PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

Introducing LogDL - Log Description Language for Insights from Complex Data

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
We propose a new logic-based language called LogDL (Log Description Language) that is designed to be a medium for the knowledge discovery workflows conducted over multimodal process-related and spatio-temporal data sets. It makes it possible to operate with the original data along with machine-learning-driven insights expressed as facts, rules and formulas, regarded as higher-level descriptive logs reflecting knowledge about the observed processes in real or virtual environments. LogDL is inspired by the research at the border of AI and games, precisely by GDL (Game Description Language) that was developed for General Game Playing. We compare LogDL to GDL, emphasizing that formal frameworks for analyzing gameplay data sets are a good prerequisite for the case of real,``not digital'' processes. As LogDL is a logic-based language, we present its syntax and semantics. We also discuss how to design its high-performance interpreter that is a must for commercial scenarios.
Rocznik
Tom
Strony
145--154
Opis fizyczny
Bibliogr. 42 poz., il., tab.
Twórcy
  • QED Software, Warsaw, Poland
  • Institute of Informatics, University of Warsaw, Poland
Bibliografia
  • 1. M. R. Genesereth, N. Love, and B. Pell, “General Game Playing: Overview of the AAAI Competition,” AI Magazine, vol. 26, no. 2, pp. 62–72, 2005.
  • 2. S. Greco and C. Molinaro, “Datalog and Logic Databases,” Synthesis Lectures on Data Management, vol. 7, no. 2, pp. 1–169, 2015.
  • 3. V. S. Costa, R. Rocha, and L. Damas, “The YAP Prolog System,” Theory and Practice of Logic Programming, vol. 12, pp. 5–34, 2012.
  • 4. M. Arntzenius and N. R. Krishnaswami, “Datafun: A Functional Datalog,” in Proc. of ICFP 2016, pp. 214–227.
  • 5. P. Alvaro, W. R. Marczak, N. Conway, J. M. Hellerstein, D. Maier, and R. Sears, “Dedalus: Datalog in Time and Space,” in Proc. of Datalog 2010, pp. 262–281.
  • 6. E. Piette, M. Stephenson, D. J. Soemers, and C. Browne, “An Empirical Evaluation of Two General Game Systems: Ludii and RBG,” in Proc. of CoG 2019, 2019, pp. 1–4.
  • 7. M. Thielscher, “Answer Set Programming for Single-Player Games in General Game Playing,” in Proc. of ICLP 2009, pp. 327–341.
  • 8. J. Kowalski and M. Szykuła, “Game Description Language Compiler Construction,” in Proc. of Australasian AI 2013, pp. 234–245.
  • 9. Y. Björnsson and S. Schiffel, “Comparison of GDL Reasoners,” in Proc. of GIGA@IJCAI 2013, pp. 55–62.
  • 10. M. Okumura and S. Fujimura, “Constructing a Log Collecting System using Splunk and its Application for Service Support,” in Proc. of SIGUCCS 2016, pp. 103–106.
  • 11. O. Etzion and P. Niblett, Event Processing in Action. Manning Publications, 2010.
  • 12. J. Małuszyński and A. Szałas, “Logical Foundations and Complexity of 4QL, a Query Language with Unrestricted Negation,” Journal of Applied Non-Classical Logics, vol. 21, no. 2, pp. 211–232, 2011.
  • 13. B. Dunin-K ̨eplicz and A. Strachocka, “Paraconsistent Multi-party Persuasion in TalkLOG,” in Proc. of PRIMA 2015, pp. 265–283.
  • 14. M. Ebner, J. Levine, S. M. Lucas, T. Schaul, T. Thompson, and J. Togelius, “Towards a Video Game Description Language,” in Artificial and Computational Intelligence in Games. Dagstuhl, 2013, pp. 85–100.
  • 15. I. Haghighi, A. Jones, Z. Kong, E. Bartocci, R. Grosu, and C. Belta, “SpaTeL: A Novel Spatial-Temporal Logic and Its Applications to Networked Systems,” in Proc. of HSCC 2015, pp. 189–198.
  • 16. D. Pedreschi, F. Giannotti, R. Guidotti, A. Monreale, S. Ruggieri, and F. Turini, “Meaningful Explanations of Black Box AI Decision Systems,” in Proc. of AAAI 2019, pp. 9780–9784.
  • 17. M. H. Segler and M. P. Waller, “Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction,” Chemistry – A European Journal, vol. 23, no. 25, pp. 5966–5971, 2017.
