The state of play in machine/environment interactions

Due to the breadth of the subject, it is no longer possible to provide a review of all of the work being carried out in the field of Artificial Intelligence. However, a more localised review of research taking place in the overlap between engineering, AI and psychology can be meaningfully performed. We show here that while there have been marked successes in the past few years, there is an identifiable set of ‘classic’ problems that remain to be solved, and which largely direct the work ongoing in this area. This review aims to discuss the directions being taken at the current time, in particular the developing and maturing possibilities provided by neural networks and evolutionary computation, and by the use of our knowledge of the mind in developing artificial agents capable of mimicking our abilities to interact with the environment.

[1]  Włodzisław Duch,et al.  Computational physics of the mind , 1996 .

[2]  Alex Pentland,et al.  Computer Vision for Human–Machine Interaction: Acknowledgements , 1998 .

[3]  Alain Destexhe,et al.  Are inhibitory synaptic conductances on thalamic relay neurons inhomogeneous? Are synapses from individual afferents clustered? , 2000, Neurocomputing.

[4]  Robin I. M. Dunbar.  Grooming, Gossip and the Evolution of Language , 1996 .

[5]  S. Thanos,et al.  Implantable bioelectronic interfaces for lost nerve functions , 1998, Progress in Neurobiology.

[6]  Witold Pedrycz.  A referential scheme of fuzzy decision making and its neural network structure , 1991, IEEE Trans. Syst. Man Cybern..

[7]  Kenneth Taylor,et al.  Climatic influences on the leaching of dissolved organic matter from upland UK moorland soils, investigated by a field manipulation experiment , 1999 .

[8]  David G. Stork,et al.  A Search for Structure: Selected Essays on Science, Art, and History and From Art to Science: Seventy-Two Objects Illustrating the Nature of Discovery , 1980 .

[9]  Juyang Weng,et al.  Vision-guided navigation using SHOSLIF , 1998, Neural Networks.

[10]  K. V. Baev,et al.  HIGHEST LEVEL AUTOMATISMS IN THE NERVOUS SYSTEM: A THEORY OF FUNCTIONAL PRINCIPLES UNDERLYING THE HIGHEST FORMS OF BRAIN FUNCTION , 1997, Progress in Neurobiology.

[11]  Johan H. M. Frijns,et al.  Transmitter release in inner hair cell synapses: a model analysis of spontaneous and driven rate properties of cochlear nerve fibres , 1997, Hearing Research.

[12]  J. L. Kavanau,et al.  Memory, sleep and the evolution of mechanisms of synaptic efficacy maintenance , 1997, Neuroscience.

[13]  Puyin Liu.  Max-min fuzzy Hopfield neural networks and an efficient learning algorithm , 2000, Fuzzy Sets Syst..

[14]  Sebastian Thrun,et al.  Lifelong robot learning , 1993, Robotics Auton. Syst..

[15]  Giorgio Carmignoto,et al.  Reciprocal communication systems between astrocytes and neurones , 2000, Progress in Neurobiology.

[16]  Friedrich Krebs,et al.  Emergent value orientation in self-organization of an animat , 1997 .

[17]  D. D. L. Rosa,et al.  An expert system/neural network model (ImpelERO) for evaluating agricultural soil erosion in Andalucia region, southern Spain , 1999 .

[18]  Ming Zhang,et al.  Rainfall estimation using artificial neural network group , 1997, Neurocomputing.

[19]  Junji Ohtsubo,et al.  Capacity of optical associative memory using a terminal attractor model , 1998 .

[20]  Enrico Zio,et al.  Neural network approach to sensitivity and uncertainty analysis , 1999 .

[21]  R. French.  The Turing Test: the first 50 years , 2000, Trends in Cognitive Sciences.

[22]  Ian Horswill,et al.  Lifeworld Analysis , 1997, J. Artif. Intell. Res..

[23]  John J. Grefenstette,et al.  Evolutionary Algorithms for Reinforcement Learning , 1999, J. Artif. Intell. Res..

