Hiding and seeking is a cognitive ability frequently demonstrated by humans in both real life and video games. We use machine learning to automatically construct the first computational model of hide/seek behavior in adult humans in a video game like setting. The model is then run generatively in a novel environment and its behavior is found indistinguishable from actual human behavior by a panel of human judges.  In doing so the artificial intelligence agent using the model appears to have passed a version of the Turing test for hiding and seeking.

In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro action sequences for individual game units, by adjusting the concept to the real-time requirements of StarCraft, and by using macros involving specific abilities of game units, our testing system was able to find either weaknesses or system crashes for all of the competition entries of the chosen scenarios. Additionally, by requiring a minimal margin with respect to surviving units, we were able to clearly identify the weaknesses of the tested AIs.

Massive Multiplayer Online Strategy games present several unique challenges to players and designers. There is the need to constantly adapt to changes in the game itself and the need to achieve a certain level of simulation and realism, which typically implies battles involving combat with several distinct armies, combat phases and diferent terrains; resource management which involves buying and selling goods and combining lots of diferent kinds of resources to fund the player's nation and cutthroat diplomacy which dictates the pace of the game. However, these constant changes and simulation mechanisms make a game harder to play, increasing the amount of effort required to play it properly. As some of these games take months to be played, players who become inactive have a negative impact on the game. This work pretends to demonstrate how to create versatile agents for playing Massive Multiplayer Online Turn Based Strategy Games, while keeping close attention to their playing performance. In a test to measure this performance the results showed similar survival performance between humans and AIs.

This paper describes the creation of a digital improvisational theatre game, called Party Quirks, that allows a human user to improvise a scene with synthetic actors according to the rules of the real-world Party Quirks improv game. The AI actor behaviors are based on our study of communication strategies between real-life actors on stage and the fuzzy concepts that they employ to define and portray characters. This paper describes the underlying fuzzy concepts used to enable reasoning in ambiguous environments, like improv theatre. It also details the development of content for the system, which involved the creation of a system for animation authoring, design for efficient data reuse, and a work flow centered on Google Docs enabling parallel data entry and rapid iteration.

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.

We address the challenges of evaluating the fidelity of AI agents that are attempting to produce human-like behaviors in games. To create a believable and engaging game play experience, designers must ensure that their non-player characters (NPCs) behave in a human-like manner. Today, with the wide popularity of massively-multi-player online games, this goal may seem less important. However, if we can reliably produce human-like NPCs, this can open up an entirely new genre of game play. In this paper, we focus on emulating human behaviors in strategic game settings, and focus on a Social Ultimatum Game as the testbed for developing and evaluating a set of metrics for comparing various autonomous agents to human behavior collected from live experiments.

Current behavior authoring tools force game designers to split their attention between the game context and the tool context. We have addressed this problem by developing a behavior authoring tool that merges these two contexts. This paper outlines the design and implementation of a gameindependent behavior tree authoring tool, called AIPaint, that allows a designer to create and edit behavior trees via a natural sketching interface overlaid on the game world. We demonstrate the use of AIPaint to author computercontrolled characters in two simple games and report on an observational evaluation.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum . We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

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.

We present ongoing work on a system that accommodates player agency in a digital narrative with an external plot. We focus on key events that should occur in that storyline for dramatic effect, but do not explicitly specify the characters that should fill the roles needed for those events. Instead, we define them abstractly, with characteristics that the selected characters should have (including previous events they should have completed for eligibility), and rely on a Director construct to populate those roles from agents in the selection pool that fit those criteria. Agents begin as largely homogeneous, primordial entities that accumulate data and narrative value from the events in which they participate. This creates an environment that differentiates characters by the actions they perform, conferring worth onto characters that become important to the player based on their direct involvement in the plot. The focus, then, is on defining a priori the what of the narrative, while leaving it to the Director construct to decide at runtime exactly who among a distributed pool of agents carries it out.