Artificial Intelligence Review

, Volume 25, Issue 3, pp 247–276 | Cite as

The state of play in machine/environment interactions

Article
First Online:

Abstract

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.

Keywords

Artificial intelligence Autonomous systems Complex systems Evolutionary computation Neural networks Robotics 
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References

  1. Agatonovic-Kustrin S and Beresford R (2000). Basic concepts of artificial neural network (ANN) modelling and its applications in pharmaceutical research. J Pharma Biomed Anal 22: 717–727 Google Scholar
  2. Agranat AJ, Schwartsglass O and Shappir J (1996). The charge controlled analog synapse. Solid-State Electron 39: 1435–1439 Google Scholar
  3. Agre P and Horswill I (1997). Lifeworld analysis. J Artif Intell Res 6: 111–145 Google Scholar
  4. Aitkenhead MJ, Dalgetty IA, Mullins CE, McDonald AJS and Strachan NJC (2003). Weed and crop discrimination using image analysis and artificial intelligence methods. Comput Electron Agri 39: 157–171 Google Scholar
  5. Aitkenhead MJ, Foster AR, FitzPatrick EA and Townend J (1999). Modelling water release and absorption in soils using cellular automata. J Hydrol 220: 104–112 Google Scholar
  6. Akbarzadeh-T M-R, Kumbla K, Tunstel E and Jamshidi M (2000). Soft computing for autonomous robotic systems. Comput Electr Eng 26: 5–32 Google Scholar
  7. Alberdi E and Sleeman DH (1997). ReTAX: a step in the automation of taxonomic revision. Artif Intell 91: 257–279 MATHGoogle Scholar
  8. Albert J (1999). Computational modeling of an early evolutionary stage of the nervous system. Biosystems 54: 77–90 Google Scholar
  9. Albesano D, Gemello R and Mana F (2000). Hybrid HMM-NN modeling of stationary-transitional units for continuous speech recognition. Inf Sci 123: 3–11 MATHGoogle Scholar
  10. Artale A and Franconi E (1998). A temporal description logic for reasoning about actions and plans. J Artif Intell Res 9: 463–506 MATHMathSciNetGoogle Scholar
  11. Baev K (1997). Highest level automatisms in the nervous system: A theory of functional principles underlying the highest forms of brain function. Prog Neurobiol 51: 129–166 Google Scholar
  12. Baluja S and Pomerleau DA (1997). Expectation-driven selective attention for visual monitoring and control of a robot vehicle. Rob Auton Syst 22: 329–344 Google Scholar
  13. Beer RD (1997). The dynamics of adaptive behavior: a research program. Rob Auton Syst 20: 257–289 Google Scholar
  14. Bellman R (1969). Modern analytic and computational methods in science and mathematics. American Elsevier Publishing Company Inc, New York Google Scholar
  15. Bench-Capon TJM (1990) Knowledge representation; an approach to artificial intelligence. APIC series, No. 32, Academic Press, London, UKGoogle Scholar
  16. Benkhoff J and Boice DC (1996). Modeling the thermal properties and the gas flux from a porous, ice-dust body in the orbit of P/Wirtanen. Planet Space Sci 44: 665–673 Google Scholar
  17. Bieszczad A and Pagurek B (1998). Neurosolver: neuromorphic general problem solver. J Inf Sci 105: 239–277 MathSciNetGoogle Scholar
  18. Bolles RC (1993). The story of psychology: a thematic history. Brooks/Cole Publishing Company. Pacific Grove, California Google Scholar
  19. Bonnet A, Haton J-P and Truong-Ngoc J-M (1988). Expert systems principle and practice. Prentice-Hall Inc., New Jersey Google Scholar
  20. Bouslama F (1999). Neural networks in the recognition of machine printed Arabic. Int J Pattern Recognit Artif Intell 13: 395–414 Google Scholar
  21. Brafman RI and Tennenholtz M (1996). On partially-controlled multi-agent systems. J Artif Intell Res 4: 477–507 MATHMathSciNetGoogle Scholar