[24]  Andrzej Bieszczad,et al.  Neurosolver: Neuromorphic General Problem Solver , 1998, Inf. Sci..

[25]  C. Lee Giles,et al.  Learning a class of large finite state machines with a recurrent neural network , 1995, Neural Networks.

[26]  Roelof K. Brouwer.  A method for training recurrent neural networks for classification by building basins of attraction , 1995, Neural Networks.

[27]  Robert I. Damper,et al.  ARBIB: An autonomous robot based on inspirations from biology , 2000, Robotics Auton. Syst..

[28]  K. T. Ng,et al.  Dynamical Associative Memory Based on an Oscillatory Neural Network , 2001 .

[29]  Naresh K. Sinha,et al.  AN IMPROVED APPROACH FOR NONLINEAR SYSTEM IDENTIFICATION USING NEURAL NETWORKS , 1999 .

[30]  Dayakar Penumadu,et al.  Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) , 1999 .

[31]  Christopher G. Langton,et al.  Artificial Life , 2019, Philosophical Posthumanism.

[32]  Scott Schreck,et al.  Neural networks: Applications and opportunities in aeronautics , 1996 .

[33]  Giuseppe Satalino,et al.  Automatic target recognition for naval traffic control using neural networks , 1998, Image Vis. Comput..

[34]  Katarina Grolinger,et al.  Autonomous agent based on reinforcement learning and adaptive shadowed network , 1999, Artif. Intell. Eng..

[35]  Tetsuo Morimoto,et al.  AI approaches to identification and control of total plant production systems , 2000 .

[36]  Mo Jamshidi,et al.  Soft computing for autonomous robotic systems , 2000 .

[37]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[38]  Sylvian R. Ray,et al.  Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences , 1998, Intell. Data Anal..

[39]  Tetsuo Morimoto,et al.  Intelligent Control for Plant Production System , 1995 .

[40]  W. B. Spatz,et al.  Structural dynamics of synapses and synaptic components , 1995, Behavioural Brain Research.

[41]  Masoud Nikravesh,et al.  Dynamic neural network control for non-linear systems: optimal neural network structure and stability analysis , 1997 .

[42]  I. Kevrekidis,et al.  Noninvertibility and resonance in discrete-time neural networks for time-series processing , 1998 .

[43]  Andrew J. Bulpitt,et al.  Learning spatio-temporal patterns for predicting object behaviour , 2000, Image Vis. Comput..

[44]  F. Baret,et al.  Evaluation of Canopy Biophysical Variable Retrieval Performances from the Accumulation of Large Swath Satellite Data , 1999 .

[45]  Ahmet Palazoglu,et al.  Classification of process trends based on fuzzified symbolic representation and hidden Markov models , 1998 .

[46]  Jean-Paul Rodrigue,et al.  Parallel modelling and neural networks: An overview for transportation/land use systems , 1997 .

[47]  H Choi.  Head gesture recognition using HMMs , 1999 .

[48]  Ovidiu Grigore.  Syntactical Self-Organizing Map , 1997, Fuzzy Days.

[49]  Salvatore Gaglio,et al.  A Cognitive Architecture for Artificial Vision , 1997, Artif. Intell..

[50]  Luc Steels,et al.  A selectionist mechanism for autonomous behavior acquisition , 1997, Robotics Auton. Syst..

[51]  Ronen I. Brafman,et al.  On Partially Controlled Multi-Agent Systems , 1996, J. Artif. Intell. Res..

[52]  Heng-Li Yang.  A simple coupler to link expert systems with database systems , 1997 .

[53]  Philippe Gaussier,et al.  Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques , 1997, Robotics Auton. Syst..

[54]  Wolfgang Kinzel,et al.  Statistical physics of interacting neural networks , 2001 .

[55]  Jean Paul Haton,et al.  Expert systems : principles and practice , 1988 .

