Abstract. Despite the recent advances in planning for classical domains, the question of how to use domain knowledge in planning is yet to be completely and clearly answered. Some ...
Alfonso Gerevini, Ugur Kuter, Dana S. Nau, Alessan...
Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...
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
Our research focuses on web information management for people who want to monitor and use the World Wide Web (WWW) information, as their information resource. Web information is m...
The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision proces...
Ranjit Nair, Milind Tambe, Makoto Yokoo, David V. ...