In this paper, we address the problem of building a system of autonomous agents for a complex environment, in our case, a museum with many visitors. Visitors may have varying pref...
Andrew S. Cantino, David L. Roberts, Charles L. Is...
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
The ability for an agent to reason under uncertainty is crucial for many planning applications, since an agent rarely has access to complete, error-free information about its envi...
In this paper, we study a particular subclass of partially observable models, called quasi-deterministic partially observable Markov decision processes (QDET-POMDPs), characterize...
Multi-agent systems are an increasingly important software paradigm and in many of its applications agents cooperate to achieve a particular goal. This requires the design of effi...
Taolue Chen, Marta Z. Kwiatkowska, David Parker, A...