[56]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[57]  Shumeet Baluja,et al.  Expectation-based selective attention for visual monitoring and control of a robot vehicle , 1997, Robotics Auton. Syst..

[58]  K. Kiguchi,et al.  Modular fuzzy-neuro controller driven by spoken language commands , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[59]  Ian Flood.  Modeling dynamic engineering processes when the governing equations are unknown , 1998 .

[60]  Faouzi Bouslama.  Neural Networks in the Recognition of Maching Printed Arabic Characters , 1999, Int. J. Pattern Recognit. Artif. Intell..

[61]  Bruce A. Draper,et al.  Color machine vision for autonomous vehicles , 1998 .

[62]  James F. Baker,et al.  Modeling learning in brain stem and cerebellar sites responsible for VOR plasticity , 1998, Brain Research Bulletin.

[63]  W. J. Walley,et al.  Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain , 1998 .

[64]  B Daya,et al.  On the role of anatomy in learning by the cerebellar cortex. , 1999, Mathematical biosciences.

[65]  L. Boullart,et al.  Using genetic algorithms to design a control strategy of an industrial process , 1998 .

[66]  Günther Schmidt,et al.  Neural and fuzzy approaches to vision-based parking control , 1996 .

[67]  I. Ladunga,et al.  Large-scale predictions of secretory proteins from mammalian genomic and EST sequences. , 2000, Current opinion in biotechnology.

[68]  Takashi Omori,et al.  Adaptive internal state space construction method for reinforcement learning of a real-world agent , 1999, Neural Networks.

[69]  Chung Bang Yun,et al.  Substructural identification using neural networks , 2000 .

[70]  Francesco Mondada,et al.  Evolutionary neurocontrollers for autonomous mobile robots , 1998, Neural Networks.

[71]  Stefano Nolfi,et al.  Evolving non-trivial behaviors on real robots: A garbage collecting robot , 1997, Robotics Auton. Syst..

[72]  Arie Rip,et al.  The Computer Revolution in Science: Steps Towards the Realization of Computer-Supported Discovery Environments , 1997, Artif. Intell..

[73]  Sungzoon Cho,et al.  Multiple permeability predictions using an observational learning algorithm , 2000 .

[74]  P. Kara,et al.  Mechanisms for regulating synaptic efficiency in the visual cortex , 1996, Journal of Physiology-Paris.

[75]  O. François,et al.  Markovian perturbations of discrete iterations: Lyapunov functions, global minimization, and associative memory , 1999 .

[76]  Budiman Minasny,et al.  Comparison of different approaches to the development of pedotransfer functions for water-retention curves , 1999 .

[77]  Roberto Gemello,et al.  Hybrid HMM-NN modeling of stationary-transitional units for continuous speech recognition , 2000, Inf. Sci..

[78]  A. J. S. McDonald,et al.  Weed and crop discrimination using image analysis and artificial intelligence methods , 2003 .

[79]  Donald Michie,et al.  Machine Intelligence 4 , 1970 .

[80]  Wolfram Burgard,et al.  Experiences with an Interactive Museum Tour-Guide Robot , 1999, Artif. Intell..

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

[82]  D. A. Rusakov,et al.  Population trends in the fine spatial re-organization of synaptic elements in forebrain regions of chicks 0.5 and 24 hours after passive avoidance training , 1995, Neuroscience.

[83]  Alberto Broggi,et al.  Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach , 1995, J. Artif. Intell. Res..

[84]  P. Bak,et al.  Learning from mistakes , 1997, Neuroscience.

[85]  David Casasent,et al.  Adaptive activation function neural net for face recognition , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[86]  T. Leahey,et al.  A History of Psychology: Main Currents in Psychological Thought , 1981 .

[87]  Liren Liu,et al.  Second-order interpattern neural networks for optical pattern recognition , 1997 .

[88]  J. van Amerongen,et al.  Learning feedforward controller for a mobile robot vehicle , 1996 .

