Anytime bounded rationality

Eric Nivel; Kristinn R. Thórisson; Bas Steunebrink; Jürgen Schmidhuber

doi:10.1007/978-3-319-21365-1_13

Anytime bounded rationality

Eric Nivel, Kristinn R. Thórisson, Bas Steunebrink, Jürgen Schmidhuber

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

9 Scopus citations

Abstract

Dependable cyber-physical systems strive to deliver anticipative, multi-objective performance anytime, facing deluges of inputs with varying and limited resources. This is even more challenging for life-long learning rational agents as they also have to contend with the varying and growing know-how accumulated from experience. These issues are of crucial practical value, yet have been only marginally and unsatisfactorily addressed in AGI research. We present a value-driven computational model of anytime bounded rationality robust to variations of both resources and knowledge. It leverages continually learned knowledge to anticipate, revise and maintain concurrent courses of action spanning over arbitrary time scales for execution anytime necessary.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer [email protected]
Pages	121-130
Number of pages	10
ISBN (Print)	9783319213644
DOIs	https://doi.org/10.1007/978-3-319-21365-1_13
State	Published - Jan 1 2015
Externally published	Yes

Access to Document

10.1007/978-3-319-21365-1_13

Cite this

@inproceedings{ccf40ebba129495aa9ed2c2e6cf74d64,

title = "Anytime bounded rationality",

abstract = "Dependable cyber-physical systems strive to deliver anticipative, multi-objective performance anytime, facing deluges of inputs with varying and limited resources. This is even more challenging for life-long learning rational agents as they also have to contend with the varying and growing know-how accumulated from experience. These issues are of crucial practical value, yet have been only marginally and unsatisfactorily addressed in AGI research. We present a value-driven computational model of anytime bounded rationality robust to variations of both resources and knowledge. It leverages continually learned knowledge to anticipate, revise and maintain concurrent courses of action spanning over arbitrary time scales for execution anytime necessary.",

author = "Eric Nivel and Th{\'o}risson, {Kristinn R.} and Bas Steunebrink and J{\"u}rgen Schmidhuber",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-14",

year = "2015",

month = jan,

day = "1",

doi = "10.1007/978-3-319-21365-1_13",

language = "English (US)",

isbn = "9783319213644",

pages = "121--130",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer [email protected]",

}

TY - GEN

T1 - Anytime bounded rationality

AU - Nivel, Eric

AU - Thórisson, Kristinn R.

AU - Steunebrink, Bas

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Dependable cyber-physical systems strive to deliver anticipative, multi-objective performance anytime, facing deluges of inputs with varying and limited resources. This is even more challenging for life-long learning rational agents as they also have to contend with the varying and growing know-how accumulated from experience. These issues are of crucial practical value, yet have been only marginally and unsatisfactorily addressed in AGI research. We present a value-driven computational model of anytime bounded rationality robust to variations of both resources and knowledge. It leverages continually learned knowledge to anticipate, revise and maintain concurrent courses of action spanning over arbitrary time scales for execution anytime necessary.

AB - Dependable cyber-physical systems strive to deliver anticipative, multi-objective performance anytime, facing deluges of inputs with varying and limited resources. This is even more challenging for life-long learning rational agents as they also have to contend with the varying and growing know-how accumulated from experience. These issues are of crucial practical value, yet have been only marginally and unsatisfactorily addressed in AGI research. We present a value-driven computational model of anytime bounded rationality robust to variations of both resources and knowledge. It leverages continually learned knowledge to anticipate, revise and maintain concurrent courses of action spanning over arbitrary time scales for execution anytime necessary.

UR - http://link.springer.com/10.1007/978-3-319-21365-1_13

UR - http://www.scopus.com/inward/record.url?scp=84952782113&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-21365-1_13

DO - 10.1007/978-3-319-21365-1_13

M3 - Conference contribution

SN - 9783319213644

SP - 121

EP - 130

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer [email protected]

ER -

Anytime bounded rationality

Abstract

Access to Document

Other files and links

Fingerprint

Cite this