In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of uncertain, incomplete, or inconsistent information. A variety of formalisms have been developed, including nonmonotonic logic, fuzzy sets, possibility theory, belief functions, and dynamic models of reasoning such as belief revision and Bayesian networks.
In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of unc...
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of...
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical developm...
This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017.
The 24 full and 6 short papers presented in this volume were carefully reviewed and selected from 35 submissions. The book also contains 3 invited papers. Managing uncertainty and inconsistency has been extensively explored in Artificial Intelligence over a number of years. Now, with the advent of massive amounts of data and knowledge from distributed, heterogeneous, and potentially conflicting sources,...
This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Gr...
