---
_id: '18159'
abstract:
- lang: eng
text: "Markov Decision Processes (MDPs) are a classical model for decision making
in the presence of uncertainty. Often they are viewed as state transformers with
planning objectives defned with respect to paths over MDP states. An increasingly\r\npopular
alternative is to view them as distribution transformers, giving rise to a sequence
of probability distributions over MDP states. For instance, reachability and safety
properties in modeling robot swarms or chemical reaction networks are naturally
defned in terms of probability distributions over states. Verifying such distributional
properties is known to be hard and often beyond the reach of classical state-based
verifcation techniques. In this work, we consider the problems of certifed policy
(i.e. controller) verifcation and synthesis in MDPs under distributional reach-avoidance
specifcations. By certifed we mean that, along with a policy, we also aim to synthesize
a (checkable) certifcate ensuring that the MDP indeed satisfes the property. Thus,
given the target set of distributions and an unsafe set of distributions over
MDP states, our goal is to either synthesize a certifcate for a given policy or
synthesize a policy along with a certifcate, proving that the target distribution
can be reached while avoiding unsafe distributions. To solve this problem, we
introduce the novel notion of distributional reach-avoid certifcates and present
automated procedures for (1) synthesizing a certifcate for a given policy, and
(2) synthesizing a policy together with the certifcate, both providing formal
guarantees on certifcate correctness. Our experimental evaluation demonstrates
the ability of our method to solve several non-trivial examples, including a multi-agent
robot-swarm model, to synthesize certifed policies and to certify existing policies. "
acknowledgement: This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt),
the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant,
Google Research Award 2023 and the SBI Foundation Hub for Data and Analytics.
article_processing_charge: No
arxiv: 1
author:
- first_name: S
full_name: Akshay, S
last_name: Akshay
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. Certified policy verification
and synthesis for MDPs under distributional reach-avoidance properties. In: Proceedings
of the Thirty-Third International Joint Conference on Artificial Intelligence.
International Joint Conferences on Artificial Intelligence; 2024:3-12. doi:10.24963/ijcai.2024/1'
apa: 'Akshay, S., Chatterjee, K., Meggendorfer, T., & Zikelic, D. (2024). Certified
policy verification and synthesis for MDPs under distributional reach-avoidance
properties. In Proceedings of the Thirty-Third International Joint Conference
on Artificial Intelligence (pp. 3–12). Jeju, Korea: International Joint Conferences
on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/1'
chicago: Akshay, S, Krishnendu Chatterjee, Tobias Meggendorfer, and Dorde Zikelic.
“Certified Policy Verification and Synthesis for MDPs under Distributional Reach-Avoidance
Properties.” In Proceedings of the Thirty-Third International Joint Conference
on Artificial Intelligence, 3–12. International Joint Conferences on Artificial
Intelligence, 2024. https://doi.org/10.24963/ijcai.2024/1.
ieee: S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “Certified policy
verification and synthesis for MDPs under distributional reach-avoidance properties,”
in Proceedings of the Thirty-Third International Joint Conference on Artificial
Intelligence, Jeju, Korea, 2024, pp. 3–12.
ista: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. 2024. Certified policy
verification and synthesis for MDPs under distributional reach-avoidance properties.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence.
IJCAI: International Joint Conference on Artificial Intelligence, 3–12.'
mla: Akshay, S., et al. “Certified Policy Verification and Synthesis for MDPs under
Distributional Reach-Avoidance Properties.” Proceedings of the Thirty-Third
International Joint Conference on Artificial Intelligence, International Joint
Conferences on Artificial Intelligence, 2024, pp. 3–12, doi:10.24963/ijcai.2024/1.
short: S. Akshay, K. Chatterjee, T. Meggendorfer, D. Zikelic, in:, Proceedings of
the Thirty-Third International Joint Conference on Artificial Intelligence, International
Joint Conferences on Artificial Intelligence, 2024, pp. 3–12.
