Deconstructing Episodic Memory and Learning in Sigma

Deconstructing Episodic Memory and Learning in Sigma Paul S. Rosenbloom (Rosenbloom@USC.Edu) Institute for Creative Technologies & Department of Computer Science, University of Southern California 12015 Waterfront Dr. Playa Vista, CA 90094 USA Abstract In an experiment in functional elegance, episodic memory and learning have been deconstructed in the Sigma cognitive architecture in terms of pre-existing memory and learning mechanisms plus a template-based structure generator. As a side effect, base-level activation also becomes deconstructed in terms of a learned temporal prior. Keywords: Cognitive architecture; learning; base-level activation. episodic memory; Episodic memory is a core competency in human cognition (Tulving, 1983), but not yet a pervasive capability in cognitive architectures. It is relevant in both modeling human cognition and in creating artificial intelligence. Its core functionality includes the ability to store the history of what has been experienced (autobiographical/temporal) and to retrieve and reuse information from relevant past episodes given appropriate cues. It may also support replay of fragments of history given a starting location in it. One of the earliest implementations of episodic memory was in the Basic Agent (Vere & Bickford, 1990). More recently, episodic memories have been added to Soar (Nuxoll & Laird, 2012) and Icarus (Stracuzzi et al., 2009). ACT-R’s declarative memory is also relevant (Anderson et al., 2004); however, as with most forms of instance-based learning, it does not explicitly represent time or adjacency (but see Altmann & Gray, 1998). Episodic memory also relates to case-based reasoning (Kolodner, 1993), but it is task independent and not solution oriented. Sigma (Rosenbloom, 2013) – a new cognitive architecture built around the state-of-the-art generality and efficiency of graphical models (Koller & Friedman, 2009) – also embodies an episodic memory, inspired primarily by Soar, but also making contact with ACT-R. The initial focus was on episodic storage and retrieval via the same memory structures – conditionals – that also yield a concept-based semantic memory and a rule-based procedural memory (Rosenbloom, 2010). Conditionals provide a deep blending of the conditionality found in both rule-based systems and probabilistic networks, all grounded in message passing – via a variant of the summary-product algorithm – in factor graphs (Kschischang, Frey & Loeliger, 2001). This early work on memory illustrated, and was driven by, a key desideratum in Sigma’s overall development – functional elegance – which focuses on architectures that are broadly capable yet simple and theoretically elegant. In other words, the goal is to generate the wealth of requisite functionality through the interactions among a small set of very general mechanisms, and thus to yield a deeper theory with broader explanatory reach (Deutsch, 2012). More recently, functional elegance has been guiding the addition of learning to Sigma, with the most significant piece being a gradient-descent mechanism for learning the parameters in factor nodes (Rosenbloom et al., 2013). Given appropriate conditionals, this has proven sufficient for both supervised and unsupervised classifier learning, the acquisition of maps (in SLAM), and reinforcement learning (along with the acquisition of action models). The work reported here combines the earlier ideas for functionally elegant representation with the newer ideas for functionally elegant learning, plus one additional idea – automatic template-driven structure creation – to yield automated episodic learning and retrieval. Episodic learning was actually one of the earliest forms implemented in Sigma, but via an isolated special purpose module. The current work shows how such learning can instead be deconstructed in terms of a combination of more general components that already exist in Sigma plus a template- driven structure generator for episodic memory. The contributions of this work concern these architectural results rather than specific matches to human data. This work also demonstrates how base-level activation, as pioneered in ACT-R (Anderson et al., 2004) and later reimplemented in Soar, can be deconstructed in terms of a learned temporal prior that forms the backbone of episodic memory (biasing retrieval towards more recently learned episodes, as well as ones that have been more recently and frequently accessed). Rather than a planned part of the architecture, it was a true discovery that gradient-descent learning mimics base-level activation in episodic memory. Several additional, albeit smaller, such discoveries are also covered in the body of this article. These two deconstructions – with a particular focus on episodic learning – are at the heart of the contribution of this paper. They reveal how such capabilities can be supported in a relatively uniform non-modular manner within a cognitive architecture. This in turn yields a deeper understanding of these capabilities in an architectural context, and more generally of how to approach the development of functionally elegant architectures. Sigma Sigma is composed of two layers: the cognitive architecture plus a graphical architecture beneath it. The cognitive architecture is based on predicates and conditionals. A predicate represents a relation – such as the number of legs