  • 18. M. Świechowski and J. Mańdziuk, “Fast Interpreter for Logical Reasoning in General Game Playing,” Journal of Logic and Computation, vol. 26, no. 5, pp. 1697–1727, 2016.
  • 19. C. F. Sironi and M. H. M. Winands, “Optimizing Propositional Networks,” in Proc. of CGW@IJCAI 2016, pp. 133–151.
  • 20. J. C. Tay and N. B. Ho, “Evolving Dispatching Rules using Genetic Programming for Solving Multi-Objective Flexible Job-Shop Problems,” Computers & Industrial Engineering, vol. 54, no. 3, pp. 453–473, 2008.
  • 21. D. J. Lizotte and E. B. Laber, “Multi-Objective Markov Decision Processes for Data-Driven Decision Support,” Journal of Machine Learning Research, vol. 17, pp. 211:1–211:28, 2016.
  • 22. L. A. Zadeh, Computing with Words – Principal Concepts and Ideas. Springer, 2012.
  • 23. D. Mark, Behavioral Mathematics for Game AI. Cengage Learning, 2009.
  • 24. M. Świechowski and D. Ślęzak, “Grail: A Framework for Adaptive and Believable AI in Video Games,” in Proc. of WI 2018, pp. 762–765.
  • 25. M. Świechowski, T. Tajmajer, and A. Janusz, “Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms,” in Proc. of CIG 2018, pp. 445–452.
  • 26. D. Irish, The Game Producer’s Handbook. Cengage Learning, 2005.
  • 27. A. Janusz, D. Śl ̨ezak, S. Stawicki, and K. Stencel, “SENSEI: An Intelligent Advisory System for the eSport Community and Casual Players,” in Proc. of WI 2018, pp. 754–757.
  • 28. M. Świechowski, “Game AI Competitions: Motivation for the Imitation Game-Playing Competition,” in Proc. of FedCSIS 2020, pp. 155–160.
  • 29. B. Dunin-Kęplicz and R. Verbrugge, Teamwork in Multi-Agent Systems – A Formal Approach. Wiley, 2010.
  • 30. G. N. Yannakakis and J. Togelius, “A Panorama of Artificial and Computational Intelligence in Games,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 7, no. 4, pp. 317–335, 2014.
  • 31. J. Ruan, W. Van Der Hoek, and M. Wooldridge, “Verification of Games in the Game Description Language,” Journal of Logic and Computation, vol. 19, no. 6, pp. 1127–1156, 2009.
  • 32. J. Togelius, “AI Researchers, Video Games Are Your Friends!” in Proc. of IJCCI 2015, pp. 3–18.
  • 33. W. Van Der Aalst, “Process Mining,” Communications of the ACM, vol. 55, no. 8, pp. 76–83, 2012.
  • 34. T. Kawamura, T. Kimura, and S. Tsumoto, “Estimation of Service Quality of a Hospital Information System Using a Service Log,” The Review of Socionetwork Strategies, vol. 8, no. 2, pp. 53–68, 2014.
  • 35. L. Dey, I. Verma, A. Khurdiya, and S. B. H., “A Framework to Integrate Unstructured and Structured Data for Enterprise Analytics,” in Proc. of FUSION 2013, pp. 1988–1995.
  • 36. P. MacAlpine, M. Depinet, and P. Stone, “UT Austin Villa 2014: RoboCup 3D Simulation League Champion via Overlapping Layered Learning,” in Proc. of AAAI 2015, pp. 2842–2848.
  • 37. M. Przyborowski, T. Tajmajer, Ł. Grad, A. Janusz, P. Biczyk, and D. Ślęzak, “Toward Machine Learning on Granulated Data – A Case of Compact Autoencoder-based Representations of Satellite Images,” in Proc. of Big Data 2018, pp. 2657–2662.
  • 38. J. Ludziejewski, Ł. Grad, Ł. Przebinda, and T. Tajmajer, “Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System,” in Proc. of FedCSIS 2020.
  • 39. G. J. Nalepa, E. Brzychczy, and S. Bobek, “On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications,” in Proc. of IDEAL (2) 2018, pp. 75–83.
  • 40. C. Han, J. Mao, C. Gan, J. Tenenbaum, and J. Wu, “Visual Concept-Metaconcept Learning,” in Proc. of NeurIPS 2019, pp. 5002–5013.
  • 41. M. Świechowski and D. Ślęzak, “Granular Games in Real-Time Environment,” in Workshop Proc. of ICDM 2018, pp. 462–469.
  • 42. R. Reiter, Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, 2001.
Uwagi
1. Track 1: Artificial Intelligence
2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9459dc32-2674-4713-a9e3-f101b1aedd65
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.