@article{GebserObermeierOttoetal.2018,
  author    = {Gebser, Martin and Obermeier, Philipp and Otto, Thomas and Schaub, Torsten H. and Sabuncu, Orkunt and Van Nguyen, and Tran Cao Son,},
  title     = {Experimenting with robotic intra-logistics domains},
  series = {Theory and practice of logic programming},
  volume    = {18},
  journal   = {Theory and practice of logic programming},
  number    = {3-4},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068418000200},
  pages     = {502 -- 519},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{FandinnoLaferriereRomeroetal.2021,
  author    = {Fandinno, Jorge and Laferriere, Francois and Romero, Javier and Schaub, Torsten H. and Son, Tran Cao},
  title     = {Planning with incomplete information in quantified answer set programming},
  series = {Theory and practice of logic programming},
  volume    = {21},
  journal   = {Theory and practice of logic programming},
  number    = {5},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068421000259},
  pages     = {663 -- 679},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{TranPontelliBalduccinietal.2022,
  author    = {Tran, Son Cao and Pontelli, Enrico and Balduccini, Marcello and Schaub, Torsten},
  title     = {Answer set planning},
  series = {Theory and practice of logic programming},
  journal   = {Theory and practice of logic programming},
  publisher = {Cambridge University Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068422000072},
  pages     = {73},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}