We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...
This paper deals with the monitoring and diagnosis of large discrete-event systems. The problem is to determine, online, all faults and states that explain the flow of observatio...
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Most algorithms for computing diagnoses within a modelbased diagnosis framework are deterministic. Such algorithms guarantee soundness and completeness, but are P 2 hard. To overc...
Alexander Feldman, Gregory M. Provan, Arjan J. C. ...
This paper addresses the problem of plan recognition for multi-agent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. ...