Biological learning and artificial intelligence

Balkenius, Christian (1994) Biological learning and artificial intelligence. [Departmental Technical Report]

Full text available as:



It was once taken for granted that learning in animals and man could be explained with a simple set of general learning rules, but over the last hundred years, a substantial amount of evidence has been accumulated that points in a quite different direction. In animal learning theory, the laws of learning are no longer considered general. Instead, it has been necessary to explain behaviour in terms of a large set of interacting learning mechanisms and innate behaviours. Artificial intelligence is now on the edge of making the transition from general theories to a view of intelligence that is based on anamalgamate of interacting systems. In the light of the evidence from animal learning theory, such a transition is to be highly desired.

Item Type:Departmental Technical Report
Subjects:Biology > Animal Cognition
Computer Science > Artificial Intelligence
Biology > Animal Behavior
ID Code:3705
Deposited By: Balkenius, Christian
Deposited On:06 Jul 2004
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Albus, J. S., (1975), “A new approach to manipulatorcontrol: the cerebellar model articulation controller(CMAC)”, Transactions of ASME Journal of Dynamical Systems, Measurement, and Control, 97,25–61.

Atkeson, C. G. & Reinkensmeyer, D. J., (1990), “Usingassociative content-addressable memories to controlrobots”, in Winston, P. H. & Shellard, S. A., Artificialintelligence at MIT: expanding frontiers, Cambridge,MA: MIT Press, in preparation.

Baird, L. C. & Klopf, H. A., (1993), “A hieararchical network pf provably optimal learning control systems:extensions of the associative control process (ACP)network”, Adaptive Behavior, 1, 321–352.

Barto, A. G., Sutton, R. S. & Watkins, C. J. C. H., (1990),“Learning and sequential decision making”, in M.Gabriel and J. Moore (eds.) Learning and computationalneuroscience: foundations of adaptive networks, 539–602, Cambridge, MA: MIT Press.

Bolles, R. C., (1970), “Species-specific defence reactions andavoidance learning”, Psychological Review, 77, 32–48.

Bolles, R. C., (1978), Learning theory, Fort Worth: Holt,Rinehart and Winston, Inc.

Brooks, R. A., (1986), “Achieving artificial intelligencethrough building robots”, MIT A. I. Memo No. 899.

Brooks, R. A., (1991a), “New approaches to robotics”,Science, 253, 1227–1232.

Brooks, R. A., (1991b), “Intelligence without reason”.Proceedings of IJCAI–91, 569–595,Brooks, R. A. & Stein, L. A., (1993), “Building brains forbodies”, MIT A. I. Memo No. 1439.

Carpenter, R. H. S., (1984), Neurophysiology, London:Edward Arnold Ltd.

Charniak, E. & McDermott, D., (1985), Introduction toartificial intelligence, Reading, MA: Addison-Wesley.

Connell, J. H., (1990), Minimalist Mobile Robots, Boston:Academic Press.

Davidsson, P., (1994), “Concepts and autonomous agents”,LU–CS–TR: 94–124, Department of computer science,Lund University.

Dunham, P. J., (1977), “The nature of reinforcing stimuli”,in W. K. Honig and J. E. R. Staddon (eds.) Handbook ofoperant behavior, Englewood Cliffs, NJ: Prentice-Hall.

Eichenbaum, H., Otto, T. A., Wible, C. G. & Piper, J. M.,(1991), “Building a model of the hippocampus inolfaction and memory”, in J. L. Davis and H.Eichenbaum (eds.) Olfaction, 167–210, Cambridge, MA:MIT Press.

Fikes, R. E. & Nilsson, N. J., (1971), “STRIPS: A newapproach to the application of theorem proving toproblem solving”, Artificial Intelligence, 2, 189–208.

Gallistel, C. R., (1980), The organisation of action: a newsynthesis, Hillsdale, JN: Erlbaum.Garcia, J. & Koelling, R. A., (1966), “Relation of cue toconsequences in avoidance learning”, Psychonomic Science, 4, 123–124.

