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AIPS
2008
15 years 9 months ago
Learning Relational Decision Trees for Guiding Heuristic Planning
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Tomás de la Rosa, Sergio Jiménez, Da...
IJMMS
1998
153views more  IJMMS 1998»
15 years 6 months ago
Brahms: simulating practice for work systems design
A continuing problem in business today is the design of human-computer systems that respect how work actually gets done. The overarching context of work consists of activities, wh...
William J. Clancey, Patricia Sachs, Maarten Sierhu...
PE
2010
Springer
102views Optimization» more  PE 2010»
15 years 5 months ago
Extracting state-based performance metrics using asynchronous iterative techniques
Solution of large sparse linear fixed-point problems lies at the heart of many important performance analysis calculations. These calculations include steady-state, transient and...
Douglas V. de Jager, Jeremy T. Bradley
JAIR
2011
134views more  JAIR 2011»
15 years 1 months ago
Scaling up Heuristic Planning with Relational Decision Trees
Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are wort...
Tomás de la Rosa, Sergio Jiménez, Ra...
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
16 years 7 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto