We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to improve the quality of the generated stories. This analysis addresses the description of the story characters, the use of narrative knowledge including about character believability, and the possible lack of more comprehensive or more detailed knowledge or creativity models. Finally, the article presents concluding remarks in the form of suggestions of research topics that might have a significant impact on the advancement of the state of the art on autonomous non-interactive story generation systems. The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.

AI can already create photorealistic faces, objects, and landscapes. We can already recreate any voice. GPT-3 can already write dialogue and movie plots almost indistinguishable from ones written by humans. Even generated music is making fast progress. It's startling to realize that Hollywood movies that cost $300M to produce today might be generated for a few cents within our lifetimes.

Characters are vital to large video game worlds as they bring a sense of life to the world. However, background characters are known to rarely exhibit any sign of motivated behavior or emotional state. We want to change this by assigning these characters emotions that can be identified through their non-verbal behavior. We feel the addition of emotion will allow players to feel more connected to the game world and make the game world more believable. This paper presents the results of an experiment to test two ways of conveying emotion: 1) through a character's gait and 2) through a character's interactions with the game world. Results from the experiment suggest that a combination of gait and interactions is the most effective method to convey emotion.

Non-player character diversity enriches game environments increasing their replay value. We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.

I describe work in progress on a system for interactiveprototyping of AI-based characters. A sort of "Sims construction set," the system combines a simple physics simulation with a set of domain-specific languages to allow programmers to quickly build and test character AI. It allows iterative, incremental development in which behaviors can be compactly authored, tested, monitored, and hot-swapped for new behaviors, using in-game editing and debugging facilities.

This paper describes a system that greatly simplifies the task of authoring new behaviors for virtual characters, including physical interactions between characters and other characters or objects. The system in implemented within Twig and allows users to interactively generate and test procedural controllers for characters, as well as triggering mechanisms and arbitration mechanisms for behaviors. It allows users to quickly add new behaviors, or reparameterize existing behaviors, without access to a motion capture studio or professional animators, making it a natural choice for AI researchers, particularly those operating within a university environment. Moreover, it allows a level of continuous parameterization that would be difficult to achieve with traditional animation techniques based on state machines and blending.