A standard model of mind will involve not only an architecture but also a set of capabilities. Ideally, the two should inform one another at a deep level, as an architecture is what both enables and constrains capabilities. In that spirit, we consider in some detail a routine and (deceptively) simple robotic task. From it, we build out a substantial list of capabilities that appear essential for a general-purpose execution of the task. We argue that this type of exercise is an indispensable step toward the establishment of a baseline for the comparison of cognitive architectures, and that the resulting taxonomy can inform the synthesis of a standard model of the mind.

We discuss Perry’s notion of the essential indexical and the requirement that robots interacting with humans (and other robots) be able to reason about themselves in a grounded way. We describe an approach based on grounding symbols via an analogue of the neural mechanism of efference copy and approaching symbolic reasoning via active logic — a situated framework for logical and temporal reasoning.

How can a robot tell when it — rather than another agent — is making an utterance or performing an action? This is rather tricky and also very important for human-robot (or even robot-robot) interaction. Here we outline our beginning attempt to deal with this issue.

We examine some ways in which contemporary results from neural network theory can potentially contribute to a Buddhist understanding of emptiness. We also make some general remarks about the pitfalls and benefits inherent in attempting to apply ideas from artificial intelligence to an understanding of Buddhism.