TY  - JOUR
A1  - Fandiño, Jorge
A1  - Laferriere, Francois
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Son, Tran Cao
T1  - Planning with incomplete information in quantified answer set programming
JF  - Theory and practice of logic programming
N2  - We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.
KW  - answer set programming
KW  - planning
KW  - quantified logics
Y1  - 2021
U6  - https://doi.org/10.1017/S1471068421000259
SN  - 1471-0684
SN  - 1475-3081
VL  - 21
IS  - 5
SP  - 663
EP  - 679
PB  - Cambridge University Press
CY  - Cambridge
ER  - 
TY  - CHAP
A1  - Mirbabaie, Milad
A1  - Rieskamp, Jonas
A1  - Hofeditz, Lennart
A1  - Stieglitz, Stefan
ED  - Bui, Tung X.
T1  - Breaking down barriers
BT  - how conversational agents facilitate open science and data sharing
T2  - Proceedings of the 57th Annual Hawaii International Conference on System Sciences
N2  - Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one's own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect.
KW  - open science practices in information systems research
KW  - conversational agents
KW  - data sharing
KW  - digital nudging
KW  - open science
KW  - research data management
Y1  - 2024
UR  - https://hdl.handle.net/10125/106457
SN  - 978-0-99813-317-1
SP  - 672
EP  - 681
PB  - Department of IT Management Shidler College of Business University of Hawaii
CY  - Honolulu, HI
ER  -