Procedural Play Generation According to Play Arcs Using Monte-Carlo Tree Search

نویسندگان

  • Suguru Ito
  • Makoto Ishihara
  • Marco Tamassia
  • Tomohiro Harada
  • Ruck Thawonmas
  • Fabio Zambetta
چکیده

More than a million spectators watch game streaming platforms such as Twitch every month. This phenomenon suggests video games are a powerful entertainment media not just for players but for spectators as well. Since each spectator has personal preferences, customized spectator-specific game plays are arguably a promising option to increase the entertainment value of video games streaming. In this paper, we propose an Artificial Intelligence (AI) that automatically generates game plays according to play arcs using Monte Carlo Tree Search (MCTS). In particular, we concentrate on fighting games and drive MCTS to achieve specific hitpoints differences between characters at different moments of the game. Our preliminary results show that the proposed AI can generate game plays following the desired transition of game progress.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo Tree Search (MCTS) where the node selection criteria are developed using evolutionary comp...

متن کامل

Generating Sokoban Puzzle Game Levels with Monte Carlo Tree Search

In this work, we develop a Monte Carlo Tree Search based approach to procedurally generate Sokoban puzzles. To this end, we propose heuristic metrics based on surrounding box path congestion and level tile layout to guide the search towards interesting puzzles. Our method generates puzzles through simulated game play, guaranteeing solvability in all generated puzzles. Our algorithm is efficient...

متن کامل

Technical Reports Cover

In this work, we develop a Monte Carlo Tree Search based approach to procedurally generate Sokoban puzzles. To this end, we propose heuristic metrics based on surrounding box path congestion and level tile layout to guide the search towards interesting puzzles. Our method generates puzzles through simulated game play, guaranteeing solvability in all generated puzzles. Our algorithm is efficient...

متن کامل

Monte-Carlo Tree Search for Persona Based Player Modeling

Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, re...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017