conference:
end_date: 2024-08-09
location: Jeju, Korea
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2024-08-03
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-29T22:01:38Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2026-10-16T08:08:15Z
day: '01'
department:
- _id: KrCh
doi: 10.24963/ijcai.2024/1
ec_funded: 1
external_id:
arxiv:
- '2405.04015'
fulldoi: https://doi.org/10.24963/ijcai.2024/1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.04015
month: '09'
oa: 1
oa_version: Preprint
page: 3-12
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the Thirty-Third International Joint Conference on Artificial
Intelligence
publication_identifier:
isbn:
- '9781956792041'
issn:
- 1045-0823
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Certified policy verification and synthesis for MDPs under distributional reach-avoidance
properties
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18160'
abstract:
- lang: eng
text: 'Markov decision processes (MDPs) provide a standard framework for sequential
decision making under uncertainty. However, MDPs do not take uncertainty in transition
probabilities into account. Robust Markov decision processes (RMDPs) address this
shortcoming of MDPs by assigning to each transition an uncertainty set rather
than a single probability value. In this work, we consider polytopic RMDPs in
which all uncertainty sets are polytopes and study the problem of solving long-run
average reward polytopic RMDPs. We present a novel perspective on this problem
and show that it can be reduced to solving long-run average reward turn-based
stochastic games with finite state and action spaces. This reduction allows us
to derive several important consequences that were hitherto not known to hold
for polytopic RMDPs. First, we derive new computational complexity bounds for
solving long-run average reward polytopic RMDPs, showing for the first time that
the threshold decision problem for them is in NP∩CONP and that they admit a randomized
algorithm with sub-exponential expected runtime. Second, we present Robust Polytopic
Policy Iteration (RPPI), a novel policy iteration algorithm for solving long-run
average reward polytopic RMDPs. Our experimental evaluation shows that RPPI is
much more efficient in solving long-run average reward polytopic RMDPs compared
to state-of-the-art methods based on value iteration. '
acknowledgement: "This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt)
and the Czech Science Foundation\r\ngrant no. GA23-06963S."
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Ehsan
full_name: Kafshdar Goharshadi, Ehsan
id: 103b4fa0-896a-11ed-bdf8-87b697bef40d
last_name: Kafshdar Goharshadi
orcid: 0000-0002-8595-0587
- first_name: Mehrdad
full_name: Karrabi, Mehrdad
id: 67638922-f394-11eb-9cf6-f20423e08757
last_name: Karrabi
orcid: 0009-0007-5253-9170
- first_name: Petr
full_name: Novotný, Petr
id: 3CC3B868-F248-11E8-B48F-1D18A9856A87
last_name: Novotný
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Chatterjee K, Goharshady E, Karrabi M, Novotný P, Zikelic D. Solving long-run
average reward robust MDPs via stochastic games. In: 33rd International Joint
Conference on Artificial Intelligence. International Joint Conferences on
Artificial Intelligence; 2024:6707-6715. doi:10.24963/ijcai.2024/741'
apa: 'Chatterjee, K., Goharshady, E., Karrabi, M., Novotný, P., & Zikelic, D.
(2024). Solving long-run average reward robust MDPs via stochastic games. In 33rd
International Joint Conference on Artificial Intelligence (pp. 6707–6715).
Jeju, South Korea: International Joint Conferences on Artificial Intelligence.
https://doi.org/10.24963/ijcai.2024/741'
chicago: Chatterjee, Krishnendu, Ehsan Goharshady, Mehrdad Karrabi, Petr Novotný,
and Dorde Zikelic. “Solving Long-Run Average Reward Robust MDPs via Stochastic
Games.” In 33rd International Joint Conference on Artificial Intelligence,
6707–15. International Joint Conferences on Artificial Intelligence, 2024. https://doi.org/10.24963/ijcai.2024/741.
ieee: K. Chatterjee, E. Goharshady, M. Karrabi, P. Novotný, and D. Zikelic, “Solving
long-run average reward robust MDPs via stochastic games,” in 33rd International
Joint Conference on Artificial Intelligence, Jeju, South Korea, 2024, pp.
