Writing a good story requires immense patience, creativity and work from the author, and the practice of writing a story requires a good grasp of the readers' psychology to create suspense and thrills and to merge the readers' world with that of the story. In the digital writing space, authors can still adhere to these rules of thumb while being aware of the disappearance of certain constraints due to the added possibility of narrating in a nonlinear fashion. There are many overlapping approaches to interactive storytelling or authoring, but each of the approaches has its own strengths and weaknesses. The motivation for this research arises from the perceived need for a new hybrid approach that coalesces and extends existing approaches. Since each of the approaches empowers certain aspects of the storytelling and narration process, the result forces a new research direction which eliminates certain weaknesses exhibited by a single approach, due to the synergistic nature of the various approaches. We have developed: 1) a Hybrid Evolutionary-Fuzzy Time-based Interactive (HEFTI) storytellling engine that generates dynamic stories from a set of authored story constructs given by human authors; 2) a set of authoring tools that allow authors to generate the needed story constructs; and, 3) a storytelling environment for them to orchestrate a digital stage play with computer agents and scripts. We have conducted a usability study and system evaluation to evaluate the performance of the engine. Our experiments and usability study have shown that the authoring environment abstracted the complexity of authoring an interactive, dynamic story from the authors with the use of windows-based interfaces to help them visualize various aspects of a story. This reduces the amount of learning and knowledge required to start having the pleasure of authoring dynamic stories. The studies also revealed certain features and tools that may be reflected by authoring tools in the future to automate various aspects of the authoring process so that the authors may spend more time thinking rather than writing (or programming) their stories.

Narrative Intelligence is the ability to craft, tell, understand, and respond appropriately to narratives. It has been proposed as a vital component of machines aiming to understand human activities or to communicate effectively with humans. However, most existing systems purported to demonstrate Narrative Intelligence rely on manually authored knowledge structures that require extensive expert labor. These systems are constrained to operate in a few domains where knowledge has been provided. This dissertation investigates the learning of knowledge structures to support Narrative Intelligence in any domain. I propose and build a system that, from an corpus of simple exemplar stories, learns complex knowledge structures that subsequently enable the creation, telling, and understanding of narratives. The knowledge representation balances the complexity of learning and the richness of narrative applications, so that we can (1) learn the knowledge robustly in the presence of noise, (2) generate a large variety of highly coherent stories, (3) tell them in recognizably different narration styles and (4) understand stories efficiently. The accuracy and effectiveness of the system have been verified by a series of user studies and computational experiments. As a result, the system is able to demonstrate Narrative Intelligence in any domain where we can collect a small number of exemplar stories. This dissertation is the first step toward scaling computational narrative intelligence to meet the challenges of the real world.

Animated agent technology has been rapidly developed to provide ubiquitously psychological and functional benefits for fulfilling communicative goals. However, the character motions of most character-centered models based on pre-stored movement, finite state machine and scripted conditional logic are generally restrictive. The major drawback lies in the lack of maturity of integrating the elements between personality, emotion and behaviour. To bridge the gap between cognitive and behavioural elements, we examine the connections between human personality, emotion, movement and cartoon modeling for the agent design. Human personality and emotional behaviour are the essences in the recognition of a believable synthetic character. Personality and emotion come from the storylines and result in characters’ motions. Cartoon animations successfully engage the audience and create emotional connections with the spectators. However, even a sophisticated animator often faces some difficulties while performing a very laborious task to simulate an emotion- and personality-rich character. This thesis focuses on exploring effective techniques to extract personality and emotion features for a high-level control of character movements. A hierarchical fuzzy rule-based system was constructed, in which personality and emotion were mapped into the body’s movement zones of a character. This facilitates agent designers to control the personality and emotion of a dynamic synthetic character. The system was then applied to a Narrative Intelligent system and extended to an Ambient Intelligent environment. An innovative storyboard-structured storytelling method was devised by using story scripts and action descriptions in a form similar to the content description of storyboards to predict specific personality and emotion. As software or device agents evolve into the Ambient Intelligence, new concepts for effective agent presentations and delegating control are necessary to minimise the human’s tasks and interventions in the complex and dynamic environment. A novel customizable personalised agent framework was developed by utilising the spirit of cartoon animation to match each user’s profile in the form of a cartoon reciprocal agent. As a result, users could explicitly modify personality and emotion values to change the psychology traits of the agent, which would affect their appearance and behaviour through body posture expression. An evaluation of the system was conducted to verify the effectiveness and the applicability in both Narrative and Ambient intelligent agent frameworks. The significance of this research is that applying higher cognitive factors to animated characters can lead to a better animation design tool and reduce strenuous animation production efforts in agent designs. It will also enable animated characters to embody more adaptive, flexible and stylised performance.