The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive SystemsGiovanni Pezzulo, Martin V. Butz, Cristiano Castelfranchi, Rino Falcone The general idea that brains anticipate the future, that they engage in prediction, and that one means of doing this is through some sort of inner model that can be run of?ine,hasalonghistory. SomeversionoftheideawascommontoAristotle,aswell as to many medieval scholastics, to Leibniz and Hume, and in more recent times, to Kenneth Craik and Philip Johnson-Laird. One reason that this general idea recurs continually is that this is the kind of picture that introspection paints. When we are engaged in tasks it seems that we form images that are predictions, or anticipations, and that these images are isomorphic to what they represent. But as much as the general idea recurs, opposition to it also recurs. The idea has never been widely accepted, or uncontroversial among psychologists, cognitive scientists and neuroscientists. The main reason has been that science cannot be s- is?ed with metaphors and introspection. In order to gain acceptance, an idea needs to be formulated clearly enough so that it can be used to construct testable hypot- ses whose results will clearly supportor cast doubtupon the hypothesis. Next, those ideasthatare formulablein one oranothersortof symbolismor notationare capable of being modeled, and modeling is a huge part of cognitive neuroscience. If an idea cannot be clearly modeled, then there are limits to how widely it can be tested and accepted by a cognitive neuroscience community. |
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action selection activation adaptive agent AIBO algorithm AMBR analogy anticipation anticipatory behavior anticipatory capabilities anticipatory mechanisms anticipatory systems approach architecture artificial Artificial Intelligence Balkenius ball bottom-up brain Butz Castelfranchi Cognitive Science cognitive systems complex components computational context cooperation coordination decision distractors DYNA-PI dynamic effects emotions emotivector environment epistemic example expectations and predictions filter forward models foveated function future Gallese goal goal-oriented behavior Grinberg Heidelberg hierarchical implemented inhibition of return intentional stance interaction internal inverse model IPDG Kalman filters LNCS LNAI memory mirror neurons module motor control move multiple neural network neurons object PAM model particle filter payoff perception performance Pezzulo planning players position real robot reinforcement learning relevant representations reward robot role Ryanair saliency map schema Schmidhuber sensorimotor sequences signal simulation situated society Springer stimulus structure surprise target task theory tion traditional animation trigger visual Wolpert