General video game playing

General video game playing (GVGP)[1] is the design of artificial intelligence programs to be able to play more than one video game successfully. In recent years, some progress have been made in this area, including programs that can learn to play Atari 2600 games[2][3][4][5] as well as a program that can learn to play NES games.[6][7][8]

GVGP could potentially be used to create real video game AI automatically, as well as "to test game environments, including those created automatically using procedural content generation and to find potential loopholes in the gameplay that a human player could exploit".[1] GVGP has also been used to generate game rules, and estimate a game's quality based on Relative Algorithm Performance Profiles (RAPP), which compare the skill differentiation that a game allows between good AI and bad AI.[9]

Since 2014, the General Video Game Playing Competition (GVGAI) has offered a way for researchers and practitioners to test and compare their best general video game playing algorithms. The competition has an associated software framework including a large number of games written in the Video Game Description Language (VGDL). VGDL can be used to describe a game specifically for procedural generation of levels, using Answer Set Programming (ASP) and an Evolutionary Algorithm (EA). GVGP can then be used to test the validity of procedural levels, as well as the difficulty or quality of levels based on how an agent performed.[10]

The games used in GVGP are, for now, often 2 dimensional arcade games, as they are the simplest and easiest to quantify.[11] To simplify the process of creating an AI that can interpret video games, games for this purpose are written in Video Game Description Language (VGDL), which is a coding language using simple semantics and commands that can easily be parsed.

See also

References

  1. 1 2 Levine, John; Congdon, Clare Bates; Ebner, Marc; Kendall, Graham; Lucas, Simon M.; Miikkulainen, Risto; Schaul, Tom; Thompson, Tommy (2013). "General Video Game Playing". Artificial and Computational Intelligence in Games (Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik) 6: 77–83. Retrieved 25 April 2015.
  2. ↑ Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Graves, Alex; Antonoglou, Ioannis; Wierstra, Daan; Riedmiller, Martin (2013). "Playing Atari with Deep Reinforcement Learning" (PDF). Neural Information Processing Systems Workshop 2013. Retrieved 25 April 2015.
  3. ↑ Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis (26 February 2015). "Human-level control through deep reinforcement learning". Nature 518: 529–533. doi:10.1038/nature14236. PMID 25719670.
  4. ↑ Korjus, Kristjan; Kuzovkin, Ilya; Tampuu, Ardi; Pungas, Taivo (2014). "Replicating the Paper "Playing Atari with Deep Reinforcement Learning"" (PDF). University of Tartu. Retrieved 25 April 2015.
  5. ↑ Guo, Xiaoxiao; Singh, Satinder; Lee, Honglak; Lewis, Richard L.; Wang, Xiaoshi (2014). "Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning" (PDF). NIPS Proceedingsβ. Conference on Neural Information Processing Systems. Retrieved 25 April 2015.
  6. ↑ Murphy, Tom (2013). "The First Level of Super Mario Bros. is Easy with Lexicographic Orderings and Time Travel ... after that it gets a little tricky." (PDF). SIGBOVIK. Retrieved 25 April 2015.
  7. ↑ Murphy, Tom. "learnfun & playfun: A general technique for automating NES games". Retrieved 25 April 2015.
  8. ↑ Teller, Swizec (October 28, 2013). "Week 2: Level 1 of Super Mario Bros. is easy with lexicographic orderings and". A geek with a hat. Retrieved 25 April 2015.
  9. ↑ Nielsen, Thorbjørn S.; Barros, Gabriella A. B.; Togelius, Julian; Nelson, Mark J. "Towards generating arcade game rules with VGDL" (PDF).
  10. ↑ Neufeld, Xenija; Mostaghim, Sanaz; Perez-Liebana, Diego. "Procedural Level Generation with Answer Set Programming for General Video Game Playing" (PDF).
  11. ↑ Levine, John; Congdon, Clare Bates; Ebner, Marc; Kendall, Graham; Lucas, Simon M.; Miikkulainen Risto, Schaul; Tom, Thompson; Tommy. "General Video Game Playing" (PDF).

External links


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