There is a growing need for efficient and scalable semantic web queries that handle inference. There is also a growing interest in representing uncertainty in semantic web knowled...
Modeling human behavior requires vast quantities of accurately labeled training data, but for ubiquitous people-aware applications such data is rarely attainable. Even researchers...
Daniel Peebles, Hong Lu, Nicholas D. Lane, Tanzeem...
Trial-based approaches offer an efficient way to solve singleagent MDPs and POMDPs. These approaches allow agents to focus their computations on regions of the environment they en...
A general game player is a system that understands the rules of unknown games and learns to play these games well without human intervention. A major challenge for research in Gen...
Labeling nodes in a network is an important problem that has seen a growing interest. A number of methods that exploit both local and relational information have been developed fo...
Multiple Kernel Learning (MKL) can be formulated as a convex-concave minmax optimization problem, whose saddle point corresponds to the optimal solution to MKL. Most MKL methods e...
Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu...
Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In...
In computational social choice, one important problem is to take the votes of a subelectorate (subset of the voters), and summarize them using a small number of bits. This needs t...
Multi-agent decision problems, in which independent agents have to agree on a joint plan of action or allocation of resources, are central to AI. In such situations, agents' ...
Reshef Meir, Maria Polukarov, Jeffrey S. Rosensche...