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Towards efficiently supporting large symbolic declarative memories

Efficient access to large declarative memories is one challenge in the development of large-scale cognitive models. Prior work has provided an initial demonstration of declarative retrievals using ACT-R and a relational database. In this paper, we provide extended analysis of the computational challenges involved. We detail data structures and algorithms for an efficient mechanism over a large set of retrievals, as well as for a class of activation bias. We have implemented this work in Soar, and present detailed evaluation on synthetic data as well as the WordNet 3 lexicon.