Abstract
In this chapter we introduce several sorts of effect estimator, which yield the likelihood of a given action tuple satisfying a given goal condition G. An effect estimator essentially answers the question: “if I succeed in changing the environment in this way, what is the probability that the environment satisfies my goal?”. We also present the TOSCA algorithm, an optimized approach to computing optimal state change attempts when using a special kind of effect estimator.
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Parker, A., Simari, G.I., Sliva, A., Subrahmanian, V.S. (2014). Different Kinds of Effect Estimators. In: Data-driven Generation of Policies. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0274-3_3
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DOI: https://doi.org/10.1007/978-1-4939-0274-3_3
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Online ISBN: 978-1-4939-0274-3
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