Modified Shared Circuits Model for Manufacturing Processes Control: - From Psychology and Neuroscience to a Computational Solution

There are many complex processes waiting for artificial cognitive solutions able to deal with new, complex, unknown, or arbitrary tasks efficiently. In this work, the modified shared circuits model (MSCM) for artificial cognitive control is presented. The main goal is to surpass the limitations of the shared circuits models and to formalize an integrated computational solution on the basis of a neuroscientific and psychological approach. Two novelties of the proposed systems are a commutation or switching mechanism between modules in order to reproduce efficiently the imitation, deliberation and mindreading characteristics of human sociocognitive skills. Another contribution is the introduction of a self-optimization strategy based on cross entropy in order to fulfil the control goals. The closed-loop behaviour of the drilling force demonstrates that the MSCM approach is an alternative and feasible option in the field of artificial cognitive control to deal with processes complexity and uncertainty.

[1]  Takaki Makino Failure, instead of inhibition, should be monitored for the distinction of self/other and actual/possible actions , 2008 .

[2]  Lotfi A. Zadeh Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008 .

[3]  M. Ziessler,et al.  Cognitive control of action: The role of action effects , 2004, Psychological research.

[4]  Pierre-Yves Glorennec,et al.  Tuning fuzzy PD and PI controllers using reinforcement learning. , 2010, ISA transactions.

[5]  A. Sánchez Boza,et al.  A FIRST APPROACH TO AN ARTIFICIAL NETWORKED COGNITIVE CONTROL SYSTEM BASED ON THE SHARED CIRCUITS MODEL OF SOCIOCOGNITIVE CAPACITIES , 2011 .

[6]  N. Meiran Modeling cognitive control in task-switching , 2000, Psychological research.

[7]  Linda B. Smith,et al.  A Dynamic Systems Approach to the Development of Cognition and Action , 2007, Journal of Cognitive Neuroscience.

[8]  Sisir Roy,et al.  The ‘prediction imperative’ as the basis for self-awareness , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  George J. Vachtsevanos,et al.  Software technology for implementing reusable, distributed control systems , 2003 .

[10]  Dirk P. Kroese,et al.  The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics) , 2004 .

[11]  Yoseph Bar-Cohen,et al.  Biomimetics : Biologically Inspired Technologies , 2011 .

[12]  Hao Ying,et al.  Fuzzy Control and Modeling: Analytical Foundations and Applications , 2000 .

[13]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[14]  S. Hurley The shared circuits model (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. , 2008, The Behavioral and brain sciences.

[15]  Dirk P. Kroese,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning , 2004 .

[16]  Graham C. Goodwin,et al.  Control System Design , 2000 .

[17]  A. M. Gajate,et al.  Internal Model Control Based on a Neurofuzzy System for Network Applications. A Case Study on the High-Performance Drilling Process , 2009, IEEE Transactions on Automation Science and Engineering.

[18]  Zenon W. Pylyshyn,et al.  Computation and Cognition: Toward a Foundation for Cognitive Science , 1984 .

[19]  Agustín Gajate,et al.  Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach , 2011, Eng. Appl. Artif. Intell..

[20]  Rodolfo E. Haber,et al.  Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process , 2010, Inf. Sci..

[21]  Cecilia Heyes Imitation as a conjunction , 2008 .

[22]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[23]  Robert Babuska,et al.  Neuro-fuzzy methods for nonlinear system identification , 2003, Annu. Rev. Control..

[24]  Masao Ito Control of mental activities by internal models in the cerebellum , 2008, Nature Reviews Neuroscience.

[25]  Richard L. Lewis,et al.  A computational unification of cognitive behavior and emotion , 2009, Cognitive Systems Research.