Skip to main content
Article thumbnail
Location of Repository

Biological learning and artificial intelligence

By Christian Balkenius

Abstract

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

Topics: Animal Cognition, Artificial Intelligence, Animal Behavior
Year: 1994
OAI identifier: oai:cogprints.org:3705
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://cogprints.org/3705/1/Ba... (external link)
  • http://cogprints.org/3705/ (external link)
  • Suggested articles

    Citations

    1. (1952). A behavior system,
    2. (1993). A hieararchical network pf provably optimal learning control systems: extensions of the associative control process (ACP) network”,
    3. (1975). A new approach to manipulator control: the cerebellar model articulation controller (CMAC)”,
    4. (1989). A note on the utility of ecological incomplete invariants”, Newsletter of the international society for ecological psychology,
    5. (1990). A summary comparison of CMAC neural network and traditional adaptive control systems”,
    6. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement”,
    7. (1986). Achieving artificial intelligence through building robots”,
    8. (1987). An introduction to computing with neural nets”,
    9. (1985). Anticipatory systems – philosophical, mathematical and methodological foundations ,
    10. (1977). Behind the mirror ,
    11. (1991). Biology and cognitive development: the case of face recognition,
    12. (1991). Building a model of the hippocampus in olfaction and memory”,
    13. (1993). Building brains for bodies”,
    14. (1971). Catching up on common sense, or two sides of a generalization: reinforcement and punishment”,
    15. (1989). Cerebellar implementation of a computational model of classical conditioning”,
    16. (1992). Characterizing adaption by constraints”,
    17. (1994). Concepts and autonomous agents”, LU–CS–TR: 94–124, Department of computer science,
    18. (1927). Conditioned reflexes ,
    19. (1983). Conditioning and associative learning ,
    20. (1990). Designing autonomous agents”,
    21. (1993). Efficient learning and planning within the Dyna framework”,
    22. (1901). Experimental study of the mental processes of the rat II”,
    23. (1987). How brains make chaos in order to make sense of the world”, Behavioral and brain sciences,
    24. (1986). Induction: Processes of inference, learning and discovery,
    25. (1990). Internalized plans: a representation for action resources”,
    26. (1985). Introduction to artificial intelligence,
    27. (1990). Learning and sequential decision making”,
    28. (1978). Learning theory,
    29. (1989). Long Term Depression”,
    30. (1982). Mechanisms of motor learning”,
    31. (1990). Minimalist Mobile Robots ,
    32. (1991). New approaches to robotics”,
    33. (1970). On the generality of the laws of learning”,
    34. (1973). On the internal structure of perceptual and semantic categories”,
    35. (1993). Place learning and the dynamics of spatial navigation: a neural network approach”,
    36. (1943). Principles of behavior ,
    37. (1992). Purposive behavior and cognitive mapping: a neural network model”,
    38. (1965). Reinforcement theory”,
    39. (1966). Relation of cue to consequences in avoidance learning”,
    40. (1976). Remembrance of places past: spatial memory in rats”,
    41. (1988). Situated vision in a dynamic world: chasing objects”.
    42. (1993). Some cognitive break-through in the evolution of cognition and consciousness, and their impact on the biology of language”, Evolution and Cognition,
    43. (1959). Some studies in machine learning using the game of checkers”,
    44. (1981). Spatial location does not require the presence of local cues”,
    45. (1970). Species-specific defence reactions and avoidance learning”,
    46. (1971). STRIPS: A new approach to the application of theorem proving to problem solving”,
    47. (1934). The concept of the habit-family hierarchy and maze learning”,
    48. (1979). The ecological approach to visual percep t i on , Hillsdale, NJ: Lawrence Erlbaum Associates.
    49. (1977). The nature of reinforcing stimuli”,
    50. (1980). The organisation of action: a new synthesis,
    51. (1949). The organisation of behavior ,
    52. (1991). The planning of action as a cognitive and biological phenomenon”,
    53. (1974). The psychology of animal learning ,
    54. (1930). The role of kinesthesis in maze learning”,
    55. (1990). Unified theories of cognition ,
    56. (1990). Using associative content-addressable memories to control robots”, in

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.