  22. Broggi A and Bertè S (1995). Vision-based road detection in automotive systems: a real-time expectation-driven approach. J Artif Intell Res 3: 325–348 Google Scholar
  23. Brooks RA and Maes P (eds) (1996). Artificial life IV. The MIT Press, Cambridge, MA Google Scholar
  24. Brosnan T and Sun D-W (2004). Improving quality inspection of food products by computer vision—a review. J Food Eng 61: 3–16 Google Scholar
  25. Brouwer RK (1995). A method for training recurrent neural networks for classification by building basins of attraction. Neural Netw 8: 597–603 Google Scholar
  26. Buessler J-L and Urban J-P (1998). Visually guided movements: learning with modular neural maps in robotics. Neural Netw 11: 1395–1415 Google Scholar
  27. Bugmann G (1997). Biologically plausible neural computation. Biosystems 40: 11–19 Google Scholar
  28. Buluswar SD and Draper BA (1998). Color machine vision for autonomous vehicles. Eng Appl Artif Intell 11: 245–256 Google Scholar
  29. Burgard W, Cremers AB, Fox D, Hähnel D, Lakemeyer G, Schulz D, Steiner W and Thrun S (1999). Experiences with an interactive museum tour-guide robot. Artif Intell 114: 3–55 MATHGoogle Scholar
  30. Cadutal JT (1998). Artificial intelligence support for the United States armed forces’ “System of systems” concept. U.S. Army War College, Pennsylvania Google Scholar
  31. Carmignoto G (2000). Reciprocal communication systems between astrocytes and neurones. Prog Neurobiol 62: 561–581 Google Scholar
  32. Castellano G, Attolico G and Distante A (1997). Automatic generation of fuzzy rules for reactive robot controllers. Rob Auton Syst 22: 133–149 Google Scholar
  33. Changeux J and Dehaene S (2000). Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London. Int J Psychophysiol 35: 179–187 Google Scholar
  34. Chella A, Frixione M and Gaglio S (1997). A cognitive architecture for artificial vision. Artif Intell 89: 73–111 MATHGoogle Scholar
  35. Chen CLP, Cao Y and LeClair SR (1998). Materials structure-property prediction using a self-architecting neural network. J Alloys Compd 279: 30–38 Google Scholar
  36. Cheng H, Liu L, Li G, Shao L and Zhou C (1997). Second-order interpattern neural networks for optical pattern recognition. Opt Commun 139: 182–186 Google Scholar
  37. Chialvo DR and Bak P (1999). Learning from mistakes. Neuroscience 90: 1137–1148 Google Scholar
  38. Choi H and Rhee P (1999). Head gesture recognition using HMMs. Expert Syst Appl 17: 213–221 Google Scholar
  39. Cipolla R and Pentland A (eds) (1998). Computer vision for human–machine interaction. Cambridge University Press, Cambridge, UK Google Scholar
  40. Cohn D, Ghahramani Z and Jordan MI (1996). Active learning with statistical models. J Artif Intell Res 4: 129–145 MATHGoogle Scholar
  41. Dailey MN and Cottrell GW (1999). Organization of face and object recognition in modular neural network models. Neural Netw 12: 1053–1073 Google Scholar
  42. Damper RI, French RLB and Scutt TW (2000). ARBIB: an autonomous robot based on inspirations from biology. Rob Auton Syst 31: 247–274 Google Scholar
  43. Darwiche A and Provan G (1997). Query DAGs: a practical paradigm for implementing belief-network inference. J Artif Intell Res 6: 147–176 MATHMathSciNetGoogle Scholar
  44. Davis GW (1995). Long-term regulation of short-term plasticity: a postsynaptic influence on presynaptic transmitter release. J Physiol 89: 33–41 Google Scholar
  45. Daxwanger WA, Schmidt G (1996) Neural and fuzzy approaches to vision-based parking control. Control Eng Pract 4(11):1607–1614Google Scholar
  46. Daya B and Chauvet GA (1999). On the role of anatomy in learning by the cerebellar cortex. Math Biosci 155: 111–138 MATHMathSciNetGoogle Scholar
