Learn More
In many cases several entities, such as commercial companies, need to work together towards the achievement of joint goals, while hiding certain private information. To collaborate effectively, some sort of plan is needed to coordinate the different entities. We address the problem of automatically generating such a coordination plan while preserving the(More)
The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly due to the idea of restricting value function computations(More)
Many Recommender Systems use either Collaborative Filtering (CF) or Content-Based (CB) techniques to receive recommendations for products. Both approaches have advantages and weaknesses. Combining the two approaches together can overcome most weaknesses. However, most hybrid systems combine the two methods in an ad-hoc manner. In this paper we present an(More)
We address the pruning or filtering problem, encountered in exact value iteration in POMDPs and elsewhere, in which a collection of linear functions is reduced to the minimal subset retaining the same maximal surface. We introduce the Skyline algorithm, which traces the graph corresponding to the maximal surface. The algorithm has both a complete and an(More)
Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods which quickly provide approximate solutions for medium-sized problems. New multi-core machines offer an opportunity to scale up to much larger domains. These machines support parallel execution and can speed up existing algorithms considerably. In this(More)
  • 1