A General Noological Framework

A number of issues are discussed in this chapter that build on previous discussions toward the characterization of a general noological framework. Spatial movement is used as an example to discuss the connection between the needs and motivations of a noological system and its attendant behaviors. It is shown how the alternative consequences of a problem solving process can be encoded in the counterfactual portion of a script – a knowledge chunk – to accelerate future problem solving. The idea of knowledge chunking is discussed in detail using a computer simulation of a micro-world. A noological system usually has more than one need that competes for attention. Anxiousness arises in the process of attempting to satisfy needs and the process of affective competition to resolve the attentional priority is illustrated using an example from the StarCraft game environment. Neuroscience research is reviewed to illustrate the current understanding in general brain architecture and it is shown how this can map nicely onto our general noological framework constructed from computational considerations.

[1]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[2]  W. T. Thach,et al.  Cerebellar Control of Posture and Movement , 2011 .

[3]  Jordan Grafman,et al.  Neural Correlates of Script Event Knowledge: a Neuropsychological Study Following Prefrontal Injury , 2005, Cortex.

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

[5]  A. Yuille,et al.  Object perception as Bayesian inference. , 2004, Annual review of psychology.

[6]  J. Grafman,et al.  Human prefrontal cortex: processing and representational perspectives , 2003, Nature Reviews Neuroscience.

[7]  J. Fuster Prefrontal Cortex , 2018 .

[8]  S. Grillner,et al.  Neural bases of goal-directed locomotion in vertebrates—An overview , 2008, Brain Research Reviews.

[9]  Hava T. Siegelmann,et al.  The Dynamic Universality of Sigmoidal Neural Networks , 1996, Inf. Comput..

[10]  Erik J. Peterson,et al.  Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling , 2010, NeuroImage.

[11]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[12]  Jaime G. Carbonell,et al.  Derivational Analogy and Its Role in Problem Solving , 1983, AAAI.

[13]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[14]  G. E. Alexander,et al.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.

[15]  John Nolte,et al.  The Human Brain An Introduction to Its Functional Anatomy , 2013 .

[16]  N. Andreasen,et al.  The Role of the Cerebellum in Schizophrenia , 2008, Biological Psychiatry.

[17]  Dileep George,et al.  Towards a Mathematical Theory of Cortical Micro-circuits , 2009, PLoS Comput. Biol..

[18]  Eytan Ruppin,et al.  Actor-critic models of the basal ganglia: new anatomical and computational perspectives , 2002, Neural Networks.

[19]  Apostolos P. Georgopoulos,et al.  Motor Functions of the Basal Ganglia , 2011 .

[20]  Richard Fikes,et al.  Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..

[21]  Kristian J. Hammond,et al.  Case-Based Planning: Viewing Planning as a Memory Task , 1989 .

[22]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[24]  Seng-Beng Ho,et al.  A grand challenge for computational intelligence a micro-environment benchmark for adaptive autonomous intelligent agents , 2013, 2013 IEEE Symposium on Intelligent Agents (IA).

[25]  Nikolaus R. McFarland,et al.  Striatonigrostriatal Pathways in Primates Form an Ascending Spiral from the Shell to the Dorsolateral Striatum , 2000, The Journal of Neuroscience.

[26]  Carol A. Seger,et al.  How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback , 2008, Neuroscience & Biobehavioral Reviews.

[27]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[28]  R. Douglas,et al.  Mapping the Matrix: The Ways of Neocortex , 2007, Neuron.

[29]  P. Strick,et al.  Basal ganglia and cerebellar loops: motor and cognitive circuits , 2000, Brain Research Reviews.

[30]  Seng-Beng Ho,et al.  Knowledge Representation, Learning, and Problem Solving for General Intelligence , 2013, AGI.

[31]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[32]  M. Sur,et al.  Experimentally induced visual projections into auditory thalamus and cortex. , 1988, Science.

[33]  Eduardo Sontag,et al.  Turing computability with neural nets , 1991 .

[34]  James C. Houk,et al.  Agents of the mind , 2005, Biological Cybernetics.

[35]  Dileep George,et al.  How the brain might work: a hierarchical and temporal model for learning and recognition , 2008 .

[36]  Kristian J. Hammond,et al.  Chapter 8 – Case-based Planning , 1989 .

[37]  J. Grafman,et al.  Structured event complexes in the medial prefrontal cortex support counterfactual representations for future planning , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[38]  James A. Hendler,et al.  Complexity results for HTN planning , 1994, Annals of Mathematics and Artificial Intelligence.

[39]  Allen Newell,et al.  Chunking in Soar: The anatomy of a general learning mechanism , 1985, Machine Learning.

[40]  Hava T. Siegelmann,et al.  Neural and Super-Turing Computing , 2003, Minds and Machines.

[41]  M. Hallett,et al.  Activation of the primary visual cortex by Braille reading in blind subjects , 1996, Nature.

[42]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[43]  A W Roe,et al.  Visual projections routed to the auditory pathway in ferrets: receptive fields of visual neurons in primary auditory cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[44]  Alan C. Evans,et al.  Abnormal basal ganglia outflow in Parkinson's disease identified with PET. Implications for higher cortical functions. , 1998, Brain : a journal of neurology.

[45]  A. Maslow Motivation and Personality , 1954 .

[46]  J. Houk On the role of the cerebellum and basal ganglia in cognitive signal processing. , 1997, Progress in brain research.

[47]  M. Gazzaniga,et al.  Cognitive Neuroscience: The Biology of the Mind , 1998 .

[48]  G. Allen,et al.  Cerebrocerebellar communication systems. , 1974, Physiological reviews.

[49]  Kevan A. C. Martin,et al.  A Canonical Microcircuit for Neocortex , 1989, Neural Computation.

[50]  V. Mountcastle,et al.  An organizing principle for cerebral function : the unit module and the distributed system , 1978 .

[51]  Joel L. Davis,et al.  Adaptive Critics and the Basal Ganglia , 1995 .

[52]  Jordan Grafman,et al.  The Human Prefrontal Cortex Stores Structured Event Complexes , 2008 .

[53]  M. Glickstein,et al.  Mossy-fibre sensory input to the cerebellum. , 1997, Progress in brain research.

[54]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[55]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[56]  C. Marsden,et al.  The behavioural and motor consequences of focal lesions of the basal ganglia in man. , 1994, Brain : a journal of neurology.

[57]  P. Strick,et al.  Cerebellum and nonmotor function. , 2009, Annual review of neuroscience.

[58]  Fiona Liausvia,et al.  Incremental Rule Chunking for Problem Solving , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

[59]  Hava T. Siegelmann,et al.  Analog computation via neural networks , 1993, [1993] The 2nd Israel Symposium on Theory and Computing Systems.

[60]  P. Strick,et al.  Activation of a cerebellar output nucleus during cognitive processing. , 1994, Science.

[61]  Oren Etzioni,et al.  PRODIGY: an integrated architecture for planning and learning , 1991, SGAR.

[62]  David H. Zald,et al.  The Orbitofrontal Cortex , 2008 .

[63]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[64]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[65]  D. Glanzman,et al.  Reinstatement of long-term memory following erasure of its behavioral and synaptic expression in Aplysia , 2014, eLife.

[66]  Eric Courchesne,et al.  Differential effects of developmental cerebellar abnormality on cognitive and motor functions in the cerebellum: an fMRI study of autism. , 2003, The American journal of psychiatry.

[67]  Kenji Doya,et al.  What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.

[68]  S. R. Y. Cajal The Croonian lecture.—La fine structure des centres nerveux , 1894 .