Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...
Information is indispensable in preparing economic decisions purposefully. In this paper knowledge is represented by a probability distribution. Knowledge acquisition is realized ...
There is a growing interest in intelligent assistants for a variety of applications from organizing tasks for knowledge workers to helping people with dementia. In our earlier wor...
Preferences in constraint problems are common but significant in many real world applications. In this paper, we extend our conditional and composite CSP (CCCSP) framework, managi...