6707–6715.
ista: 'Chatterjee K, Goharshady E, Karrabi M, Novotný P, Zikelic D. 2024. Solving
long-run average reward robust MDPs via stochastic games. 33rd International Joint
Conference on Artificial Intelligence. IJCAI: International Joint Conference on
Artificial Intelligence, 6707–6715.'
mla: Chatterjee, Krishnendu, et al. “Solving Long-Run Average Reward Robust MDPs
via Stochastic Games.” 33rd International Joint Conference on Artificial Intelligence,
International Joint Conferences on Artificial Intelligence, 2024, pp. 6707–15,
doi:10.24963/ijcai.2024/741.
short: K. Chatterjee, E. Goharshady, M. Karrabi, P. Novotný, D. Zikelic, in:, 33rd
International Joint Conference on Artificial Intelligence, International Joint
Conferences on Artificial Intelligence, 2024, pp. 6707–6715.
conference:
end_date: 2024-08-09
location: Jeju, South Korea
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2024-08-03
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-29T22:01:39Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2026-10-16T08:07:05Z
day: '01'
department:
- _id: KrCh
doi: 10.24963/ijcai.2024/741
ec_funded: 1
external_id:
arxiv:
- '2312.13912'
fulldoi: https://doi.org/10.24963/ijcai.2024/741
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2312.13912
month: '09'
oa: 1
oa_version: Preprint
page: 6707-6715
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: 33rd International Joint Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781956792041'
issn:
- 1045-0823
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Solving long-run average reward robust MDPs via stochastic games
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '10847'
abstract:
- lang: eng
text: 'We study the two-player zero-sum extension of the partially observable stochastic
shortest-path problem where one agent has only partial information about the environment.
We formulate this problem as a partially observable stochastic game (POSG): given
a set of target states and negative rewards for each transition, the player with
imperfect information maximizes the expected undiscounted total reward until a
target state is reached. The second player with the perfect information aims for
the opposite. We base our formalism on POSGs with one-sided observability (OS-POSGs)
and give the following contributions: (1) we introduce a novel heuristic search
value iteration algorithm that iteratively solves depth-limited variants of the
game, (2) we derive the bound on the depth guaranteeing an arbitrary precision,
(3) we propose a novel upper-bound estimation that allows early terminations,
and (4) we experimentally evaluate the algorithm on a pursuit-evasion game.'
acknowledgement: "This research was supported by the Czech Science Foundation (no.
19-24384Y), by the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16 019/0000765
“Research Center for Informatics”, by the ERC CoG 863818 (ForM-SMArt), and by the
Combat Capabilities Development Command Army Research Laboratory and was accomplished
under Cooperative\r\nAgreement Number W911NF-13-2-0045 (ARL Cyber Security CRA).
The views and conclusions contained in this document are those of the authors and
should not be interpreted as\r\nrepresenting the official policies, either expressed
or implied, of the Combat Capabilities Development Command Army Research Laboratory
or the U.S. Government. The U.S. Government is authorized to reproduce and distribute
reprints for Government purposes not withstanding any copyright notation here on. "
article_processing_charge: No
author:
- first_name: Petr
full_name: Tomášek, Petr
last_name: Tomášek
- first_name: Karel
full_name: Horák, Karel
last_name: Horák
- first_name: Aditya
full_name: Aradhye, Aditya
last_name: Aradhye
- first_name: Branislav
full_name: Bošanský, Branislav
last_name: Bošanský
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: 'Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. Solving partially
observable stochastic shortest-path games. In: 30th International Joint Conference
on Artificial Intelligence. International Joint Conferences on Artificial
Intelligence; 2021:4182-4189. doi:10.24963/ijcai.2021/575'
apa: 'Tomášek, P., Horák, K., Aradhye, A., Bošanský, B., & Chatterjee, K. (2021).
Solving partially observable stochastic shortest-path games. In 30th International
Joint Conference on Artificial Intelligence (pp. 4182–4189). Virtual, Online:
International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/575'
chicago: Tomášek, Petr, Karel Horák, Aditya Aradhye, Branislav Bošanský, and Krishnendu
Chatterjee. “Solving Partially Observable Stochastic Shortest-Path Games.” In
30th International Joint Conference on Artificial Intelligence, 4182–89.
