The No Free Lunch Theorem and hypothesis of instinctive animal behavior

The problem of knowledge acquisition in animals is considered from the point of view of cybernetics. We show that all typesof animal behavior can be consistently explained on the basis of innate behavior programs and the creation of new behaviorprograms is logically inconsistent. The hypothesis that all animal behavior is completely innate is proposed. As a possiblephysical implementation of the storage of congenital programs, we considered quantum entanglement of biologically importantmolecules. Experiments to test hypotheses are proposed.

[1]  Lev Tsitolovsky.  A Model of Motivation with Chaotic Neuronal Dynamics , 1997 .

[2]  S. Shettleworth.  Cognition, evolution, and behavior, 2nd ed. , 2010 .

[3]  Melanie Keller,et al.  The Music Of Life , 2016 .

[4]  Joshua B. Tenenbaum,et al.  One-Shot Learning with a Hierarchical Nonparametric Bayesian Model , 2011, ICML Unsupervised and Transfer Learning.

[5]  J. Eccles,et al.  Quantum aspects of brain activity and the role of consciousness. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Jack A Tuszynski,et al.  Implications of quantum metabolism and natural selection for the origin of cancer cells and tumor progression. , 2012, AIP advances.

[7]  M. Bouton.  Learning and Behavior: A Contemporary Synthesis , 2006 .

[8]  J. Fodor,et al.  The Modularity of Mind: An Essay on Faculty Psychology , 1984 .

[9]  P. Davies,et al.  Does quantum mechanics play a non-trivial role in life? , 2004, Bio Systems.

[10]  Aleksei V. Melkikh.  First principles of probability theory and some paradoxes in modern biology (comment on , 2011 .

[11]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[12]  Yi Hong,et al.  Learning Assignment Order of Instances for the Constrained K-Means Clustering Algorithm , 2009, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Plamen L. Simeonov,et al.  Integral biomathics: a post-Newtonian view into the logos of bios. , 2007, Progress in biophysics and molecular biology.

[14]  Aude Billard,et al.  From Animals to Animats , 2004 .

[15]  Gerhardt von Bonin,et al.  Cybernetics or control and communication in the animal and the machine: Norbert wiener, 1948. 194 pp. New York: John Wiley & Sons, Inc. Paris: Hermann et cie , 1949 .

[16]  B. Bernstein,et al.  Animal Behavior , 1927, Japanese Marine Life.

[17]  R. R. Miller,et al.  Assessment of the Rescorla-Wagner model. , 1995, Psychological bulletin.

[18]  D. Noble.  The music of life : biology beyond genes , 2008 .

[19]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[20]  Kumpati S. Narendra,et al.  Learning Automata - A Survey , 1974, IEEE Trans. Syst. Man Cybern..

[21]  Martin V. Butz,et al.  Internal Models and Anticipations in Adaptive Learning Systems , 2003, ABiALS.

[22]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[23]  Scott Aaronson,et al.  Guest Column: NP-complete problems and physical reality , 2005, SIGA.

[24]  Jack Tuszynski,et al.  Conduction pathways in microtubules, biological quantum computation, and consciousness. , 2002, Bio Systems.

[25]  J. Fodor.  The Modularity of mind. An essay on faculty psychology , 1986 .

[26]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[27]  Alexey V. Melkikh,et al.  Congenital Programs of the Behavior as the Unique Basis of the Brain Activity , 2007 .

[28]  Herbert A. Simon,et al.  WHY SHOULD MACHINES LEARN , 1983 .

[29]  George Luger,et al.  Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition) , 2004 .

[30]  Jean-Arcady Meyer,et al.  Learning reactive and planning rules in a motivationally autonomous animat , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[31]  Yiyu Yao,et al.  Web Intelligence Meets Brain Informatics , 2006, WImBI.

[32]  Setsuo Ohsuga,et al.  Intelligence for Upgrading Information , 2006, WImBI.

[33]  Genrikh R. Ivanitskii,et al.  Remembering a random choice kills alternatives(reply to comment on , 2011 .

[34]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[35]  Gordon Pask,et al.  The Cybernetics of Human Learning and Performance , 1977 .

[36]  Abir U Igamberdiev,et al.  Quantum computation, non-demolition measurements, and reflective control in living systems. , 2004, Bio Systems.

[37]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[38]  Mihai Nadin,et al.  Not Everything We Know We Learned , 2003, ABiALS.

[39]  J. Searle,et al.  Is the brain's mind a computer program? , 1990, Scientific American.