[89]  R. Cipolle,et al.  Computer Vision and Human-Computer Interaction , 1998 .

[90]  Dirk Husmeier.  Learning non-stationary conditional probability distributions , 2000, Neural Networks.

[91]  Giovanni Attolico,et al.  Automatic generation of fuzzy rules for reactive robot controllers , 1997, Robotics Auton. Syst..

[92]  H. Markram,et al.  Redistribution of synaptic efficacy: A mechanism to generate infinite synaptic input diversity from a homogenous population of neurons without changing absolute synaptic efficacies , 1996, Journal of Physiology - Paris.

[93]  A. A. Ezhov,et al.  Object Generation with Neural Networks (When Spurious Memories are Useful) , 1996, Neural Networks.

[94]  N. Rashevsky.  The mathematical biophysics of some mental phenomena , 1945 .

[95]  Eystein S. Husebye,et al.  Explosion site recognition; neural net discriminator using single three-component stations , 1999 .

[96]  Mohamed Ibnkahla,et al.  Applications of neural networks to digital communications - a survey , 2000, Signal Process..

[97]  T. L. Sculley,et al.  A multilayer neural network structure for analog filtering , 1996 .

[98]  Garrison W. Cottrell,et al.  Organization of face and object recognition in modular neural network models , 1999, Neural Networks.

[99]  Edmund T. Rolls,et al.  Time for retrieval in recurrent associative memories , 1997 .

[100]  Francesco M. Donini,et al.  Structured Knowledge Representation for Image Retrieval , 2011, J. Artif. Intell. Res..

[101]  Sheng-De Wang,et al.  A self-organizing fuzzy control approach for bank-to-turn missiles , 1998, Fuzzy Sets Syst..

[102]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[103]  J. Stephen Judd,et al.  A robust landmark-based system for vehicle location using low-bandwidth vision , 1998, Robotics Auton. Syst..

[104]  Georg von Wichert.  Mobile robot localization using a self-organized visual environment representation , 1998, Robotics Auton. Syst..

[105]  Ronaldo A. Sequeira,et al.  Emergent computation and the modeling and management of ecological systems , 1995 .

[106]  Derek H. Sleeman,et al.  ReTAX: A Step in the Automation of Taxonomic Revision , 1997, Artif. Intell..

[107]  Vincent B. Robinson,et al.  A multiple criteria decision support system for testing integrated environmental models , 2000, Fuzzy Sets Syst..

[108]  Misha Tsodyks.  Attractor Neural Networks and Spatial Maps in Hippocampus , 2005, Neuron.

[109]  Patrick D. O'malley.  HUMAN ACTIVITY TRACKING FOR WIDE-AREA SURVEILLANCE , 2002 .

[110]  G Bugmann,et al.  Biologically plausible neural computation. , 1997, Bio Systems.

[111]  S Dehaene,et al.  Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London. , 1998, Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie.

[112]  Inman Harvey,et al.  Evolutionary robotics: the Sussex approach , 1997, Robotics Auton. Syst..

[113]  Ray Tsaih,et al.  Forecasting S&P 500 stock index futures with a hybrid AI system , 1998, Decis. Support Syst..

[114]  P. Gaussiera,et al.  The visual homing problem : An example of robotics / biology cross fertilization , 1999 .

[115]  A.J.M. Timmermans,et al.  Computer vision system for on-line sorting of pot plants using an artificial neural network classifier , 1996 .

[116]  Rüdiger Dillmann,et al.  Hierarchical refinement of skills and skill application for autonomous robots , 1997, Robotics Auton. Syst..

[117]  Shinichi Nakasuka,et al.  New control problems associated with a proposed future space transportation infrastructure , 1996 .

[118]  RD Freeman.  Studies of functional connectivity in the developing and mature visual cortex , 1996, Journal of Physiology-Paris.

[119]  Calvin S. Hall,et al.  A guide to psychologists and their concepts , 1975 .

