Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
- The kinematic filter is a common tool in control and signal processing applications dealing with position, velocity and other kinematical variables. Usually the filter gain is gi...
Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
The enormous interest for peer-to-peer systems in recent years has prompted research into finding scalable and robust seeding and searching methods to support these overlay netwo...
Bow-Nan Cheng, Murat Yuksel, Shivkumar Kalyanarama...