Decision-Theoretic Planning with Concurrent Temporally Extended Actions

The Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI01), August 3-5, 2001, University of Washington, Seattle, Washington, USA
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
Khashayar Rohanimanesh, Sridhar Mahadevan

We investigate a model for planning under uncertainty with temporally extended actions, where multiple actions can be taken concurrently at each decision epoch. Our model is based on the options framework, and combines it with factored state space models, where the set of options can be partitioned into classes that affect disjoint state variables.

We show that the set of decision epochs for concurrent options defines a semi-Markov decision process, if the underlying temporally extended actions being parallelized are restricted to Markov options.

This property allows us to use SMDPalgorithms for computing the value function over concurrent options. The concurrent options model allows overlapping execution of options in order to achieve higher performance or in order to perform a complex task.

We describe a simple experiment using a navigation task which illustrates how concurrent options results in a faster plan when compared to the case when only one option is taken at a time.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.