  47. De Jong H and Rip A (1997). The computer revolution in science: steps towards the realization of computer- supported discovery environments. Artif Intell 91: 225–256 MATHGoogle Scholar
  48. De la Rosa D, Mayol F, Moreno JA, Bonsón T and Lozano S (1999). An expert system/neural network model (ImpelERO) for evaluating agricultural soil erosion in Andalucia region, southern Spain. Agric, Ecosyst Enviro 73: 211–226 Google Scholar
  49. De Oliveira KA, Vannucci A and da Silva EC (2000). Using artificial neural networks to forecast chaotic time series. Physica D 284: 393–404 Google Scholar
  50. Dean J (1998). Animats and what they can tell us. Trends Cogn Sci 2: 60–67 MathSciNetGoogle Scholar
  51. Di Sciascio E, Donini FM and Mongiello M (2002). Structured knowledge representation for image retrieval. J Artif Intell Res 16: 209–257 MATHMathSciNetGoogle Scholar
  52. Duch W (1996). Computational physics of the mind. Comput Phys Commun 97: 136–153 MATHGoogle Scholar
  53. Dunbar R (1996). Grooming, gossip and the evolution of language. Harvard University Press, Cambridge, Massachusetts, USA Google Scholar
  54. Erichsen R and Theumann WK (1995). Learning and retrieval in attractor neural networks with noise. Physica A 220: 390–402 Google Scholar
  55. Ezhov AA and Vvedensky VL (1996). Object generation with neural networks (when spurious memories are useful). Neural Netw 9: 1491–1495 MATHGoogle Scholar
  56. Faller WE and Schreck SJ (1996). Neural networks: applications and opportunities in aeronautics. Prog Aerospace Sci 32: 433–456 Google Scholar
  57. Fedorenko YV, Husebye ES and Ruud BO (1999). Explosion site recognition; neural network discriminator using single three-component stations. Phys Earth Planet Int 113: 131–142 Google Scholar
  58. Fernández M and Caballero J (2006). Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. J Mol Grap Model 25(4): 410–422 Google Scholar
  59. Flood I (1998). Modeling dynamic engineering processes when the governing equations are unknown. Comput Struct 67: 367–374 MATHGoogle Scholar
  60. Floreano D and Mondada F (1998). Evolutionary neurocontrollers for autonomous mobile robots. Neural Netw 11: 1461–1478 Google Scholar
  61. Franco L, Treves A (2001) A neural network face expression recognition system using an unsupervised local processing. In: Proceedings of the second international symposium on image and signal processing and analysis (ISPA’01), Croatia, 2001Google Scholar
  62. Francois O and Zaharie D (1999). Markovian perturbations of discrete iterations: Lyapunov functions, global minimization and associative memory. Math Comput Model 29: 81–94 MATHMathSciNetGoogle Scholar
  63. Freeman RD (1996). Studies of functional connectivity in the developing and mature visual cortex. J Physiol 90: 199–203 Google Scholar
  64. French RM (2000). The Turing test: the first 50 years. Trends Cogn Sci 4: 115–122 Google Scholar
  65. Friedlander MJ, Hersanyi K and Kara P (1996). Mechanisms for regulating synaptic efficiency in the visual cortex. J Physiol 90: 179–184 Google Scholar
  66. Gandhi CC and Matzel LD (2000). Modulation of presynaptic action potential kinetics underlies synaptic facilitation of Type B photoreceptors after associative conditioning in Hermissenda. J Neurosci 20: 2022–2035 Google Scholar
  67. García-Pedrajas N (2006). Cooperative coevolution of neural networks and ensembles of neural networks. Stud Comput Intell 16: 465–490 CrossRefGoogle Scholar
  68. Gaussier P, Joulain C, Banquet JP, Leprêtre S and Revel A (2000). The visual homing problem: an example of robotics/biology cross fertilization. Rob Auton Syst 30: 155–180 Google Scholar
