The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

A Cognitive Approach to Narrative Planning with Believable Characters

In this work, we address the question of generating understandable narratives using a cognitive approach. The requirements of cognitive plausibility are presented. Then an abduction-based cognitive model of the human deliberative reasoning ability is presented. We believe that implementing such a procedure in a narrative context to generate plans would increase the chances that the characters will be perceived as believable. Our suggestion is that the use of a deliberative reasoning procedure can be used as a basis of several strategies to generate interesting stories

Player agency in interactive narrative: audience, actor & author

The question motivating this review paper is, how can computer-based interactive narrative be used as a constructivist learn- ing activity? The paper proposes that player agency can be used to link interactive narrative to learner agency in constructivist theory, and to classify approaches to interactive narrative. The traditional question driving research in interactive narrative is, ‘how can an in- teractive narrative deal with a high degree of player agency, while maintaining a coherent and well-formed narrative?’ This question derives from an Aristotelian approach to interactive narrative that, as the question shows, is inherently antagonistic to player agency. Within this approach, player agency must be restricted and manip- ulated to maintain the narrative. Two alternative approaches based on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are reviewed. If a Boalian approach to interactive narrative is taken the conflict between narrative and player agency dissolves. The question that emerges from this approach is quite different from the traditional question above, and presents a more useful approach to applying in- teractive narrative as a constructivist learning activity

A conceptual framework for interactive virtual storytelling

This paper presents a framework of an interactive storytelling system. It can integrate five components: management centre, evaluation centre, intelligent virtual agent, intelligent virtual environment, and users, making possible interactive solutions where the communication among these components is conducted in a rational and intelligent way. Environment plays an important role in providing heuristic information for agents through communicating with the management centre. The main idea is based on the principle of heuristic guiding of the behaviour of intelligent agents for guaranteeing the unexpectedness and consistent themes

Personality and Emotion for Virtual Characters in Strong-Story Narrative Planning

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. The interaction of rich virtual characters can produce interesting narratives and enhance user experience in virtual environments. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. I demonstrate the value of the strong-story properties of the model by generating story conflicts intelligently. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts using certain behavior types. In doing so, the author can encourage the player to adopt and exhibit those behaviors. I conduct multiple human subject and case studies to evaluate these models and show that they enable generating a larger number of stories and character behavior that is preferred and more believable to a human audience

An Architecture for Believable Socially Aware Agents

The main focus of this thesis is to solve the believability problem in video game agents by integrating necessary psychological and sociological foundations by means of role based architecture. Our design agent also has the capability to reason and predict the decisions of other actors by using its own mental model. The agent has a separate mental model for every actor

Don\u27t Give Me That Story! -- A Human-Centered Framework for Usable Narrative Planning

Interactive or branching stories are engaging and can be embedded into digital systems for a variety of purposes, but their size and complexity makes it difficult and time-consuming for humans to author them. Narrative planning algorithms can automatically generate large branching stories with guaranteed causal consistency, using a hand-authored library of story content pieces. The usability of such a system depends on both the quality of the narrative model upon which it is built and the ability of the user to create the story content library. Current narrative planning algorithms use either a limited or no model of character belief, which typically leads to undesireable stories and difficult domain authoring challenges. Domain authoring is further complicated by a lack of intelligent tools for summarizing the content that a domain can produce so that its author can effectively evaluate it. In this work I extend a prior narrative planning framework to model deeply nested character beliefs, thus avoiding common character omniscience problems without overburdening the domain author. Human subjects evaluations demonstrate that the belief model tracks nested beliefs correctly, and that it improves overall character believability in solution spaces over previous models. This model makes domain authoring more intuitive, but also adds complexity to the story generation algorithm, making the planner\u27s output even harder for the author to predict. As a step toward more intelligent domain authoring tools, I present a novel method for measuring story similarity by encoding important story information into a fixed-length numeric vector. This enables automatic clustering of stories based on their semantic similarity, facilitating high-level communication of large story spaces. I compare the story similarity metric to assessments made by humans, and find the metric to be highly accurate in judging how similar two stories are to each other. I then demonstrate its use in clustering solution spaces, and evaluate two strategies for summarizing the content of the resulting clusters. I find both techniques to be more effective than a control in communicating large story spaces to humans. These contributions together advance the usability of narrative planning algorithms by improving their underlying narrative model and providing a basis for more intelligent domain authoring tools