Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

Abstract

Keywords (1) (2008) 1 -22 is enriched and improved in this work. A five-layer computational architecture for designing artificial cognitive control systems is proposed on the basis of a modified shared circuits model for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificial cognitive control system is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approach.

An Approach to Fuzzy Modeling of Electromagnetic Actuated Clutch Systems

“…Moreover, the convergence of the modeling error to zero can be achieved by the optimization of the parameters of several parameters of the fuzzy models including input membership functions or parameters in the rule consequents. Various optimization algorithms can be implemented in this context [28], [29], [30], [31], [32], [33], [34], [35], [36]. …”

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

“…MSCM embodied a computational infrastructure that is plausible from a neuroscientific and psychological perspective, but which lacks a generalizable approach with optimization and learning mechanisms. More details about the five modules and the overall performance can be found in [3]. The main drawbacks are:…”

“…The authors noted that self-optimizing and self-learning control systems are a crucial factor for cognitive systems and identified important gaps such as the individual worker internal model. Sanchez-Boza et al [3] proposed an artificial cognitive control architecture based on the shared circuit model (SCM). Its main drawback is a lack of systematic procedures for learning and optimization in the proposed five-layer architecture.…”

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

“…Obstacle recognition multi-layer architectures to represent the common patterns of roads, lane markings, traffic signals, vehicles, pedestrians and so on are a priority line for researchers and car fabricants ( Figure 1 ). Nowadays, many classifiers rely on machine-learning approaches to exploit data redundancy and abundance to find out patterns, trends and relations amongst input attributes and class labels [ 8 , 9 , 10 ]. Within obstacle-recognition techniques, vector support machines have been widely applied for classification and regression problems [ 11 ].…”