Memory-Centred Cognitive Architectures for Robots Interacting Socially with Humans
This work addresses the challenge of developing cognitive architectures for robots to interact socially with humans, but it is incremental as it builds on existing memory-centred perspectives.
The paper tackles the problem of enabling robots to engage in social interactions with humans by proposing a memory-centred cognitive architecture that emphasizes prediction and priming based on prior experience, with examples including multi-modal alignment, though it notes further refinement is needed.
The Memory-Centred Cognition perspective places an active association substrate at the heart of cognition, rather than as a passive adjunct. Consequently, it places prediction and priming on the basis of prior experience to be inherent and fundamental aspects of processing. Social interaction is taken here to minimally require contingent and co-adaptive behaviours from the interacting parties. In this contribution, I seek to show how the memory-centred cognition approach to cognitive architectures can provide an means of addressing these functions. A number of example implementations are briefly reviewed, particularly focusing on multi-modal alignment as a function of experience-based priming. While there is further refinement required to the theory, and implementations based thereon, this approach provides an interesting alternative perspective on the foundations of cognitive architectures to support robots engage in social interactions with humans.