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...
Boolean linear programs (BLPs) are ubiquitous in AI. Satisfiability testing, planning with resource constraints, and winner determination in combinatorial auctions are all example...
Dale Schuurmans, Finnegan Southey, Robert C. Holte
We consider an on-line decision-theoretic interpreter and incremental execution of Golog programs. This new interpreter is intended to overcome some limitations of the off-line in...
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...