Architectural Mechanisms for Mitigating Uncertainty during Long-Term Declarative Knowledge Access

Long-term declarative semantic memory is a key component of a cognitive architecture that allows an agent to perform effectively in complex tasks. Over the years, a number of new mechanisms beyond deliberate cued retrieval have been developed, including partial matching and spreading activation. However, there has yet to be a theory that coherently explains why these mechanisms are beneficial or necessary. We propose that these semantic memory mechanisms are attempts to mitigate the uncertainty in the environment, as well as to compensate for the errors and incompleteness in agent knowledge. Through this lens, we show that existing mechanisms are misunderstood and motivate future areas of exploration, including how retrieval mechanisms should incorporate agent problem-solving context and how to unify the treatment of diverse mechanisms and the uncertainties they address.