International Joint Conferences on Artificial Intelligence, 2021. https://doi.org/10.24963/ijcai.2021/575.
ieee: P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, and K. Chatterjee, “Solving
partially observable stochastic shortest-path games,” in 30th International
Joint Conference on Artificial Intelligence, Virtual, Online, 2021, pp. 4182–4189.
ista: 'Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. 2021. Solving partially
observable stochastic shortest-path games. 30th International Joint Conference
on Artificial Intelligence. IJCAI: International Joint Conferences on Artificial
Intelligence, 4182–4189.'
mla: Tomášek, Petr, et al. “Solving Partially Observable Stochastic Shortest-Path
Games.” 30th International Joint Conference on Artificial Intelligence,
International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–89,
doi:10.24963/ijcai.2021/575.
short: P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, K. Chatterjee, in:, 30th International
Joint Conference on Artificial Intelligence, International Joint Conferences on
Artificial Intelligence, 2021, pp. 4182–4189.
conference:
end_date: 2021-08-27
location: Virtual, Online
name: 'IJCAI: International Joint Conferences on Artificial Intelligence'
start_date: 2021-08-19
date_created: 2022-03-13T23:01:47Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2026-06-18T10:41:02Z
day: '01'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.24963/ijcai.2021/575
ec_funded: 1
fulldoi: https://doi.org/10.24963/ijcai.2021/575
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.24963/ijcai.2021/575
month: '09'
oa: 1
oa_version: Published Version
page: 4182-4189
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: 30th International Joint Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9780999241196'
issn:
- 1045-0823
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Solving partially observable stochastic shortest-path games
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '5977'
abstract:
- lang: eng
text: 'We consider the stochastic shortest path (SSP)problem for succinct Markov
decision processes(MDPs), where the MDP consists of a set of vari-ables, and a
set of nondeterministic rules that up-date the variables. First, we show that
several ex-amples from the AI literature can be modeled assuccinct MDPs. Then
we present computationalapproaches for upper and lower bounds for theSSP problem:
(a) for computing upper bounds, ourmethod is polynomial-time in the implicit descrip-tion
of the MDP; (b) for lower bounds, we present apolynomial-time (in the size of
the implicit descrip-tion) reduction to quadratic programming. Our ap-proach is
applicable even to infinite-state MDPs.Finally, we present experimental results
to demon-strate the effectiveness of our approach on severalclassical examples
from the AI literature.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Hongfei
full_name: Fu, Hongfei
id: 3AAD03D6-F248-11E8-B48F-1D18A9856A87
last_name: Fu
- first_name: Amir
full_name: Goharshady, Amir
id: 391365CE-F248-11E8-B48F-1D18A9856A87
last_name: Goharshady
orcid: 0000-0003-1702-6584
- first_name: Nastaran
full_name: Okati, Nastaran
last_name: Okati
citation:
ama: 'Chatterjee K, Fu H, Goharshady AK, Okati N. Computational approaches for stochastic
shortest path on succinct MDPs. In: Proceedings of the Twenty-Seventh International
Joint Conference on Artificial Intelligence. Vol 2018. IJCAI; 2018:4700-4707.
doi:10.24963/ijcai.2018/653'
apa: 'Chatterjee, K., Fu, H., Goharshady, A. K., & Okati, N. (2018). Computational
approaches for stochastic shortest path on succinct MDPs. In Proceedings of
the Twenty-Seventh International Joint Conference on Artificial Intelligence
(Vol. 2018, pp. 4700–4707). Stockholm, Sweden: IJCAI. https://doi.org/10.24963/ijcai.2018/653'
chicago: Chatterjee, Krishnendu, Hongfei Fu, Amir Kafshdar Goharshady, and Nastaran
Okati. “Computational Approaches for Stochastic Shortest Path on Succinct MDPs.”