[120]  Orlando J. Illi,et al.  Future diagnostics technology , 1996 .

[121]  Reinhard Eckhorn,et al.  A neural network for scene segmentation by temporal coding , 1996, Neurocomputing.

[122]  Jean-Paul Rodrigue,et al.  PARALLEL DISTRIBUTED PROCESSING OF TRANSPORTATION/LAND USE SYSTEMS: THEORY AND MODELLING WITH NEURAL NETWORKS , 1992 .

[123]  DeLiang Wang,et al.  Appearance-based recognition using perceptual components , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[124]  Klaus J. Kirchberg,et al.  Genetic Model Optimization for Hausdorff Distance-Based Face Localization , 2002, Biometric Authentication.

[125]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[126]  Ying Dong,et al.  On constructing a cooperative paradigm , 2002, Appl. Artif. Intell..

[127]  Gwynne L. Davis,et al.  Long-term regulation of short-term plasticity: A postsynaptic influence on presynaptic transmitter release , 1995, Journal of Physiology-Paris.

[128]  G. Horneck.  Life sciences on the Moon , 1996 .

[129]  H. Sebastian Seung,et al.  Continuous attractors and oculomotor control , 1998, Neural Networks.

[130]  Jean-Luc Buessler,et al.  Visually guided movements: learning with modular neural maps in robotics , 1998, Neural Networks.

[131]  J. R. Rosenberg,et al.  Identification of patterns of neuronal connectivity—partial spectra, partial coherence, and neuronal interactions , 1998, Journal of Neuroscience Methods.

[132]  David S. Touretzky,et al.  Shaping robot behavior using principles from instrumental conditioning , 1997, Robotics Auton. Syst..

[133]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[134]  Ingrid M. Schleiter,et al.  Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks , 1999 .

[135]  Joseph Shappir,et al.  The charge controlled analog synapse , 1996 .

[136]  C. A. Czarnecki,et al.  A neural vision based controller for a robot footballer , 1999 .

[137]  Darleen V. Pigford,et al.  Expert Systems for Business: Concepts and Applications , 1990 .

[138]  M. Schaap,et al.  Using neural networks to predict soil water retention and soil hydraulic conductivity , 1998 .

[139]  Morris W. Hirsch,et al.  On-line training of a continually adapting adaline-like network , 1997, Neurocomputing.

[140]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[141]  Anne Johannet,et al.  Goal-directed behaviours by reinforcement learning , 1999, Neurocomputing.

[142]  D. Noever,et al.  The effects of variable biome distribution on global climate. , 1996, Bio Systems.

[143]  Satoshi Yoshida,et al.  Biochemical neuron: hardware implementation of functional devices by mimicking the natural intelligence such as metabolic control systems , 1999 .

[144]  Robert E. Smith,et al.  Combined biological paradigms: A neural, genetics-based autonomous systems strategy , 1997, Robotics Auton. Syst..

[145]  Enrico Franconi,et al.  A Temporal Description Logic for Reasoning about Actions and Plans , 1998, J. Artif. Intell. Res..

[146]  Shuta Murakami,et al.  Predictive fuzzy control of an autonomous mobile robot with forecast learning function , 1995 .

[147]  Pamela McCorduck,et al.  Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence , 1979 .

[148]  Nicolás García-Pedrajas,et al.  Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks , 2006, Multi-Objective Machine Learning.

[149]  Jamshid Ghaboussi,et al.  New nested adaptive neural networks (NANN) for constitutive modeling , 1998 .

[150]  J. Albert,et al.  Computational modeling of an early evolutionary stage of the nervous system. , 1999, Bio Systems.

[151]  Julio Caballero,et al.  Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. , 2006, Journal of molecular graphics & modelling.

[152]  C. L. Philip Chen,et al.  Materials structure-property prediction using a self-architecting neural network , 1998 .

[153]  S. Agatonovic-Kustrin,et al.  Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. , 2000, Journal of pharmaceutical and biomedical analysis.

