Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the workerabilities by optimizing an objective function, for instance, by maximizing th...
In many learning tasks with structural properties, structural sparsity methods help induce sparse models, usually leading to better interpretability and higher generalization perf...
PDDL+ planning involves reasoning about mixed discretecontinuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique th...
Daniel Bryce, Sicun Gao, David J. Musliner, Robert...
Teams of mobile robots often need to divide up subtasks efficiently. In spatial domains, a key criterion for doing so may depend on distances between robots and the subtasks’ l...
Monte Carlo planning has been proven successful in many sequential decision-making settings, but it suffers from poor exploration when the rewards are sparse. In this paper, we im...
Sriram Srinivasan, Erik Talvitie, Michael H. Bowli...
Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context...
We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of...
Commitment protocols provide an effective formalism for the regulation of agent interaction. Although existing work mainly focus on the design-time development of static commitmen...
Operator cost partitioning is a well-known technique to make admissible heuristics additive by distributing the operator costs among individual heuristics. Planning tasks are usua...
Latent author attribute prediction in social media provides a novel set of conditions for the construction of supervised classification models. With individual authors as trainin...