Extracting event knowledge from open-world survival video games is a promising domain to investigate the application of Machine Learning techniques to routine human decision making. This contrasts with and builds upon typical game-based decision making work that focuses on optimal behavior. We propose an Interaction Graph data structure that can be trained from game play to enable hybrid reasoning and statistical estimation about what events can happen in the world. This enables an agent to exhibit increasingly more reasonable behavior after low numbers of training runs. An implementation and initial experimental validation are presented.

The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

In this paper we present a corpus of quest stories taken from a popular commercial Massively Multiplayer Online Role-Playing Game (MMORPG). These stories are open-ended narrative, but anchored to formal, in-game actions and entities, providing valuable constraint as a narrative corpus. We present two preliminary experiments establishing baselines for evaluating similar patterns and content across the corpus.

In this paper we explore the use of recursive cubic Hermite splines to mimic human movement in open world games. Human-like movement in an open world environment has many characteristics that are not optimal or directed towards clear, discrete goals. Using data collected from a simple MMORPG-like game, we use our spline representation to model human player movements relative to corresponding optimal paths. Using this representation, we show that simple distributions can be used to estimate control parameters to generate human-like movement across a population of agents in a novel environment.

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs.  We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model .  Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.

In this paper, we present adaptive quests, an extension to the dominant quest model that guides and motivates gameplay in MMORPG shared worlds. The standard model has proven effective, but is significantly incompatible with the desire for player driven change in the world. We present an incremental step to increasing player impact, discuss the problems it creates with the quest model, and show how adaptive quests can help reconcile the two. We present simulation experiments supporting not only that adaptive quests help mitigate those problems, but that they can actually improve them over the standard model.

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one-day workshops were held on October 11: Artificial Intelligence in the Game Design Process, and Intelligent Narrative Technologies. The highlights of each workshop are presented in this report.

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one-day workshops were held on October 11: Artificial Intelligence in the Game Design Process, and Intelligent Narrative Technologies. The highlights of each workshop are presented in this report.

Empirical research supporting computational models of narrative is often constrained by the lack of large-scale corpora with deep annotation. In this paper, we report on our annotation and analysis of a dataset of 283 individual narrations of the events in two short video clips. The utterances in the narrative transcripts were annotated to align with known events in the source videos, offering a unique opportunity to study the regularities and variations in the way that different people describe the exact same set of events. We identified the causal relationships between events in the two video clips, and investigated the role that causality plays in determining whether subjects will mention a particular story event and the likelihood that these events will be told in the order that they occurred in the original videos.

In this paper, we discuss approaches to dialogue in interactive video games and interactive narrative research. We propose that situating interactive dialogue in the simplified expectations of video games is a profitable way to investigate computational dialogue simulation. Taking cues from existing physical simulations such as combat, we propose a hypothetical game environment and design goals for an embedded interactive dialogue system. We present a modular framework targeted at that environment, which is designed to enable incremental development and exploration of dialogue concepts. We describe this framework together with a work-in-progress system for simulating simple in-game negotiation dialogues.