Different Kinds of Effect Estimators

Austin Parker*, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages19-29
Number of pages11
Edition9781493902736
DOIs
StatePublished - 2014
Externally publishedYes

Publication series

NameSpringerBriefs in Computer Science
Number9781493902736
Volume0
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

Keywords

  • Effect Estimator
  • Goal Condition
  • Leaf Node
  • Negative Instance
  • Recursive Call

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Different Kinds of Effect Estimators'. Together they form a unique fingerprint.

Cite this