In Proceedings of the Twenty-Seventh International Joint Conference on Artificial
Intelligence, 2018:4700–4707. IJCAI, 2018. https://doi.org/10.24963/ijcai.2018/653.
ieee: K. Chatterjee, H. Fu, A. K. Goharshady, and N. Okati, “Computational approaches
for stochastic shortest path on succinct MDPs,” in Proceedings of the Twenty-Seventh
International Joint Conference on Artificial Intelligence, Stockholm, Sweden,
2018, vol. 2018, pp. 4700–4707.
ista: 'Chatterjee K, Fu H, Goharshady AK, Okati N. 2018. Computational approaches
for stochastic shortest path on succinct MDPs. Proceedings of the Twenty-Seventh
International Joint Conference on Artificial Intelligence. IJCAI: International
Joint Conference on Artificial Intelligence vol. 2018, 4700–4707.'
mla: Chatterjee, Krishnendu, et al. “Computational Approaches for Stochastic Shortest
Path on Succinct MDPs.” Proceedings of the Twenty-Seventh International Joint
Conference on Artificial Intelligence, vol. 2018, IJCAI, 2018, pp. 4700–07,
doi:10.24963/ijcai.2018/653.
short: K. Chatterjee, H. Fu, A.K. Goharshady, N. Okati, in:, Proceedings of the
Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI,
2018, pp. 4700–4707.
conference:
end_date: 2018-07-19
location: Stockholm, Sweden
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2018-07-13
date_created: 2019-02-13T13:26:27Z
date_published: 2018-07-17T00:00:00Z
date_updated: 2026-10-16T22:30:59Z
day: '17'
department:
- _id: KrCh
doi: 10.24963/ijcai.2018/653
ec_funded: 1
external_id:
arxiv:
- '1804.08984'
isi:
- '000764175404118'
fulldoi: https://doi.org/10.24963/ijcai.2018/653
intvolume: ' 2018'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1804.08984
month: '07'
oa: 1
oa_version: Preprint
page: 4700-4707
project:
- _id: 25892FC0-B435-11E9-9278-68D0E5697425
grant_number: ICT15-003
name: Efficient Algorithms for Computer Aided Verification
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 2581B60A-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '279307'
name: 'Quantitative Graph Games: Theory and Applications'
publication: Proceedings of the Twenty-Seventh International Joint Conference on Artificial
Intelligence
publication_identifier:
isbn:
- '9780999241127'
issn:
- 1045-0823
publication_status: published
publisher: IJCAI
quality_controlled: '1'
related_material:
record:
- id: '8934'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Computational approaches for stochastic shortest path on succinct MDPs
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2018
year: '2018'
...
---
_id: '1003'
abstract:
- lang: eng
text: Network games (NGs) are played on directed graphs and are extensively used
in network design and analysis. Search problems for NGs include finding special
strategy profiles such as a Nash equilibrium and a globally optimal solution.
The networks modeled by NGs may be huge. In formal verification, abstraction has
proven to be an extremely effective technique for reasoning about systems with
big and even infinite state spaces. We describe an abstraction-refinement methodology
for reasoning about NGs. Our methodology is based on an abstraction function that
maps the state space of an NG to a much smaller state space. We search for a global
optimum and a Nash equilibrium by reasoning on an under- and an overapproximation
defined on top of this smaller state space. When the approximations are too coarse
to find such profiles, we refine the abstraction function. Our experimental results
demonstrate the efficiency of the methodology.
article_processing_charge: No
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Shibashis
full_name: Guha, Shibashis
last_name: Guha
- first_name: Orna
full_name: Kupferman, Orna
last_name: Kupferman
citation:
ama: 'Avni G, Guha S, Kupferman O. An abstraction-refinement methodology for reasoning
about network games. In: AAAI Press; 2017:70-76. doi:10.24963/ijcai.2017/11'
apa: 'Avni, G., Guha, S., & Kupferman, O. (2017). An abstraction-refinement
methodology for reasoning about network games (pp. 70–76). Presented at the IJCAI:
International Joint Conference on Artificial Intelligence , Melbourne, Australia:
AAAI Press. https://doi.org/10.24963/ijcai.2017/11'
chicago: Avni, Guy, Shibashis Guha, and Orna Kupferman. “An Abstraction-Refinement
Methodology for Reasoning about Network Games,” 70–76. AAAI Press, 2017. https://doi.org/10.24963/ijcai.2017/11.