  69. Gaussier P, Revel A, Joulain C and Zrehen S (1997). Living in a partially structured environment: how to bypass the limitations of classical reinforcement techniques. Rob Auton Syst 20: 225–250 Google Scholar
  70. Gicquel N, Anderson JS and Kevrekidis IG (1998). Noninvertibility and resonance in discrete-time neural networks for time-series processing. Phys Lett A 238: 8–18 Google Scholar
  71. Giles LC, Horne BG and Lin T (1995). Learning a class of large finite state machines with a recurrent neural network. Neural Netw 8: 1359–1365 Google Scholar
  72. Glymour C, Ford KM and Hayes PJ (1998). Ramón Lull and the infidels. AI Mag 19: 136 Google Scholar
  73. Grigore O (1997). Syntactical self-organising map. Lect Notes Comput Sci 1226: 101–109 Google Scholar
  74. Gupta P and Sinha NK (1999). An improved approach for nonlinear system identification using neural networks. J Franklin Inst 336: 721–734 MATHGoogle Scholar
  75. Harvey I, Husbands P, Cliff D, Thompson A and Jacobi N (1997). Evolutionary robotics: the Sussex approach. Rob Auton Syst 20: 205–224 Google Scholar
  76. Heiduschka P and Thanos S (1998). Implantable bioelectric interfaces for lost nerve functions. Prog Neurobiol 55: 433–461 Google Scholar
  77. Hirsch MW (1997). On-line training of a continually adapting adaline-like network. Neurocomputing 15: 347–361 MATHGoogle Scholar
  78. Horiuchi TK and Koch C (1999). Analog VLSI-based modeling of the primate oculomotor system. Neural Comput 11: 243–265 Google Scholar
  79. Horneck G (1996). Life sciences of the Moon. Adv Space Res 18(11): 95–101 Google Scholar
  80. Husmeier D (2000). Learning non-stationary conditional probability distributions. Neural Netw 13: 287–290 Google Scholar
  81. Ibnkahla M (2000). Applications of neural networks to digital communications—a survey. Signal Processing 80: 1185–1215 MATHGoogle Scholar
  82. Ilg W and Berns K (1995). A learning architecture based on reinforcement learning for adaptive control of the walking machine LAURON. Rob Auton Syst 15: 321–334 Google Scholar
  83. Illi OJ (1996). Future diagnostics technology. Expert Syst Appl 11: 147–155 Google Scholar
  84. Jerbic B, Grolinger K and Vranjes B (1999). Autonomous agent based on reinforcement learning and adaptive shadowed network. Artif Intell Eng 13: 141–157 Google Scholar
  85. Johannet A and Sarda I (1999). Goal-directed behaviours by reinforcement learning. Neurocomputing 28: 107–125 Google Scholar
  86. Kaiser M and Dillman R (1997). Hierarchical refinement of skills and skill application for autonomous robots. Rob Auton Syst 19: 259–271 Google Scholar
  87. Kamm C, Walker M and Rabiner L (1997). The role of speech processing in human-computer intelligent communication. Speech Commun 23: 263–278 Google Scholar
  88. Kavanau JL (1997). Memory, sleep and the evolution of mechanisms of synaptic efficacy maintenance. Neuroscience 79: 7–44 Google Scholar
  89. Kilmer W (1997). A command computer for complex autonomous systems. Neurocomputing 17: 47–59 Google Scholar
  90. Kinzel W (1999). Statistical physics of neural networks. Comput Phys Commun 121–122: 86–93 MathSciNetGoogle Scholar
  91. Kirchberg KJ, Jesorsky O and Frischholtz RW (2002). Genetic model opimization for Hausdorff distance-based face localization. Lect Notes Comput Sci 2359: 103–111 Google Scholar
  92. Kozma R (1997). Multi-level knowledge representation in neural networks with adaptive structure. Syst Res Inf Sci 7: 147–167 Google Scholar
  93. Krebs F and Bossel H (1997). Emergent value orientation in self-organization of an animat. Ecol Model 96: 143–164 Google Scholar
