Day two of tutorials and workshops at AAAI 2011. The crowd is starting to grow and the robots are being set up. Things don’t get fully started until tomorrow but there is a growing number of activities going on today. I went to two tutorials which I describe in more details below. There were also a number of workshops going on, the one I heard about most was the Analyzing Microtext Workshop run by David Aha and others. In the evening the social events got started with the banquet and IAAI video competition winners.
This was quite an interesting and well run tutorial. He was trying to highlight the relation between structured prediction and Inverse Reinforcement Learning, also called apprenticeship learning. There was a lot of content but there was a common theme of learning linear classifiers using various methods (prerceptrons, SVMs) for part of language understanding and other problems. He then related this to Inverse Reinforcement Learning which is essentially trying to learn the reward function from an optimal policy rather than the other way around. This can be done by observing an ‘optimal’ agent (assuming you have one) and using the same techniques as in structured prediction to iteratively improve your estimate of the agent’s reward model. Once you have this reward model you can now do normal reinforcement learning to find an optimal policy that is more robust than simply doing supervised learning since you are not mimicking their actions but mimicking their intent.
Philosophy as AI and AI as Philosophy
I had been torn this afternoon between two tutorials to attend, the overview of the latest results in classical planning which I really should attend and this very broad philosophy and AI tutorial. Nature intervened and made the choice for me though as the planning tutorial was cancelled. Prof. Sloman gave us a general overview of many branches of philosophy and how he believes they relate deeply to Artificial Intelligence.
The main point of his talk and much of his work over the past thirty years is increase the flow of ideas in both directions between philosophy and computer science, but specifically AI. He argues that there is lots each field could contribute to the other. So, philosophical ideas and open problems could guide AI research into areas that could help resolve philosophical questions. One fun question is what is humour? What does it mean for something to be funny? From an AI perspective, what would need to be true for us to say that a computer found a joke funny? This is as opposed to a computer identifying a statement as funny. Is there a difference?
Near the end when he was summing up he also stated that he believes philosophers who do learn about AI concepts are deeply changed by it and can then address philosophical questions in ways they previously could not. He calls on young AI researchers to join him in trying to bridge these gaps and start more discussion on the philosophical questions AI could address rather than just the engineering questions.