This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

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 influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

This book constitutes the refereed proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009, held in La Laguna, Canary Islands, Spain, in November 2011. The 50 revised full papers presented were carefully selected from 149 submissions. The papers are organized in topical sections on agent-based and multi-agent systems; machine learning; knowledge representation, logic, search and planning; multidisciplinary topics and applications; vision and robotics; soft computing; Web intelligence and information retrieval.

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 influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, held in Salamanca, Spain, in September 2016. The 47 revised full papers presented were carefully selected from 166 submissions. Apart from the presentation of technical full papers, the scientific program of CAEPIA 2016 included an App contest, a Doctoral Consortium and, as a follow-up to the success achieved in previously CAEPIA editions, a special session on outstanding recent papers (Key Works) already published in renowned journals or forums.

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015, held in Albacete, Spain, in November 2015. The 31 revised full papers presented were carefully selected from 175 submissions. The papers are organized in topical sections on Bayesian networks and uncertainty modeling; fuzzy logic and soft computing; knowledge representation, reasoning, and logic; intelligent systems and environment; intelligent Web and recommender systems; machine learning and data mining; metaheuristics and evolutionary computation; and social robotics.