Gibson, J. J., (1979), The ecological approach to visualperception, Hillsdale, NJ: Lawrence ErlbaumAssociates.Glass, A. L. & Holyoak, K. J., (1985), Cognition, New York:Random House.

Gulz, A., (1991), “The planning of action as a cognitive andbiological phenomenon”, Lund University CognitiveStudies, 2.

Hebb, D. O., (1949), The organisation of behavior, NewYork: John Wiley and Sons.

Holland, J. H., Holyoak, K. J., Nisbett, R. E. & Thagard, P.R., (1986), Induction: Processes of inference, learningand discovery, Cambridge, MA: MIT Press.

Horswill, I. D. & Brooks, R. A., (1988), “Situated vision ina dynamic world: chasing objects”. Proceedings ofAAAI–88, St Paul, MN, 796–800.

Horswill, I., (1992), “Characterizing adaption byconstraints”, in F. J. Varela and P. Bourgine (eds.)Towards an practice of autonomous systems, 58–63,Cambridge, MA: MIT Press.

Hull, C. L., (1934), “The concept of the habit-familyhierarchy and maze learning”, Psychological Review,41, 33–52, 134–152.

Hull, C. L., (1943), Principles of behavior, New York: Appleton-Century-Crofts.Hull, C. L., (1952), A behavior system, New Haven: YaleUniversity Press.

Ito, M., (1982), “Mechanisms of motor learning”, in S.Amari and M. A. Arbib (eds.) Competition andcooperation in neural nets, 418–429, Berlin: Springer-Verlag.

Ito, M., (1989), “Long Term Depression”, Annual Reviewof Neuroscience, 12, 85–102.Johnson, M. H. & Morton, J., (1991), Biology and cognitivedevelopment: the case of face recognition, Oxford:Blackwell.

Kraft, L. G., III & Campagna, D. P., (1990), “A summarycomparison of CMAC neural network and traditionaladaptive control systems”, in W. T. Miller, III, R. S.Sutton and P. J. Werbos (eds.) Neural networks forcontrol, 143–169, Cambridge, MA: MIT Press.

Lippman, R. P., (1987), “An introduction to computing with neural nets”, IEEE ASSP magazine, 4–22.

Lorenz, K., (1977), Behind the mirror, London: Methuen &co Ltd.Macfarlane, D. A., (1930), “The role of kinesthesis in mazelearning”, University of California Publications in Psychology, 4, 277–305.

Mackintosh, N. J., (1974), The psychology of animallearning, New York: Academic Press.Mackintosh, N. J., (1983), Conditioning and associativelearning, Oxford: Oxford University Press.

Maes, P., (1990a), “Designing autonomous agents”, in P.Maes (ed.) Designing autonomous agents, 1–2,Cambridge, MA: MIT Press.

Maes, P., (1990b), Designing autonomous agents,Cambridge, MA: MIT Press.

Marler, P., (1970), A comparative approach to vocallearning: song-development in white-crownedsparrows, Journal of Comparative and PhysiologicalPsychology Monograph, 71, pt. 2, 1–25.

Masterson, F. A., (1970), “Is termination of a warningsignal an effective reward for the rat?”, Journal ofComparative and Physiological Psychology, 72, 471–475.

Melzoff, A. N. & Moore, M. K., (1977), “Imitation offacial and manual gestures by human neonates”,Science, 198, 75–78.

Moore, J. W., & Blazis, D. E. J., (1989), “Cerebellarimplementation of a computational model of classicalconditioning”, in Strata, P. (ed.), The olivocerebellarsystem in motor control, Berlin: Springer-Verlag.

Morris, R. G. M., (1981), “Spatial location does not requirethe presence of local cues”, Learning and Motivation,12, 239–260.

Newell, A., (1990), Unified theories of cognition,Cambridge, MA: Harvard University Press.

Olton, D. S. & Samuelson, R. J., (1976), “Remembrance ofplaces past: spatial memory in rats”, Journal ofExperimental Psychology: Animal Behaviourprocesses, 2, 97–116.

