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6 papers · 2 filters
Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games
Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich +1
In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent's disposition is limited to wh…
Learning Diagnostic Policies from Examples by Systematic Search
Valentina Bayer-Zubek
A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform t…
On the Robustness of Most Probable Explanations
Hei Chan, Adnan Darwiche
In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with a highest probability given the current evidence. In this paper, we discuss the pr…
Learning Bayesian Network Structure from Correlation-Immune Data
Eric Lantz, Soumya Ray, David Page
Searching the complete space of possible Bayesian networks is intractable for problems of interesting size, so Bayesian network structure learning algorithms, such as the commonly…
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams
Ethan W. Dereszynski, Thomas G. Dietterich
Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate u…
Hybrid Batch Bayesian Optimization
Javad Azimi, Ali Jalali, Xiaoli Fern
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function eva…