Towards story-based content generation: From plot-points to maps

Valls-Vargas, Josep; Ontañón, Santiago; Zhu, Jichen

doi:10.1109/cig.2013.6633654

Cited by 13 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Computational narrative → Procedural content generation: By breaking the game narrative into subareas of game content we can find core game content elements such as the game's plotline [7], [108], but also the ways a story is represented in the game environment [109]. The coupling of a game's representation and the story of the game is of vital importance for player experience.…”

Section: F Computational Narrativementioning

confidence: 99%

A Panorama of Artificial and Computational Intelligence in Games

Yannakakis

Togelius

2015

IEEE Trans. Comput. Intell. AI Games

134

View full text Add to dashboard Cite

Abstract-This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: (1) the dominant AI method(s) used under each area; (2) the relation of each area with respect to the end (human) user; and (3) the placement of each area within a human-computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.

show abstract

Section: F Computational Narrativementioning

confidence: 99%

A Panorama of Artificial and Computational Intelligence in Games

Yannakakis

Togelius

2015

IEEE Trans. Comput. Intell. AI Games

134

View full text Add to dashboard Cite

show abstract

“…The other attempt in this type of PGC was by Valls-Vargas and peers [41]. Their work focused on generating a map that could support multiple quests/stories that unfolded non-linearly.…”

Section: Static Quest In Generated Game Worldmentioning

confidence: 99%

Multi-Paths Quest Generation For Structural Analysis

Chongmesuk

View full text Add to dashboard Cite

Existing quest generation systems that used quest structures had an important limitation. They were not compatible with dynamic environment games, where player actions may have unforeseen repercussion from chain-reaction, such as locking the quest or preventing it from being completed. In addition, quests generated by such systems were not guaranteed to have multiple completion paths. In this thesis, action-based-objective quests were replaced with game state-based-objective quests, while retaining the previous grammar rule. Each quest's contents and objectives were filled using Token system and Full Condition State system to ensure the connectivity within the quest's contents and to prevent players from locking the quest. The Quest Query system simulated a dynamic environment game. It analysed every combination of player's possible action to discover how many paths the player could complete the quest. The state-based objectives were compatible with dynamic environment simulation. The query system discovered paths that used multiple steps of planning, including using the dynamic environment to complete the quest. The proposed systems functioned as intended and could be used as a framework for further studies. However, the simulation only used a small number of objects because of performance issue. Further study is advised to focus on improving the performance of the systems and applying the systems and its simulator to commercial products.

show abstract