Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
Computational modeling of human belief maintenance and decision-making processes has become increasingly important for a wide range of applications. We present a framework for mod...
Jonathan Y. Ito, David V. Pynadath, Stacy C. Marse...
We show how to use linear belief functions to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations...
We propose an agent-based architecture that allows context-aware communication between users. In seeking a model that is suitable for the design of the required functionalities of...
We investigate the discrete (finite) case of the Popper-Renyi theory of conditional probability, introducing discrete conditional probabilistic models for (multi-agent) knowledge...