  94. Kühn S and Cruse H (2005). Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Sci 17(3–4): 343–360 Google Scholar
  95. Ladunga I (2000). Large-scale predictions of secretory proteins from mammalian genomic and EST sequences. Curr Opin Biotechnol 11: 13–18 Google Scholar
  96. Langton CG (ed) (1996) Artificial Life: an overview. The MIT Press, Cambridge, Massachusetts, USAGoogle Scholar
  97. Leahey TH (1980). A history of psychology: main currents in psychological thought. Prentice-Hall Inc., New Jersey Google Scholar
  98. Lek S and Guégan JF (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 120: 65–73 Google Scholar
  99. Levine ER, Kimes DS and Sigillito VG (1996). Classifying soil structure using neural networks. Ecol Model 92: 101–108 Google Scholar
  100. Lin C-K and Wang S-D (1998). A self-organizing fuzzy control approach for bank-to-turn missiles. Fuzzy Sets Syst 96: 281–306 MathSciNetGoogle Scholar
  101. Lin L-J, Hancock TR and Judd JS (1998a). A robust landmark-based system for vehicle location using low- bandwidth vision. Rob Auton Syst 25: 19–32 Google Scholar
  102. Lin X, Ohtsubo J and Mori M (1998b). Capacity of optical associative memory using a terminal attractor model. Opt Commun 146: 49–54 Google Scholar
  103. Liu P (2000). Max-min fuzzy Hopfield neural networks and an efficient learning algorithm. Fuzzy Sets Syst 112: 41–49 MATHGoogle Scholar
  104. Liu X, Wang DL (2001) Appearance-based recognition using perceptual components. In: Proceedings of the international joint conference on neural networks 2001 (IJCNN-01), Washington DC, USA, 2001Google Scholar
  105. Mackay DS and Robinson VB (2000). A multiple criteria decision support system for testing integrated environmental models. Fuzzy Sets Syst 113: 53–67 MATHGoogle Scholar
  106. Maeda M, Shimakawa M and Murakami S (1995). Predictive fuzzy control of an autonomous mobile robot with forecast learning function. Fuzzy Sets Syst 72: 51–60 Google Scholar
  107. Mahajan A and Figueroa F (1997). Four-legged intelligent mobile autonomous robot. Robot CIM-INT Manuf 13: 51–61 Google Scholar
  108. Marchant JA (1996). Tracking of row structure in three crops using image analysis. Comput Electr Agric 15: 161–179 Google Scholar
  109. Markram H and Tsodyks M (1996). Redistribution of synaptic efficacy: a mechanism to generate infinite synaptic input diversity from a homogenous population of neurons without changing absolute synaptic efficacies. J Physiol 90: 229–232 Google Scholar
  110. McCafferty JD (1990). Human and machine vision: computing perceptual organisation. Ellis Horwood, New York Google Scholar
  111. McCarthy J and Hayes PJ (1969). Some philosophical problems from the standpoint of artificial intelligence. In: Michie, D (eds) Machine Intelligence 4, American Elsevier, New York Google Scholar
  112. McCorduck P (1979). Machines who think. W. H. Freeman and Company, San Francisco Google Scholar
  113. McCulloch W and Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 7: 115–133 MathSciNetGoogle Scholar
  114. McNamara S, Cunningham P and Byrne J (1998). Neural networks for language identification: a comparative study. Inf Processing & Management 34: 395–403 Google Scholar
  115. Mehr I and Sculley TL (1996). A multilayer neural network structure for analog filtering. IEEE Trans Circuits Syste II-Analog Digital 43: 613–618 Google Scholar
  116. Minasny B, McBratney AB and Bristow KL (1999). Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93: 225–253 Google Scholar
  117. Minsky M (1986). The society of mind. Simon and Schuster, New York Google Scholar
  118. Moriarty DE, Schultz AC and Grefenstette JJ (1999). Evolutionary algorithms for reinforcement learning. J Artif Intell Res 11: 241–276 MATHGoogle Scholar
