We derive a knowledge gradient policy for an optimal learning problem on a graph, in which we use sequential measurements to refine Bayesian estimates of individual edge values i...
The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their ...
Simple lexicographic decision heuristics that consider cues one at a time in a particular order and stop searching for cues as soon as a decision can be made have been shown to be...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
This study demonstratesthe use of decision tree classifiers as the basis for a general gene-finding system. The system uses a dynamic programmingalgorithm that. finds the optimal ...
Steven Salzberg, Xin Chen, John Henderson, Kenneth...