[154]  Wolfgang Kinzel.  Statistical physics of neural networks , 1999 .

[155]  Yangsheng Xu,et al.  Neural network approach to control system identification with variable activation functions , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[156]  William L. Kilmer,et al.  A command computer for complex autonomous systems , 1997, Neurocomputing.

[157]  Padraig Cunningham,et al.  Neural Networks for Language Identification: A Comparative Study , 1998, Inf. Process. Manag..

[158]  Fernando Figueroa,et al.  Four-legged intelligent mobile autonomous robot , 1997 .

[159]  Jukka Saarinen,et al.  Neural Network Based Digit Recognition System for Voice Dialling in Noisy Environments , 1999, Inf. Sci..

[160]  A. Vannucci,et al.  Using artificial neural networks to forecast chaotic time series , 2000 .

[161]  Holk Cruse,et al.  Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences , 2005, Connect. Sci..

[162]  J. McCafferty.  Human and machine vision: computing perceptual organisation , 1990 .

[163]  J. Dean.  Animats and what they can tell us , 1998, Trends in Cognitive Sciences.

[164]  I-Cheng Yeh,et al.  Application of neural networks to automatic soil pressure balance control for shield tunneling , 1997 .

[165]  A. Treves,et al.  A neural network facial expression recognition system using unsupervised local processing , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[166]  Randall D. Beer,et al.  The dynamics of adaptive behavior: A research program , 1997, Robotics Auton. Syst..

[167]  Christof Koch,et al.  Analog VLSI-Based Modeling of the Primate Oculomotor System , 1999, Neural Computation.

[168]  Mukul Agarwal,et al.  Dynamic modelling using neural networks , 1997, Int. J. Syst. Sci..

[169]  Edward Rietman.  Genesis Redux: Experiments Creating Artificial Life , 1993 .

[170]  Gregory M. Provan,et al.  Query DAGs: A practical paradigm for implementing belief-network inference , 1996, UAI.

[171]  V. G. Sigillito,et al.  Classifying soil structure using neural networks , 1996 .

[172]  Georg von Wichert.  Can robots learn to see , 1999 .

[173]  Marilyn A. Walker,et al.  The role of speech processing in human-computer intelligent communication , 1997, Speech Commun..

[174]  Stanislas Dehaene,et al.  Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[175]  Robert C. Bolles,et al.  The Story of Psychology: A Thematic History , 1993 .

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

[177]  P. A. Paraskevas,et al.  An advanced integrated expert system for wastewater treatment plants control , 1999, Knowl. Based Syst..

[178]  Nicolas Garc ´ ia-Pedrajas.  Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks , 2006 .

[179]  J. A. Marchant,et al.  Tracking of row structure in three crops using image analysis , 1996 .

[180]  J. Alex Thomasson,et al.  Soil texture classification with artificial neural networks operating on remote sensing data , 2006 .

[181]  Andrew Zardecki.  Fuzzy controllers in nuclear material accounting , 1995, Fuzzy Sets Syst..

[182]  Johannes Benkhoff,et al.  Modeling the thermal properties and the gas flux from a porous, ice-dust body in the orbit of P/Wirtanen , 1996 .

[183]  Matt Aitkenhead,et al.  Modelling water release and absorption in soils using cellular automata , 1999 .

[184]  L. Matzel,et al.  Modulation of Presynaptic Action Potential Kinetics Underlies Synaptic Facilitation of Type B Photoreceptors after Associative Conditioning in Hermissenda , 2000, The Journal of Neuroscience.

[185]  Karsten Berns,et al.  A learning architecture based on reinforcement learning for adaptive control of the walking machine LAURON , 1995, Robotics Auton. Syst..

[186]  Kenneth M. Ford,et al.  Ramon Lull and the Infidels , 1998 .

[187]  R Erichsen,et al.  LEARNING AND RETRIEVAL IN ATTRACTOR NEURAL NETWORKS WITH NOISE , 1995 .