Advanced Intelligent Paradigms in Computer Games by Norio Baba, Hisashi Handa

By Norio Baba, Hisashi Handa

This ebook provides a pattern of the latest study in regards to the software of computational intelligence concepts and web know-how in desktop video games. The contents include:

  • COMMONS online game in clever environment
  • Adaptive iteration of dilemma-based interactive narratives
  • Computational intelligence in racing games
  • Evolutionary algorithms for board video game gamers with area knowledge
  • The ChessBrain project
  • Electronic industry games
  • EVE’s entropy
  • Capturing participant entertainment in computing device games

This publication is directed to researchers, training engineers/scientists and students.

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These soaps were selected for their accessibility, familiarity and popularity with the general public. For each soap, a series of dilemmas which characters had faced in recent years were identified1 . It was found that these dilemmas fell into only three of the five possible categories, namely Betrayal (1), Sacrifice (2) and Favour (5). Figure 4 gives examples of these dilemmas, one of which is generalised in fig. 5. All domain specific background knowledge was added to the system, including STRIPS-style actions (such as why two characters may fall in love) and locations (for example club and house) which appear in the considered soaps.

6. Syd Field. The Screen-writer’s Workbook. Dell Publishing, New York, 1984. 7. B¨ orje Karlsson, Angelo E. M. Ciarlini, Bruno Feij´ o, and Antonio L. Furtado. Applying a plan-recognition/plan-generation paradigm to interactive storytelling. In Workshop on AI Planning for Computer Games and Synthetic Characters, The Lake District, UK, 2006. 8. Brian Magerko. Story representation and interactive drama. In 1st Artificial Intelligence and Interactive Digital Entertainment Conference, Marina Del Rey, California, 2005.

Kudenko Current state Most appropriate remaining dilemma selected Can’t achieve dilemma Planner ...... (all possible plans) Selected plan presented to user New state: dependent on user choice Fig. 2. This figure gives an overview of the system moving between states dependent on plans, dilemmas and user decisions The system uses a GraphPlan planner [2] which utilises a STRIPS-style representation of actions. The GraphPlan algorithm is sound and complete. On being passed a dilemma, the planner finds all plans to achieve this dilemma given the current storyworld state and background knowledge.

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