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(test) This year the annual AAAI conference held a special track for the field of Computational Sustainability. I attended the AAAI conference and presented a paper in the CompSust track but I also ended up spending most of my time listening to other talks from this track. This was partly because each of the talks was interesting in itself but also because it turned out to be a great way to see a range of work going on in AI without changing rooms as often.

There was a huge diversity of problem domains and AI methods brought to bear on them. This was an interesting way to attend AAAI actually since each session had a variety of approaches, exposing a lot of the variety of approaches there is at a large general conference like AAAI. Most of my most vigourous discussions were with people in very different fields from me since we both needed to translate each other’s language and discover our own assumptions. I think this is something that happens less often at more focussed conferences.

One of papers chosen as outstanding paper (one of only two as far as I could tell) came from the CompSust track (Dynamic Resource Allocation in Conservation Planning by Daniel Golovin, Andreas Krause, Beth Gardner, Sarah J. Converse, Steve Morey). This was a very impressive project on reserve management for nature reserves to protect wildlife which was the result of wide collaboration between universities, government and industry.

Just some of the domains and methods used in the papers in this track to give you an idea of the variety the topics:

- smart energy grid design
- distributed energy storage
- nature reserve planning
- wildlife migration corridors
- building energy efficiency, comparing and improving efficiency
- water conservation in residental landscapes
- bird species tracking

- market simulation of energy tariffs with Qlearning
- multiagent planning — an agent buying and selling power from the grid from your local batteries
in order to lower your energy bill and maintain the necessary power needed no demand
- steiner multigraph optimization
- modelling interactions between plants as agents and optimize their placement and watering
- graphical probabilisitc models
- boosted regression trees