Testing embedded software systems on the control units of vehicles is a safety-relevant task, and developing the test suites for performing the tests on test benches is time-consu...
The PDDL3 specifications include soft goals and trajectory constraints for distinguishing highquality plans among the many feasible plans in a solution space. To reduce the compl...
Chih-Wei Hsu, Benjamin W. Wah, Ruoyun Huang, Yixin...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
We show that a simple procedure based on maximizing the number of informative content-words can produce some of the best reported results for multi-document summarization. We fir...
Time series data abounds in real world problems. Measuring the similarity of time series is a key to solving these problems. One state of the art measure is the longest common sub...
This paper investigates how to represent and solve multiagent task scheduling as a Distributed Constraint Optimization Problem (DCOP). Recently multiagent researchers have adopted...
Evan Sultanik, Pragnesh Jay Modi, William C. Regli
Mechanism design is the study of preference aggregation protocols that work well in the face of self-interested agents. We present the first general-purpose techniques for automa...
Tuomas Sandholm, Vincent Conitzer, Craig Boutilier
Evaluation measures play an important role in machine learning because they are used not only to compare different learning algorithms, but also often as goals to optimize in cons...
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...