TY  - JOUR
A1  - Gebser, Martin
A1  - Obermeier, Philipp
A1  - Otto, Thomas
A1  - Schaub, Torsten H.
A1  - Sabuncu, Orkunt
A1  - Van Nguyen, 
A1  - Tran Cao Son, 
T1  - Experimenting with robotic intra-logistics domains
JF  - Theory and practice of logic programming
N2  - We introduce the asprilo1 framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return, asprilo allows users to study alternative solutions as regards effectiveness and scalability. Although asprilo relies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely, asprilo consists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000200
SN  - 1471-0684
SN  - 1475-3081
VL  - 18
IS  - 3-4
SP  - 502
EP  - 519
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Fandinno, 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  - JOUR
A1  - Tran, Son Cao
A1  - Pontelli, Enrico
A1  - Balduccini, Marcello
A1  - Schaub, Torsten
T1  - Answer set planning
BT  - a survey
JF  - Theory and practice of logic programming
N2  - Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.
KW  - planning
KW  - knowledge representation and reasoning
KW  - logic programming
Y1  - 2022
U6  - https://doi.org/10.1017/S1471068422000072
SN  - 1471-0684
SN  - 1475-3081
PB  - Cambridge University Press
CY  - New York
ER  -