Abstract
Current perceptual algorithms are error-prone and require the use of additional ad hoc heuristic methods that detect and recover from these errors. In this paper we explore how existing architectural mechanisms in a high-level cognitive architecture like ACT-R can be used instead of such ad hoc measures. In particular, we describe how implicit learning that results from ACT-R’s architectural features of partial matching and blending can be used to recover from errors in object identification, tracking and action prediction. We demonstrate its effectiveness by building a model that can identify and track objects as well as predict their actions in a simple checkpoint scenario.
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Kurup, U., Lebiere, C., Stentz, A. (2011). Integrating Perception and Cognition for AGI. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_11
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DOI: https://doi.org/10.1007/978-3-642-22887-2_11
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