Accepted Papers
Uncertainty in AI
A trust prediction approach capturing agents’ dynamic behavior
Xin Liu and Anwitaman Datta
Bayesian Chain Classifiers for Multidimensional Classification
Julio Zaragoza, Enrique Sucar, Eduardo Morales, Pedro Larrañaga and Concha Bielza
Eliciting Additive Reward Functions for Markov Decision Processes
Kevin Regan and Craig Boutilier
Finding (?,?)-solutions via sampled SCSPs
Roberto Rossi, Brahim Hnich, S. Armagan Tarim and Steven Prestwich
Inference with multinomial data: why to weaken the prior strength
Cassio de Campos and Alessio Benavoli
Learning Optimal Bayesian Networks Using A* Search
Changhe Yuan, Brandon Malone and Xiaojian Wu
Lifted Probabilistic Inference by First-Order Knowledge Compilation
Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and Luc de Raedt
Lifted Relational Kalman Filtering
Jaesik Choi, Abner Guzman and Eyal Amir
Log-Linear Description Logics
Mathias Niepert, Jan Noessner and Heiner Stuckenschmidt
Motor Simulation via Coupled Internal Models using Sequential Monte Carlo
Haris Dindo, Daniele Zambuto and Giovanni Pezzulo
New Complexity Results for MAP in Bayesian Networks
Cassio de Campos
Pairwise decomposition for combinatorial optimization in graphical models
Aurélie Favier and Simon de Givry
Randomized Sensing in Adversarial Environments
Andreas Krause, Alex Roper and Daniel Golovin
Resolute Choice in Sequential Decision Problems with Multiple Priors
Helene Fargier, Gildas Jeantet and Olivier Spanjaard
Robust Online Optimization of Reward-uncertain MDPs
Kevin Regan and Craig Boutilier
Scalable Multiagent Planning Using Probabilistic Inference
Akshat Kumar, Shlomo Zilberstein and Marc Toussaint