@conference {1502827,
	title = {Online Planning for Optimal Protector Strategies in Resource Conservation Games },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)},
	year = {2014},
	abstract = {Protecting our environment and natural resources is a major global
challenge. {\textquotedblleft}Protectors{\textquotedblright} (law enforcement agencies) try to protect
these natural resources, while {\textquotedblleft}extractors{\textquotedblright} (criminals) seek to exploit them. In many domains, such as illegal fishing, the extractors
know more about the distribution and richness of the resources than
the protectors, making it extremely difficult for the protectors to optimally allocate their assets for patrol and interdiction. Fortunately,
extractors carry out frequent illegal extractions, so protectors can
learn about the richness of resources by observing the extractor{\textquoteright}s
behavior. This paper presents an approach for allocating protector
assets based on learning from extractors. We make the following
four specific contributions: (i) we model resource conservation as a
repeated game; (ii) we transform this repeated game into a POMDP
by adopting a fixed model for the adversary{\textquoteright}s behavior, which cannot be solved by the latest general POMDP solvers due to its exponential state space; (iii) in response, we propose GMOP, a dedicated algorithm that combines Gibbs sampling with Monte Carlo
tree search for online planning in this POMDP; (iv) for a specific
class of our game, we can speed up the GMOP algorithm without
sacrificing solution quality, as well as provide a heuristic that trades
off solution quality for lower computational cost.},
	author = {Qian, Yundi and William B. Haskell and Xin Jiang, Albert and Tambe, Milind}
}