ieee: 'G. Avni, S. Guha, and O. Kupferman, “An abstraction-refinement methodology
for reasoning about network games,” presented at the IJCAI: International Joint
Conference on Artificial Intelligence , Melbourne, Australia, 2017, pp. 70–76.'
ista: 'Avni G, Guha S, Kupferman O. 2017. An abstraction-refinement methodology
for reasoning about network games. IJCAI: International Joint Conference on Artificial
Intelligence , 70–76.'
mla: Avni, Guy, et al. An Abstraction-Refinement Methodology for Reasoning about
Network Games. AAAI Press, 2017, pp. 70–76, doi:10.24963/ijcai.2017/11.
short: G. Avni, S. Guha, O. Kupferman, in:, AAAI Press, 2017, pp. 70–76.
conference:
end_date: 2017-08-25
location: Melbourne, Australia
name: 'IJCAI: International Joint Conference on Artificial Intelligence '
start_date: 2017-08-19
date_created: 2018-12-11T11:49:38Z
date_published: 2017-05-30T00:00:00Z
date_updated: 2025-07-10T11:49:38Z
day: '30'
ddc:
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department:
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doi: 10.24963/ijcai.2017/11
external_id:
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language:
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page: 70 - 76
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title: An abstraction-refinement methodology for reasoning about network games
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...
---
_id: '11909'
abstract:
- lang: eng
text: This article presents a high-level discussion of some problems that are unique
to web search engines. The goal is to raise awareness and stimulate research in
these areas.
article_processing_charge: No
author:
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Rajeev
full_name: Motwani, Rajeev
last_name: Motwani
- first_name: Craig
full_name: Silverstein, Craig
last_name: Silverstein
citation:
ama: 'Henzinger M, Motwani R, Silverstein C. Challenges in web search engines. In:
18th International Joint Conference on Artificial Intelligence. Association
for Computing Machinery; 2003:1573-1579.'
apa: 'Henzinger, M., Motwani, R., & Silverstein, C. (2003). Challenges in web
search engines. In 18th International Joint Conference on Artificial Intelligence
(pp. 1573–1579). Acapulco, Mexico: Association for Computing Machinery.'
chicago: Henzinger, Monika, Rajeev Motwani, and Craig Silverstein. “Challenges in
Web Search Engines.” In 18th International Joint Conference on Artificial Intelligence,
1573–79. Association for Computing Machinery, 2003.
ieee: M. Henzinger, R. Motwani, and C. Silverstein, “Challenges in web search engines,”
in 18th International Joint Conference on Artificial Intelligence, Acapulco,
Mexico, 2003, pp. 1573–1579.
ista: 'Henzinger M, Motwani R, Silverstein C. 2003. Challenges in web search engines.
18th International Joint Conference on Artificial Intelligence. IJCAI: International
Joint Conference on Artificial Intelligence, 1573–1579.'
mla: Henzinger, Monika, et al. “Challenges in Web Search Engines.” 18th International
Joint Conference on Artificial Intelligence, Association for Computing Machinery,
2003, pp. 1573–79.
short: M. Henzinger, R. Motwani, C. Silverstein, in:, 18th International Joint Conference
on Artificial Intelligence, Association for Computing Machinery, 2003, pp. 1573–1579.
conference:
end_date: 2003-08-15
location: Acapulco, Mexico
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2003-08-09
date_created: 2022-08-18T06:40:02Z
date_published: 2003-08-01T00:00:00Z
date_updated: 2024-11-06T08:13:06Z
day: '01'
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ijcai.org/Proceedings/03/Papers/278.pdf
month: '08'
oa: 1
oa_version: Published Version
page: 1573-1579
publication: 18th International Joint Conference on Artificial Intelligence
publication_identifier:
issn:
- 1045-0823
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Challenges in web search engines
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2003'
...