One of the more controversial recent planning algorithms is the SHOP algorithm, an HTN planning algorithm that plans for tasks in the same order that they are to be executed. SHOP...
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
For many supervised learning tasks it is very costly to produce training data with class labels. Active learning acquires data incrementally, at each stage using the model learned...
Combinatorial auctions where bidders can bid on bundles of items can lead to more economical allocations, but determining the winners is NP-complete and inapproximable. We present...
Tuomas Sandholm, Subhash Suri, Andrew Gilpin, Davi...