Building a Belief–Desire–Intention Agent for Modeling Neural Networks

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