Cinematic, Ambient, Inhabitable Narrative Environments (CAINEs) are conceptual AI-driven interactive story systems combining text, audio, and visual imagery that are scalable and adaptable to a wide range of storytelling needs and interactor inputs. Conceived by at artist outside the AI community, they represent an opportunity to use AI in a nontraditional and immersive narrative fashion that relies not on the goal-based arrangement of story elements, but on the accretion and association of those elements in the minds of interactors. This paper represents the initial phase of the project’s development.

Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting Deep Recurrent Q-Network (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.

Multi-robot systems are an exciting application domain for AI research and Dec-POMDPs, specifically. MacDec-POMDP methods can produce high-quality general solutions for realistic heterogeneous multi-robot coordination problems by automatically generating control and communication policies, given a model. In contrast to most existing multi-robot methods that are specialized to a particular problem class, our approach can synthesize policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents.

According to the Journal, Inclusion, in 2012, 9% of the US population, or 28.5M Americans, had a cognitive disability. Worldwide the number is believed to exceed 630 million. This is a very heterogeneous group, with a wide variety of abilities and impairments making it challenging to develop assistive technologies to meet their needsA growing sub-set of this cohort is the aging population, who continue to work but experience mild cognitive changes. Though these these individuals have deep funds of knowledge, and valuable skills they may struggle in the workplace, in part due to lack of access to tools that can address their individual challenges. . The development of new technologies, such as cognitive assistants, has opened the door to more useful solutions. This paper will review the history and challenges of assistive technology for cognition (ATC) and highlight the work in which we are currently engaged.

Videogame avatars provide an expressive avenue for players to represent themselves virtually. Research has shown that these avatars, while virtual, can reveal aspects of players' identities, along with physical, social, and cultural values of the real-world. In this paper, we present an approach for modeling player values through their avatars using artificial intelligence (AI) clustering techniques. In a study with 191 participants who created avatars using our system, we provide a thorough comparison of the techniques across numerical, textual, and visual data. Our findings showed that these data structures can effectively reveal players' values and preferences, such as conforming to stereotypes of character roles using statistical attributes, modeling nuances in text descriptions of avatars, and identifying "best-example" (prototypical) avatar appearances that players can be quantitatively shown to conform to. Our findings suggest that AI clustering approaches can be used to model players to yield insight into implicitly held values in a data-driven manner through virtual avatars.

We propose a declarative framework for representing and reasoning about truthfulness  of agents using answer set programming. We show how statements by agents can be  evaluated against a set of observations over time equipped with our knowledge about  the actions of the agents and the normal behavior of agents. We illustrate the framework  using examples and discuss possible extensions that need to be considered.

We report a study performed with a social robot that autonomously plays a competitive game. By relying on an emotional agent architecture (using an appraisal mechanism) the robot was built with the capabilities of emotional appraisal and thus was able to express and share its emotions verbally throughout the game. Contrary to what was expected, emotional sharing in this context seemed to damage the social interaction with the users.

We have constructed a system that supports unscripted social interaction between a player and virtual characters, where the participants pursue internal agendas and respond to one another in real-time.  Our emphasis on unscripted interaction means that the characters must accept dynamically generated performance requests, while our concern with social interaction implies that the characters must interleave performances with an attention to natural flow that encourages social engagement. We present initial work on a performance management mechanism that produces this interleaving.   It initiates and suspends character performances by allocating animation resources to requests via a utility function representing aesthetic concerns.  That function weighs extrinsic factors reflecting the purpose of taking an action against intrinsic ones that concern features of a given performance.  We show, via multiple short videos, that the features are individually material to the aesthetic quality of the result and that the mechanism can produce aesthetically pleasing performances on par with the best hand-generated prioritization scheme. We argue, anecdotally, that the parameters of the model are easy to identify, suggesting that the feature vocabulary is both intuitive and useful for shaping character performances.

This abstract describes the development of algorithms for personalized anxiety reduction feedback for use by a robot buddy interacting with a child about to receive intravenous therapy (an IV insertion). This three-phase study is currently being conducted; it consists of two data collections to determine domain-specific approaches, followed by the full study with personalized anxiety-reducing feedback. Participants receiving personalized feedback will be compared to participants with a non-personalized robot (to control for novelty) and a no robot condition (baseline control).

We present Heapcraft: an open-source suite of tools for monitoring and improving collaboration in Minecraft. At the core of our system is a data collection and analysis framework for recording gameplay. We collected over 3451 player-hours of game behavior from 908 different players, and performed a general study of online collaboration. To make our game analytics easily accessible, we developed interactive information visualization tools and an analysis framework for players, administrators, and researchers to explore graphs, maps and timelines of live server activity. As part of our research, we introduce the collaboration index, a metric which allows server administrators and researchers to quantify, predict, and improve collaboration on Minecraft servers. Our analysis reveals several possible predictors of collaboration which can be used to improve collaboration on Minecraft servers. Heapcraft is designed to be general, and has the potential to be used for other shared online virtual worlds.