References

  1. Selen Basol, Ozan Erdem, Michael Fink & Giovambattista Ianni (2010): HEX Programs with Action Atoms, pp. 24–33, doi:10.4230/LIPIcs.ICLP.2010.24.
  2. Nick Bassiliades, Antonis Bikakis, Stefania Costantini, Enrico Franconi, Adrian Giurca, Roman Kontchakov, Theodore Patkos, Fariba Sadri & William Van Woensel (2017): Proceedings of the Doctoral Consortium @ RuleML+RR 2017. CEUR Workshop Proceedings 1875. CEUR-WS.org.
  3. Harald Beck, Minh Dao-Tran & Thomas Eiter (2018): LARS: A Logic-based framework for Analytic Reasoning over Streams. Artificial Intelligence 261, pp. 16–70, doi:10.1016/j.artint.2018.04.003.
  4. Harald Beck, Thomas Eiter & Christian Folie (2017): Ticker: A system for incremental ASP-based stream reasoning. TPLP 17(5-6), pp. 744–763, doi:10.1017/S1471068417000370.
  5. Rafael H. Bordini, Jomi Fred Hübner & Michael Wooldridge (2007): Programming Multi-Agent Systems in AgentSpeak Using Jason (Wiley Series in Agent Technology), doi:10.1002/9780470061848.
  6. Gerhard Brewka, Stefan Ellmauthaler, Ricardo Gonçalves, Matthias Knorr, João Leite & Jörg Pührer (2018): Reactive multi-context systems: Heterogeneous reasoning in dynamic environments. Artif. Intell. 256, pp. 68–104, doi:10.1016/j.artint.2017.11.007.
  7. Francesco Calimeri, Michael Fink, Stefano Germano, Giovambattista Ianni, Christoph Redl & Anton Wimmer (2013): AngryHEX: an Artificial Player for Angry Birds Based on Declarative Knowledge Bases. In: Proceedings of the Workshop Popularize Artificial Intelligence co-located with the 13th Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), Turin, Italy, December 5, 2013., pp. 29–35. Available at http://ceur-ws.org/Vol-1107/paper10.pdf.
  8. Francesco Calimeri, Stefano Germano, Giovambattista Ianni, Francesco Pacenza, Armando Pezzimenti & Andrea Tucci (2018): Answer Set Programming for Declarative Content Specification: A Scalable Partitioning-Based Approach. In: AI*IA 2018 - Advances in Artificial Intelligence - XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20-23, 2018, Proceedings, pp. 225–237, doi:10.1007/978-3-030-03840-3_17.
  9. Francesco Calimeri, G Ianni, Francesco Pacenza, Simona Perri & Jessica Zangari: Incremental answer set programming with overgrounding.
  10. Stefania Costantini & Arianna Tocchio (2004): The DALI Logic Programming Agent-Oriented Language. In: Logics in Artificial Intelligence, 9th European Conference, JELIA 2004, Lisbon, Portugal, September 27-30, 2004, Proceedings, pp. 685–688, doi:10.1007/978-3-540-30227-8_57.
  11. Akshat Dhaon & Rem W. Collier (2014): Multiple Inheritance in AgentSpeak(L)-Style Programming Languages. In: Proceedings of the 4th International Workshop on Programming Based on Actors Agents & Decentralized Control, AGERE! '14. ACM, New York, NY, USA, pp. 109–120, doi:10.1145/2687357.2687362.
  12. Conrad Drescher, Stephan Schiffel & Michael Thielscher (2009): A Declarative Agent Programming Language Based on Action Theories. In: Frontiers of Combining Systems, 7th International Symposium, FroCoS 2009, Trento, Italy, September 16-18, 2009. Proceedings, pp. 230–245, doi:10.1007/978-3-642-04222-5_14.
  13. Thomas Eiter, V.S. Subrahmanian & T.J. Rogers (2000): Heterogeneous active agents, III: Polynomially implementable agents. Artificial Intelligence 117(1), pp. 107 – 167, doi:10.1016/S0004-3702(99)00104-6. Available at http://www.sciencedirect.com/science/article/pii/S0004370299001046.
