A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
We study the problem of fairly dividing a set of goods amongst a group of agents, when those agents have preferences that are ordinal relations over alternative bundles of goods (r...
Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of co...
As part of the efforts put in understanding the intricacies of the k-colorability problem, different distributions over k-colorable graphs were analyzed. While the problem is notor...
We study the complexity of cost-optimal classical planning over propositional state variables and unary-effect actions. We discover novel problem fragments for which such optimiza...