Pavlov, I. P., (1927), Conditioned reflexes, Oxford: OxfordUniversity Press.

Payton, D. W., (1990), “Internalized plans: a representationfor action resources”, in P. Maes (ed.) Designingautonomous agents, 89–103, Cambridge, MA: MITPress.

Peng, J. & Williams, R. J., (1993), “Efficient learning andplanning within the Dyna framework”, AdaptiveBehavior, 1, 437–454.

Premack, D., (1965), “Reinforcement theory”, in D. Levine(ed.) Nebraska symposium on motivation, Lincoln:University of Nebraska Press.Premack, D., (1971), “Catching up on common sense, or twosides of a generalization: reinforcement andpunishment”, in R. Glaser (ed.) On the nature ofreinforcement, New York: Academic Press.

Rescorla, R. A. & Wagner, A. R., (1972), “A theory ofPavlovian conditioning: variations in the effectivenessof reinforcement and nonreinforcement”, in A. H. Blackand W. F. Prokasy (eds.) Classical conditioning II:current research and theory, 64–99, New York:Appleton-Century-Crofts.

Rosch, E., (1973), “On the internal structure of perceptualand semantic categories”, Journal of ExperimantalPsychology: General, 104, 192–233.

Rosen, R., (1985), Anticipatory systems – philosophical,mathematical and methodological foundations,Elmsford, N.Y: Pergamon Press.

Runeson, S., (1989), “A note on the utility of ecologicalincomplete invariants”, Newsletter of the internationalsociety for ecological psychology, 4, 6–9.

Samuel, A. L., (1959), “Some studies in machine learningusing the game of checkers”, IBM Journal of ResearchDevelopment, 3, 210–229.

Schmajuk, N. A. & Blair, H. T., (1993), “Place learning andthe dynamics of spatial navigation: a neural networkapproach”, Adaptive Behavior, 1, 353–385.

Schmajuk, N. A. & Thieme, A. D., (1992), “Purposive behavior and cognitive mapping: a neural networkmodel”, Biological Cybernetics, 67, 165–174.

Seligman, M. E. P., (1970), “On the generality of the lawsof learning”, Psychological Review, 77, 406–418.

Sjölander, S., (1993), “Some cognitive break-through in theevolution of cognition and consciousness, and theirimpact on the biology of language”, Evolution andCognition, in press.

Skarda, C. A. & Freeman, W. J., (1987), “How brains makechaos in order to make sense of the world”, Behavioraland brain sciences, 10, 161–195.

Small, W. S., (1901), “Experimental study of the mentalprocesses of the rat II”, American Journal ofPsychology, 12, 206–239.

Sutton, R. S., (1992), “Reinforcement learning architec-tures for animats”, in J.-A. Meyer and S. W. Wilson(eds.) From animals to animats, 288–296, Cambridge,MA: MIT Press.

Sutton, R. S. & Barto, A. G., (1990), “Time-derivative models of Pavlovian reinforcement”, in M. Gabriel andJ. Moore (eds.) learning and computationalneuroscience: foundations of adaptive networks, 497–538, Cambridge, MA: MIT Press.

Timberlake, W., (1984), “The functional organization ofappetitive behavior: behavior systems and learning”, in M. D. Zeiler and P. Harzem (eds.) Advances in theanalysis of behavior, New York: Wiley.

Tolman, E. C., (1932), Purposive behaviour in animals andmen, New York: Appleton-Century-Crofts.

Tolman, E. C. & Honzik, C. H., (1930), “‘Insight’ in rats”,University of California Publications in Psychology, 4,215–232.

Watkins, C. J. C. H., (1992), “Q-learning”, Machine Learning, 8, 279–292.

Widrow, B. & Hoff, M. E., (1960/1988), “Adaptiveswitching circuits”, in J. A. Anderson and E. Rosenfeld(eds.) Neurocomputing: foundations of research, 123–134, Cambridge, MA: MIT Press.

Zipser, D., (1985), “A computational model ofhippocampal place fields”, Behavioral Neuroscience,99, 1006–1018.


Repository Staff Only: item control page