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A real‐world rational agent: unifying old and new AI
Author(s) -
Verschure Paul F.M.J.,
Althaus Philipp
Publication year - 2003
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2704_1
Subject(s) - cognitive architecture , computer science , artificial intelligence , variety (cybernetics) , bayesian probability , context (archaeology) , embodied cognition , robot , architecture , argument (complex analysis) , task (project management) , machine learning , perspective (graphical) , intelligent agent , cognition , psychology , paleontology , art , biochemistry , chemistry , neuroscience , visual arts , biology , management , economics
Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.