  14. Michael Fink, Stefano Germano, Giovambattista Ianni, Christoph Redl & Peter Schüller (2013): ActHEX: Implementing HEX Programs with Action Atoms, pp. 317–322, doi:10.1007/978-3-642-40564-8_31.
  15. Martin Gebser, Torsten Grote, Roland Kaminski & Torsten Schaub (2011): Reactive Answer Set Programming. In: Proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR'11. Springer-Verlag, Berlin, Heidelberg, pp. 54–66, doi:10.1016/S0004-3702(02)00187-X. Available at http://dl.acm.org/citation.cfm?id=2010192.2010201.
  16. Martin Gebser, Roland Kaminski, Benjamin Kaufmann & Torsten Schaub (2019): Multi-shot ASP solving with clingo. TPLP 19(1), pp. 27–82, doi:10.1017/S1471068418000054.
  17. Jason S Hartford, James R Wright & Kevin Leyton-Brown (2016): Deep Learning for Predicting Human Strategic Behavior. In: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon & R. Garnett: Advances in Neural Information Processing Systems 29. Curran Associates, Inc., pp. 2424–2432. Available at http://papers.nips.cc/paper/6509-deep-learning-for-predicting-human-strategic-behavior.pdf.
  18. Giovambattista Ianni, Francesco Pacenza & Jessica Zangari (2019): An Infrastructure for Multi-Shot Reasoning with Incremental Grounding. In: Proceedings of the 34th Italian Conference on Computational Logic, CILC2019.
  19. Niels Justesen, Philip Bontrager, Julian Togelius & Sebastian Risi (2017): Deep Learning for Video Game Playing. CoRR abs/1708.07902. Available at http://arxiv.org/abs/1708.07902.
  20. Benjamin Kaufmann, Nicola Leone, Simona Perri & Torsten Schaub (2016): Grounding and Solving in Answer Set Programming. AI Magazine 37(3), pp. 25–32, doi:10.1609/aimag.v37i3.2672. Available at http://www.aaai.org/ojs/index.php/aimagazine/article/view/2672.
  21. Sven Koenig & Maxim Likhachev (2002): Improved Fast Replanning for Robot Navigation in Unknown Terrain. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, ICRA 2002, May 11-15, 2002, Washington, DC, USA, pp. 968–975, doi:10.1109/ROBOT.2002.1013481.
  22. Marco Maratea, Luca Pulina & Francesco Ricca (2013): Automated Selection of Grounding Algorithm in Answer Set Programming. In: Proceeding of the XIIIth International Conference on AI*IA 2013: Advances in Artificial Intelligence - Volume 8249. Springer-Verlag New York, Inc., New York, NY, USA, pp. 73–84, doi:10.1007/978-3-319-03524-6_7.
  23. Mark J. Nelson & Adam M. Smith (2016): ASP with Applications to Mazes and Levels, pp. 143–157. Springer International Publishing, Cham, doi:10.1007/978-3-319-42716-4_8.
  24. Diego Pérez-Liébana, Jialin Liu, Ahmed Khalifa, Raluca D. Gaina, Julian Togelius & Simon M. Lucas (2018): General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms. CoRR abs/1802.10363. Available at http://arxiv.org/abs/1802.10363.
  25. Noor Shaker, Antonios Liapis, Julian Togelius, Ricardo Lopes & Rafael Bidarra (2016): Constructive generation methods for dungeons and levels, pp. 31–55. Springer International Publishing, Cham, doi:10.1007/978-3-319-42716-4_3.
  26. A. M. Smith & M. Mateas (2011): Answer Set Programming for Procedural Content Generation: A Design Space Approach. IEEE Transactions on Computational Intelligence and AI in Games 3(3), pp. 187–200, doi:10.1109/TCIAIG.2011.2158545.
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