We study the complexity of influencing elections through bribery: How computationally complex is it for an external actor to determine whether by a certain amount of bribing voter...
Piotr Faliszewski, Edith Hemaspaandra, Lane A. Hem...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decisi...
We introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well stu...
As embedded systems grow increasingly complex, there is a pressing need for diagnosing and monitoring capabilities that estimate the system state robustly. This paper is based on ...
We establish a declarative theory of forgetting for disjunctive logic programs. The suitability of this theory is justified by a number of desirable properties. In particular, one...
Many planning and design problems can be characterized as optimal search over a constrained network of conditional choices with preferences. To draw upon the advanced methods of c...
LOCATE is workspace layout design software that also serves as a testbed for developing and refining principles of adaptive aiding. This demonstration illustrates LOCATE's ab...
This paper considers strategies for external memory based optimal planning. An external breadth-first search exploration algorithm is devised that is guaranteed to find the costop...