Conclusions

Austin Parker; Gerardo I. Simari; Amy Sliva; V. S. Subrahmanian

doi:10.1007/978-1-4939-0274-3_6

Conclusions

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The AI planning literature contains decades of substantial work on discovering sequences of actions that lead to a given outcome that, similar to this work, is often specified as a goal condition.

Original language	English (US)
Title of host publication	SpringerBriefs in Computer Science
Publisher	Springer
Pages	47-48
Number of pages	2
Edition	9781493902736
DOIs	https://doi.org/10.1007/978-1-4939-0274-3_6
State	Published - 2014
Externally published	Yes

Publication series

Name	SpringerBriefs in Computer Science
Number	9781493902736
Volume	0
ISSN (Print)	2191-5768
ISSN (Electronic)	2191-5776

Keywords

Goal Condition
Markov Decision Process
Planning Approach
School Performance
Type Action

ASJC Scopus subject areas

Computer Science(all)

Access to Document

10.1007/978-1-4939-0274-3_6

Cite this

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title = "Conclusions",

abstract = "The AI planning literature contains decades of substantial work on discovering sequences of actions that lead to a given outcome that, similar to this work, is often specified as a goal condition.",

keywords = "Goal Condition, Markov Decision Process, Planning Approach, School Performance, Type Action",

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KW - Markov Decision Process

KW - Planning Approach

KW - School Performance

KW - Type Action

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Conclusions

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this