  119. Morimoto T and Hashimoto Y (2000). AI approaches to identification and control of total plant production systems. Control Eng Pract 8: 555–567 Google Scholar
  120. Morimoto T, Takeuchi T, Miyata H and Hashimoto Y (1996). Intelligent control for a plant production system. Control Eng Pract 4: 773–784 Google Scholar
  121. Nakasuka S and Tanabe T (1996). New control problems associated with a proposed future space transportation infrastructure. Control Eng Pract 4: 1703–1714 Google Scholar
  122. Nechyba MC, Xu Y (1994) Neural network approach to control system identification with variable activation functions. In: Proceedings of the IEEE international symposium on intelligent control, Columbus, Ohio, USA, 1994Google Scholar
  123. Neubig M, Destexhe A (2000) Are inhibitory synaptic conductances on thalamic relay neurons inhomogeneous? Are synapses from individual afferents clustered? Neurocomputing 32–33:213–218Google Scholar
  124. Ng KT and Feng J (2001). Dynamical associative memory based on an oscillatory neural network. J Intell Syst 11: 155–171 Google Scholar
  125. Nikravesh M, Farell AE and Stanford TG (1997). Dynamic neural network control for non-linear systems: optimal neural network structure and stability analysis. Chem Eng J 68: 41–50 Google Scholar
  126. Noever DA, Brittain A, Matsos HC, Baskaran S and Obenhuber D (1996). The effects of variable biome distribution on global climate. Biosystems 39: 135–141 Google Scholar
  127. Nolfi S (1997). Evolving non-trivial behaviours on real robots: a garbage collecting robot. Rob Auton Syst 22: 187–198 Google Scholar
  128. Nordby VJ and Hall CS (1974). A guide to psychologists and their concepts. W. H. Freeman & Son, San Francisco Google Scholar
  129. O’ Malley PD, Nechyba MC, Arroyo AA (2002) Human activity tracking for wide-area surveillance. In: Proceedings of 2002 Florida conference on recent advances in robotics, Miami, USA, 2002Google Scholar
  130. Okamoto M, Sekiguchi T, Tanaka K, Maki Y and Yoshida S (1999). Biochemical neuron: hardware implementation of functional devices by mimicking the natural intelligence such as metabolic control systems. Comput Electr Eng 25: 421–438 Google Scholar
  131. Olson RL and Sequeira RA (1995). Emergent computation and the modeling and management of ecological systems. Comput Electr Agric 12: 183–209 Google Scholar
  132. Paraskevas PA, Pantelakis IS and Lekkas TD (1999). An advanced integrated expert system for wastewater treatment plants control. Knowledge-Based Syst 12: 355–361 Google Scholar
  133. Pasquariello G, Satalino G, la Forgia V and Spilotros F (1998). Automatic target recognition for naval traffic control using neural networks. Image Vision Comput 16: 67–73 Google Scholar
  134. Pedrycz W (1991). A referential scheme of fuzzy decision-making and its neural network structure. IEEE Trans Syst Man Cybern 21: 1593–1604 Google Scholar
  135. Pentland A and Liu A (1999). Modeling and prediction of human behaviour. Neural Comput 11: 229–242 Google Scholar
  136. Penumadu D and Zhao R (1999). Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Comput Geotech 24: 207–230 Google Scholar
  137. Pigford DV and Baur G (1990). Expert systems for business: concepts and applications. Boyd & Fraser, San Francisco Google Scholar
  138. Pulasinghe K, Watanabe K, Izumi K and Kiguchi K (2004). Modular fuzzy-neuro controller driven by spoken language commands. IEEE Trans Syst Man Cybern Part B: Cybern 34(1): 293–302 Google Scholar
  139. Quinn K, Didier AJ, Baker JF and Peterson BW (1998). Modeling learning in brain stem and cerebellar sites responsible for VOR plasticity. Brain Res Bull 46: 333–346 Google Scholar
