bibliography.bib

@article{10.1111/coin.12079,
author = {\v{C}ern\'{y}, Martin and Bart\'{a}k, Roman and Brom, Cyril and Gemrot, Jakub},
title = {To Plan or to Simply React? {A}n Experimental Study of Action Planning in a Game Environment},
year = {2016},
issue_date = {November 2016},
publisher = {Blackwell Publishers, Inc.},
address = {USA},
volume = {32},
number = {4},
issn = {0824-7935},
url = {https://doi.org/10.1111/coin.12079},
doi = {10.1111/coin.12079},
journal = {Comput. Intell.},
month = nov,
pages = {668–710},
numpages = {43},
keywords = {intelligent virtual agents, dynamic environments, action planning, delete-free planning, comparison}
}

@book{10.5555/3073924,
author = {Ghallab, Malik and Nau, Dana and Traverso, Paolo},
title = {Automated Planning and Acting},
year = {2016},
isbn = {1107037271},
publisher = {Cambridge University Press},
address = {USA},
edition = {1st}
}

@inproceedings{Churchill2011BuildOO,
  title={Build Order Optimization in StarCraft},
  author={David Churchill and Michael Buro},
  booktitle={AIIDE},
  year={2011}
}

@InProceedings{10.1007/978-3-540-74141-1_12,
author="Onta{\~{n}}{\'o}n, Santiago
and Mishra, Kinshuk
and Sugandh, Neha
and Ram, Ashwin",
editor="Weber, Rosina O.
and Richter, Michael M.",
title="Case-Based Planning and Execution for Real-Time Strategy Games",
booktitle="Case-Based Reasoning Research and Development",
year="2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="164--178",
abstract="Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal with RTS games. We propose to extract behavioral knowledge from expert demonstrations in form of individual cases. This knowledge can be reused via a case based behavior generator that proposes behaviors to achieve the specific open goals in the current plan. Specifically, we applied our technique to the WARGUS domain with promising results.",
isbn="978-3-540-74141-1"
}

@inproceedings{Weber2011BuildingHA,
  title={Building Human-Level AI for Real-Time Strategy Games},
  author={Ben George Weber and Michael Mateas and Arnav Jhala},
  booktitle={AAAI Fall Symposium: Advances in Cognitive Systems},
  year={2011}
}

@inproceedings{Alczar2010peleaP,
  title={pelea : Planning , Learning and Execution Architecture},
  author={Vidal Alc{\'a}zar},
  year={2010}
}

@article{Aha_2018, title={Goal Reasoning: Foundations, Emerging Applications, and Prospects}, volume={39}, url={https://www.aaai.org/ojs/index.php/aimagazine/article/view/2800}, DOI={10.1609/aimag.v39i2.2800}, abstractNote={<p class="Text">Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.</p>}, number={2}, journal={AI Magazine}, author={Aha, David W.}, year={2018}, month={Jul.}, pages={3-24} }

@article{pddl,
author = {Ghallab, Malik and Knoblock, Craig and Wilkins, David and Barrett, Anthony and Christianson, Dave and Friedman, Marc and Kwok, Chung and Golden, Keith and Penberthy, Scott and Smith, David and Sun, Ying and Weld, Daniel},
year = {1998},
month = {08},
pages = {},
title = {PDDL - The Planning Domain Definition Language}
}

@ARTICLE{7038214,  author={D. {Perez-Liebana} and S. {Samothrakis} and J. {Togelius} and T. {Schaul} and S. M. {Lucas} and A. {Couëtoux} and J. {Lee} and C. {Lim} and T. {Thompson}},  journal={IEEE Transactions on Computational Intelligence and AI in Games},   title={The 2014 General Video Game Playing Competition},   year={2016},  volume={8},  number={3},  pages={229-243},}

@article{Schaul2013AVG,
  title={A video game description language for model-based or interactive learning},
  author={Tom Schaul},
  journal={2013 IEEE Conference on Computational Inteligence in Games (CIG)},
  year={2013},
  pages={1-8}
}

@inproceedings{muise-icaps16demo-pd,
  title = {{Planning.Domains}},
  author = {Christian Muise},
  booktitle = {The 26th International Conference on Automated Planning and Scheduling - Demonstrations},
  year = {2016},
  url = {http://www.haz.ca/papers/planning-domains-icaps16.pdf},
  abstract = {Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.}
}

@misc{Vladislav2020,
  author = {Vladislav Nikolov Vasilev},
  title = {gvgai-pddl},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Vol0kin/gvgai-pddl}}
}