“…Large-scale infrastructures such as healthcare monitoring are an ideal candidate for the holonic model, as service orientation is focused on a patient-centric environment [12], [14], hence the motivation of utilizing a BDI-based agent holonic infrastructure to model the patient's needs and concerns as an individual agent-based holon. The agent's desires and intentions are captured and realized within the holon entities in practice, so that the sensory determination is executed in a real-time task control process.…”
Section: A Basis Of Bdi-based Holonic Infrastructures For Sanetsmentioning
Abstract-This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirectmapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.
“…Large-scale infrastructures such as healthcare monitoring are an ideal candidate for the holonic model, as service orientation is focused on a patient-centric environment [12], [14], hence the motivation of utilizing a BDI-based agent holonic infrastructure to model the patient's needs and concerns as an individual agent-based holon. The agent's desires and intentions are captured and realized within the holon entities in practice, so that the sensory determination is executed in a real-time task control process.…”
Section: A Basis Of Bdi-based Holonic Infrastructures For Sanetsmentioning
Abstract-This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirectmapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.
“…However, multidimensional problems outside of Euclidean space are sacrificed as a result, inclusive of network interference and ensuring energy efficiency. By not factoring the domain concern of including the problem set into a singular process model, the problem scope increases exponentially [1]. The solution is to transform the views and decompose them into a singular map of the highest activation energies or stimuli, which is achieved once the maximum value of the integration matrix is calculated for all EKMs for each of the input stimuli received from the SANET's environment.…”
Section: A Cooperative Ekms With Agent-based Architecturesmentioning
confidence: 99%
“…It is designed to bind a compatibility space and allow legacy devices to interface with the environment. SANETs architectures for healthcare monitoring need to be service-oriented for composition, configuration and integration so the application can be designed efficiently from the data model, so the biomimetic model perspective can be projected onto a three dimensional space based on the POE Classification Model [1][2] [20] as elaborated in Table 1. The POE model represents the different levels of organization, with POE standing for Phylogeny, Ontogeny and Epigenesis.…”
Section: A Basis Of Bdi-based Agent Infrastructures For Sanetsmentioning
“…1. Tuple Space paradigm consists of distributed Memory Spaces overlayed on multiple platforms [1] As shown in Fig. 2 SANET Correlation: This space corresponds to the facets or aspects of knowledge acquisition by responding to stimuli and weights adaption feedback.…”
Section: Vertical Models Of Domain Paradigmmentioning
confidence: 99%
“…As shown in Fig. 1, distributed data space concerns are addressed through Tuple Space [2] implementations supported in modern component middleware systems, following the multi-verse concepts of elemental slicing of a core entity of representing the structures and Sensor Actor Network Modeling utilizing the Holonic Architectural Framework Christopher Chiu, Zenon Chaczko, Perez Moses C interconnections between internal entities [1]. This provides users with a specific set of capabilities:…”
Section: A Theoretical Principles Of Holonic Architecturesmentioning
Abstract-This paper discusses the results of utilizing advanced EKM modeling techniques to manage Sensor-Actor networks (SANETs) based upon the Holonic Architectural Framework. EKMs allow a quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize from a given input space to administer SANET routing and clustering functions with a control parameter space. Results demonstrate that in comparison to linear approximation techniques, indirect mapping with EKMs provide fluid control and feedback mechanisms by operating in a continuous sensory control space -thus enabling interactive detection and optimization of events in real-time environments.
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