  140. Ray SR and Hsu WH (1998). Self-organized-expert modular network for classification of spatiotemporal sequences. Intell Data Anal 2: 287–301 Google Scholar
  141. Ricotti ME and Zio E (1999). Neural network approach to sensitivity and uncertainty analysis. Reliability Engi Syst Safety 64: 59–71 Google Scholar
  142. Rietman E (1994). Genesis redux: experiments creating artificial life. McGraw-Hill, New York Google Scholar
  143. Rodrigue J-P (1997). Parallel modelling and neural networks: an overview for transportation/land use systems. Transpn Res.-C. 5: 259–271 Google Scholar
  144. Rosenberg JR, Halliday DM, Breeze P and Conway BA (1998). Identification of patterns of neuronal connectivity—partial spectra, partial coherence, and neuronal interactions. J Neurosci Meth 83: 57–72 Google Scholar
  145. Rusakov DA, Stewart MG, Davies HA and Harrison E (1995). 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. Neuroscience 66: 291–307 Google Scholar
  146. Saksida LM, Raymond SM and Touretzky DS (1997). Shaping robot behavior using principles from instrumental conditioning. Rob Auton Syst 22: 231–249 Google Scholar
  147. Salmela P, Lehtokangas M and Saarinen J (1999). Neural network based digit recognition system for dialling in noisy environments. Inf Sci 121: 171–199 Google Scholar
  148. Samejima K and Omori T (1999). Adaptive internal state space construction method for reinforcement learning of a real-world agent. Neural Netw 12: 1143–1155 Google Scholar
  149. Schaal S (1999). Is imitation learning the route to humanoid robots?. Trends Cogn Sci 3: 233–242 Google Scholar
  150. Schaap MG and Leij FJ (1998). Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil Tillage Res 47: 37–42 Google Scholar
  151. Schenker B and Agarwal M (1997). Dynamic modelling using neural networks. Int J Syst Sci 28: 1285–1298 MATHGoogle Scholar
  152. Schleiter IM, Borchardt D, Wagner R, Dapper T, Schmidt K-D, Schmidt H-H and Werner H (1999). Modelling water quality, bioindication and population dynamics on lotic ecosystems using neural networks. Ecol Model 120: 271–286 Google Scholar
  153. Schoonhoven R, Prijs VF and Frijns JHM (1997). Transmitter release in inner hair cell synapses: a model analysis of spontaneous and driven rate properties of cochlear nerve fibres. Hear Res 113: 247–260 Google Scholar
  154. Sette S, Boullart L and van Langenhove L (1998). Using genetic algorithms to design a control strategy of an industrial process. Control Engi Pract 6: 523–527 Google Scholar
  155. Seung HS (1998). Continuous attractors and oculomotor control. Neural Netw 11: 1253–1258 Google Scholar
  156. Smith CS (1980). From art to science. Seventy-two objects illustrating the nature of discovery. The MIT Press, Cambridge, Massachusetts Google Scholar
  157. Smith RE and Cribbs HB (1997). Combined biological paradigms: a neural, genetics-based autonomous systems strategy. Rob Auton Syst 22: 65–74 Google Scholar
  158. Starrenburg JG, van Luenen WTC, Oelen W and van Amerongen J (1996). Learning feedforward controller for a mobile robot vehicle. Control Eng Pract 4: 1221–1230 Google Scholar
  159. Steels L (1997). A selectionist mechanism for autonomous behaviour acquisition. Rob Auton Syst 20: 117–131 Google Scholar
  160. Stoecker M, Reitboeck HJ and Eckhorn R (1996). A neural network for scene segmentation by temporal coding. Neurocomputing 11: 123–134 MATHGoogle Scholar
  161. Sumpter N and Bulpitt A (2000). Learning spatio-temporal patterns for predicting object behaviour. Image Voice Compu 18: 697–704 Google Scholar