[40]  M. L. Tsetlin.  FINITE AUTOMATA AND MODELS OF SIMPLE FORMS OF BEHAVIOUR , 1963 .

[41]  Genrikh R. Ivanitskii,et al.  21st century: what is life from the perspective of physics? , 2010 .

[42]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[43]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[44]  Y. Ho,et al.  Simple Explanation of the No-Free-Lunch Theorem and Its Implications , 2002 .

[45]  Martin V. Butz,et al.  Anticipatory Behavior in Adaptive Learning Systems , 2003, Lecture Notes in Computer Science.

[46]  K Matsuno,et al.  Cell motility as an entangled quantum coherence. , 1999, Bio Systems.

[47]  Peter Dittrich,et al.  Cells as semantic systems. , 2011, Biochimica et biophysica acta.

[48]  I. Pavlov.  Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex , 1929 .

[49]  Daniel Calvelo,et al.  Think!: A unified numerical-symbolic knowledge representation scheme and reasoning system , 2000, Artif. Intell..

[50]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[51]  Alexey V. Melkikh,et al.  Congenital programs of the behavior and nontrivial quantum effects in the neurons work , 2014, Biosyst..

[52]  Bernard Kamsu-Foguem,et al.  User-centered visual analysis using a hybrid reasoning architecture for intensive care units , 2012, Decision Support Systems.

[53]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[54]  S. A. Barnett,et al.  Learning and instinct in animals. , 1957 .

[55]  M. Reuter,et al.  Genetically Determined Differences in Learning from Errors , 2007, Science.

[56]  F. Bloom,et al.  Brain, Mind, and Behavior , 1985 .

[57]  Alexey V. Melkikh,et al.  Quantum information and the problem of mechanisms of biological evolution , 2014, Biosyst..

[58]  D. Glanzman,et al.  Common Mechanisms of Synaptic Plasticity in Vertebrates and Invertebrates , 2010, Current Biology.

[59]  R. Cattell.  The description of personality: basic traits resolved into clusters. , 1943 .

[60]  David G. Stork,et al.  Pattern Classification , 1973 .

[61]  B. Carter.  Large Number Coincidences and the Anthropic Principle in Cosmology , 1974 .

[62]  Alexey V. Melkikh,et al.  Biological complexity, quantum coherent states and the problem of efficient transmission of information inside a cell , 2013, Biosyst..

[63]  S. Z. Cardon,et al.  Introduction to a Biological Systems Science , 1971 .

[64]  David H. Wolpert,et al.  Coevolutionary free lunches , 2005, IEEE Transactions on Evolutionary Computation.

[65]  Scott Aaronson,et al.  NP-complete Problems and Physical Reality , 2005, Electron. Colloquium Comput. Complex..

[66]  Anurag A. Agrawal,et al.  Phenotypic Plasticity in the Interactions and Evolution of Species , 2001, Science.

[67]  Anurag A. Agrawal,et al.  What is phenotypic plasticity and why is it important , 2009 .

[68]  Robin I. M. Dunbar.  Quantitative Ethology: The State Space Approach, D.J. McFarland, A. Houston. Pitman Books, London (1981), 204, Price £17.50 , 1983 .

[69]  D. Glanzman,et al.  Behavioral Neuroscience: No Easy Path from Genes to Cognition , 2012, Current Biology.

[70]  Danko D. Georgiev,et al.  Consciousness Operates Beyond the Timescale for Discerning Time Intervals: Implications for Q-mind Theories and Analysis of Quantum Decoherence in Brain , 2007 .

[71]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[72]  Ronald J. Brachman,et al.  Systems That Know What They're Doing , 2002, IEEE Intell. Syst..

[73]  W. Quinn,et al.  Flies, genes, and learning. , 2001, Annual review of neuroscience.

[74]  Peder J. Johnson,et al.  Cognitive Science: The Science of Intelligent Systems , 1994 .

[75]  Wenbo Mao,et al.  Modern Cryptography: Theory and Practice , 2003 .

[76]  Melkikh Av.  Can an organism select new valuable information from environment , 2002 .

[77]  W. Gantt.  Biology and Neurophysiology of the Conditioned Reflex and Its Role in Adaptive Behavior , 1976 .

[78]  Q. H. Wu,et al.  Stochastic Optimal Control of Active Vehicle Suspensions Using Learning Automata , 1993 .

[79]  Setsuo Osuga,et al.  A CONSIDERATION TO KNOWLEDGE REPRESENTATION-AN INFORMATION THEORETIC VIEW , 1984 .

[80]  M. Minsky.  The Society of Mind , 1986 .