@inbook{93c712122e0247e58f28eed979485622,

title = "Different Kinds of Effect Estimators",

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 State Change AttemptTrie-enhanced Optimal optimal state change attempts when using a special kind of effect estimator.",

keywords = "Effect Estimator, Goal Condition, Leaf Node, Negative Instance, Recursive Call",

author = "Austin Parker and Simari, {Gerardo I.} and Amy Sliva and Subrahmanian, {V. S.}",

note = "Publisher Copyright: {\textcopyright} 2014, The Author(s).",

year = "2014",

doi = "10.1007/978-1-4939-0274-3_3",

language = "English (US)",

series = "SpringerBriefs in Computer Science",

publisher = "Springer",

number = "9781493902736",

pages = "19--29",

booktitle = "SpringerBriefs in Computer Science",

edition = "9781493902736",

}