Science is a long slow conversation. As a doctoral student, I was very impressed by this simple claim which, at the time, was offered as advice regarding research strategy. I was quite taken by the idea that there are many different ways to influence and shape the conversation, that the loudest voice is typically not the most interesting one, that influence plays out at many different time scales.
Years later, as a practicing scientist interested in the elusive question of how an autonomous agent should learn in open ended scenarios, I draw a different set of lessons from this notion. If we take the metaphor seriously then just as it is hard for a single person to have an interesting conversation, it is likely that a lot of interesting learning must come out of different perspectives that get weaved together. Moreover, the most interesting thing about good conversations is that it seems to have a flow that is determined by multiple, often inconsistent, views brought in by participants – the overall quantum of ‘knowledge’ consists of a proper synthesis of the consituent theories and philosophies. Wouldn’t it be nice if we could have the same happening with autonomous agents that operate in large worlds? What might one get if the thousands of autonomous robots that roam around various microworlds (individually trained using standard state of the art algorithms and techniques) could ‘compare notes’ and discuss strategies – that would lead to the elusive `step change’ in capability of these machines. Of course, we don’t fully understand how to achieve this sort of open ended transfer and generalization.
In this context, I was quite intrigued when a colleague told me about the semantic science project (http://www.semantic-science.org/). The idea is to put up machine understandable theories (and data) on the web so that this sort of synthesis may reveal new theories. The project seems to be in its infancy but I hope it does take off. Mostly, I am curious to see the conceptual foundations behind this style of knowledge aggregation and formation.
This isn’t just idle speculation from the sidelines. I am trying to work on some aspects of this problem myself. I suppose I should really say that I am trying to understand the groundwork in this area, in order to be able to eventually make progress on these questions. This doesn’t seem easy – the groundwork ranges from ways to reason about knowledge and ways in which one can assign and compute with probabilities over structured fragments of knowledge for learning from experience all the way through to the methodology of discovery, e.g., Lakatos’ famous little book on Proofs and Refutations. Let us see where all this takes us…