Emotions are a key element in all human interactions. It is well documented that individual- and group-level interactions have different emotional expressions and humans are by nature extremely competent in perceiving, adapting and reacting to them. However, when developing social robots, emotions are not so easy to cope with. In this paper we introduce the concept of emotional climate applied to human-robot interaction (HRI) to define a group-level emotional expression at a given time. By doing so, we move one step further in developing a new tool that deals with group emotions within HRI.

Performing experimental research on robotic platforms involves numerous practical complications, while studying collaborative interactions and efficiently collecting data from humans benefit from real time response. Roboticists can circumvent some complications by using simulators like Gazebo to test algorithms and building games like the Mars Escape game to collect data. Making use of existing resources for simulation and game creation requires the development of assets and algorithms along with the recruitment and training of users. We have created a Minecraft mod called BurlapCraft which enables the use of the reinforcement learning and planning library BURLAP to model and solve different tasks within Minecraft. BurlapCraft makes AI-HRI development easier in three core ways: the underlying Minecraft environment makes the construction of experiments simple for the developer and so allows the rapid prototyping of experimental setup; BURLAP contributes a wide variety of extensible algorithms for learning and planning, allowing easy iteration and development of task models and algorithms; and the familiarity and ubiquity of Minecraft trivializes the recruitment and training of users. To validate BurlapCraft as a platform for AI development, we demonstrate the execution of A*, BFS, RMax, language understanding, and learning language groundings from user demonstrations in five Minecraft "dungeons."

In typical human interactions, nonverbal behaviors such as eye gazes and gestures serve to augment and reinforce spoken communication. To use similar nonverbal behaviors in human-robot interactions, researchers can apply artificial intelligence techniques such as machine learning, cognitive modeling, and computer vision. But knowledge of nonverbal behavior can also benefit artificial intelligence: because nonverbal communication can reveal human mental states, these behaviors provide additional input to artificial intelligence problems such as learning from demonstration, natural language processing, and motion planning. This article describes how nonverbal communication in HRI can benefit from AI techniques as well as how AI problems can use nonverbal communication in their solutions.

This paper explores the concept of Continuation Desire further by investigating the behavioral intent of players’ desire to keep playing. User experience is a complex, multifaceted topic, which is commonly studied through different aspects namely engagement, continuation desire, immersion, flow experience, motivation and enjoyment — yet it is difficult to measure. These concepts were conceptualized into different factors and thereby it was identified which of them are related. This resulted in a synthesized model that was based on the Theory of Planned Behavior model. This model takes into account the perceived user experience factors relevant for Continuation Desire and then attempts to predict players’ intention to continue playing. Structural Equation Modeling analysis was performed to validate the model and to predict the intention of continuation desire. At the same time, exploring why people continue playing, based on experiments using Candy Crush Saga, one of the most popular Free-to-Play mobile games worldwide. The findings indicate that motivation is an important factor of Continuation Desire in Free-to-Play mobile games, with engagement, enjoyment and flow being less important. This paper contributes an early work of a factor-based exploration of measuring user experience and their continuation desire.

In this article, we describe our method of modeling sociolinguistic behaviors of players in massively multi-player online games. The focus of this paper is leadership, as it is manifested by the participants engaged in discussion, and the automated modeling of this complex behavior in virtual worlds. We first approach the research question of modeling from a social science perspective, and ground our models in theories from human communication literature. We then adapt a two-tiered algorithmic model that derives certain mid-level sociolinguistic behaviors--such as Task Control, Topic Control and Disagreement from discourse linguistic indicators--and combines these in a weighted model to reveal the complex role of Leadership. The algorithm is evaluated by comparing its prediction of leaders against ground truth – the participants’ own ratings of leadership of themselves and their conversation peers. We find the algorithm performance to be considerably better than baseline.

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.

Game designers can use computer-aided game design methods to quantitatively compare player skill levels, different game variants, and learning rates, for the purpose of modeling how players will likely experience a game. We use Monte-Carlo simulation, hazard functions, and survival analysis to show how difficulty will quantitatively change throughout a game level as we vary skill, game parameters, and learning rates. We give a mathematical overview of survival analysis, present empirical data analyses of our player models for each game variant, and provide theoretical probability distributions for each game. This analysis shows the quantitative reasons why balancing a game for a wide range of player skill can be difficult; our player modeling provides tools for tuning this game balance. We also analyze the score distribution of over 175 million play sessions of a popular online Flappy Bird variant to demonstrate how learning effects can impact scores, implying that learning is crucial aspect of player modeling.

Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, representing five different decision making styles archetypal for the game. The personas vary solely in the weights of decision-making utilities that describe their valuation of a set affordances in MiniDungeons 2. By configuring these weights using designer expert knowledge, and passing the configurations directly to the MCTS algorithm, we make the personas exhibit a number of distinct decision making and play styles.

Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.

An integral part of social believability in role playing games is believability of non-player characters (NPC). In this paper we argue for the importance of believability in NPCs, even those that are completely outside of any pre-written quest or plot. We present NPCAgency, a system designed to generate many conversational NPCs as packaged narrative assets that can be shared and imported into various projects to increase story-world immersion. We believe such a system can help solve two problems. First, the authorial burden of the game designer is lessened, allowing renderings of large numbers of NPCs, each with their own unique background and conversation topics, all conforming to the norms of a predefined “universe”. Second, the immersive aspect of the game is heightened as the player can engage complex characters with lengthy dialogue affordances. We demonstrate the concept by generating fifty characters with attributes drawn from “Game of Thrones” (GOT) / “A Song of Ice and Fire” universe, and exporting them as Inform 7 code, a popular declarative interactive fiction (IF) programming language and authoring tool. A user study of thirty-seven Inform 7 programmers demonstrates that a 62% majority find the tool useful enough to consider for their own work. Further 70% said they would use the system to create “Game of Thrones” background characters for their own projects.