Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact.

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.

Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value-aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the children's educational comic strip, Goofus & Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non-normative by identifying if they align with the main characters' behavior. We also report the models' performance when transferring to two unrelated tasks with little to no additional training on the new task.

Smart cities are a growing paradigm in the design of systems that interact with one another for informed and efficient decision making, empowered by data and technology, of resources in a city. The diffusion of information to citizens in a smart city will rely on social trends and smart advertisement. Online social networks (OSNs) are prominent and increasingly important platforms to spread information, observe social trends, and advertise new products. To maximize the benefits of such platforms in sharing information, many groups invest in finding ways to maximize the expected number of clicks as a proxy of these platform's performance. As such, the study of click-through rate (CTR) prediction of advertisements, in environments like online social media, is of much interest. Prior works build machine learning (ML) using user-specific data to classify whether a user will click on an advertisement or not. For our work, we consider a large set of Facebook advertisement data (with no user data) and categorize targeted interests into thematic groups we call conceptual nodes. ML models are trained using the advertisement data to perform CTR prediction with conceptual node combinations. We then cast the problem of finding the optimal combination of conceptual nodes as an optimization problem. Given a certain budget k, we are interested in finding the optimal combination of conceptual nodes that maximize the CTR. We discuss the hardness and possible NP-hardness of the optimization problem. Then, we propose a greedy algorithm and a genetic algorithm to find near-optimal combinations of conceptual nodes in polynomial time, with the genetic algorithm nearly matching the optimal solution. We observe that simple ML models can exhibit the high Pearson correlation coefficients w.r.t. click predictions and real click values. Additionally, we find that the conceptual nodes of “politics”, “celebrity”, and “organization” are notably more influential than other considered conceptual nodes.

In this work, we present an approach for performing computational storytelling in open domain based on Author Goals. Author Goals are constraints placed on a story event directed by the author of the system. There are two challenges present in this type of story generation: (1) automatically acquiring a model of story progression, and (2) guiding the progress of story progression in light of different goals. We propose a novel approach to story generation based on probabilistic graphical models and Loopy Belief Propagation (LBP) that addresses both of these problems. We show the applicability of our technique through a case study on the Visual Storytelling (VIST) 2017 dataset. We use image descriptions as author goals. This empirical analysis suggests that our approach is able to utilize goals information to better automatically generate stories.

In this paper, we explore how multi-modal video representations can be applied in an end-to-end fashion for automatically generating game commentary based on Let's Play videos using deep learning. We introduce a comprehensive pipeline that involves directly taking videos from YouTube and then using a sequence-to-sequence strategy to learn how to generate appropriate commentary. We evaluate our framework using Let's Play commentaries for the game Getting Over It with Bennet Foddy. To test the quality of the commentary generation, we apply perplexity to evaluate our language models using different input video representations to highlight different aspects of gameplay that might influence commentary.

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model toward a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.

We introduce \em AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior. We describe a rationalization technique that uses neural machine translation to translate internal state-action representations of an autonomous agent into natural language. We evaluate our technique in the Frogger game environment, training an autonomous game playing agent to rationalize its action choices using natural language. A natural language training corpus is collected from human players thinking out loud as they play the game. We motivate the use of rationalization as an approach to explanation generation and show the results of two experiments evaluating the effectiveness of rationalization. Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.

This paper shows how game analytics can be used to dynamically adapt casual game environments in order to increase session-level retention. Our technique involves using game analytics to create an abstracted game analytic space to make the problem tractable. We then model player retention in this space and use these models to make guided changes to game analytics in order to bring about a targeted distribution of game states that will, in turn, influence player behavior. Experiments performed showed that the adaptive versions of two different casual games, Scrabblesque and Sidequest: The Game, were able to better fit a target distribution of game states while also significantly reducing the quitting rate compared to the nonadaptive version of the games. We showed that these gains were not coming at the cost of player experience by performing a psychometric evaluation in which we measured player intrinsic motivation and engagement with the game environments. In both cases, we showed that players playing the adaptive version of the games reported higher intrinsic motivation and engagement scores than players playing the nonadaptive version of the games.

Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

My research involves creating and evaluating adaptive game environments using player models created with data-driven techniques and algorithms. I hypothesize that I will be able to change parts of a game to elicit certain behaviors from players,and that these changes will also result in an increase of engagement and/or intrinsic motivation. Initial results in my testbed game, Scrabblesque, indicate that data-driven models and techniques can be used to influence player behavior and that these changes in play behavior manifest themselves as an increase in engagement and intrinsic motivation.

In this paper, we present a data-driven technique for designing models of user behavior. Previously, player models were designed using user surveys, small-scale observation experiments, or knowledge engineering. These methods generally produced semantically meaningful models that were limited in their applicability. To address this, we have developed a purely data-driven methodology for generating player models based on past observations of other players. Our underlying assumption is that we can accurately predict what a player will do in a given situation if we examine enough data from former players that were in similar situations. We have chosen to test our method on achievement data from the MMORPG World of Warcraft. Experiments show that our method greatly outperforms a baseline algorithm in both precision and recall, proving that this method can create accurate player models based solely on observation data.