  162. Talukder A, Casasent D (2001) Adaptive activation function neural net for face recognition. In: Proceedings of the IEEE international joint conference on neural networks, Washington, DC, USA, 2001Google Scholar
  163. Thrun S and Mitchell TM (1995). Lifelong robot learning. Rob Auton Syst 15: 25–46 Google Scholar
  164. Timmermans AJM and Hulzebosch AA (1996). Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. Comput Electr Agric 15: 41–55 Google Scholar
  165. Tipping E, Woof C, Rigg E, Harrison AF, Ineson P, Taylor K, Benham D, Poskitt J, Rowland AP, Bol R and Harkness DD (1999). Climatic influences on the leaching of dissolved organic matter from upland UK moorland soils, investigated by a field manipulation experiment. Environ Int 25: 83–95 Google Scholar
  166. Treves A, Rolls E and Simmen M (1997). Time for retrieval in recurrent associative memories. Physica D 107: 392–400 Google Scholar
  167. Tsaih R, Hsu Y and Lai CC (1998). Forecasting S & P 500 stock index futures with a hybrid AI system. Decis Support Syst 23: 161–174 Google Scholar
  168. Tsodyks M (2005). Attractor neural networks and spatial maps in hippocampus. Neuron 48(2): 168–169 Google Scholar
  169. Tyler L, Czarnecki CA (1999) A neural vision based controller for a robot footballer. In: Proceedings of the 7th IEE int. conference on image processing and its applications, Manchester, UK, 1999Google Scholar
  170. Von Wichert G (1998). Mobile robot localization using a self-organized visual environment representation. Rob Auton Syst 25: 185–194 Google Scholar
  171. Von Wichert G (1999). Can robots learn to see?. Control Eng Pract 7: 783–795 Google Scholar
  172. Walley WJ and Fontama VN (1998). Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain. Wat Res 32: 613–622 Google Scholar
  173. Weiss M and Baret F (1999). Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote Sens Environ 70: 293–306 Google Scholar
  174. Weng J and Chen S (1998). Vision-guided navigation using SHOSLIF. Neural Netw 11: 1511–1529 Google Scholar
  175. Wolff JR, Laskawi R, Spatz WB and Missler M (1995). Structural dynamics of synapses and synaptic components. Behav Brain Res 66: 13–20 Google Scholar
  176. Wong JC, McDonald KA and Palazoglu A (1998). Classification of process trends based on fuzzified symbolic representation and hidden Markov models. J Proc Cont 8: 395–408 Google Scholar
  177. Wong PM, Jang M, Cho S and Gedeon TD (2000). Multiple permeability predictions using an observational learning algorithm. Comput Geosci 26: 907–913 Google Scholar
  178. Yang H-L (1997). A simple coupler to link expert systems with database systems. Expert Syst Appl 12: 179–188 Google Scholar
  179. Yeh I-C (1997). Application of neural networks to automatic soil pressure balance control for shield tunneling. Autom Construct 5: 421–426 Google Scholar
  180. Yun C-B and Bahng EY (2000). Substructural identification using neural networks. Comput Struct 77: 41–52 Google Scholar
  181. Zardeki A (1995). Fuzzy controllers in nuclear material accounting. Fuzzy Sets Syst 74: 73–79 Google Scholar
  182. Zhai Y, Thomasson JA, Boggess JE III and Sui R (2006). Soil texture classification with artificial neural networks operating on remote sensing data. Comput Electr Agric 54(2): 53–68 Google Scholar
  183. Zhang M, Fulcher J and Scofield RA (1997). Rainfall estimation using artificial neural network group. Neurocomputing 16: 97–115 Google Scholar
  184. Zhao Y and Collins EG Jr (2005). Robust automatic parallel parking in tight spaces via fuzzy logic. Rob Auton Syst 51(2–3): 111–127 Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Plant & Soil ScienceUniversity of AberdeenAberdeenScotland, UK

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