Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.

Indexter is a plan-based computational model of narrative discourse which leverages cognitive scientific theories of how events are stored in memory during online comprehension. These discourse models are valuable for static and interactive narrative generation systems because they allow the author to reason about the audience's understanding and attention as they experience a story. A pair of Indexter events can share up to five indices: protagonist , time , space , causality , and intentionality . We present the first in a planned series of evaluations that will explore increasingly nuanced methods of using these indices to predict salience. The Pairwise Event Salience Hypothesis states that when a past event shares one or more indices with the most recently narrated event, that past event is more salient than one which shares no indices with the most recently narrated event. A crowd-sourced (n=200) study of 24 short text stories that control for content, text, and length supports this hypothesis. While this is encouraging, we believe it also motivates the development of a richer model that accounts for intervening events, narrative complexity, and episodic memory decay.

IW(1) is a simple search algorithm that assumes that states can be characterized in terms of a set of boolean features or atoms. IW(1) consists of a standard breadth-first search with one variation: a newly generated state is pruned if it does not make a new atom true. Thus, while a breadth-first search runs in time that is exponential in the number of atoms, IW(1) runs in linear time. Variations of the algorithm have been shown to yield state-of-the-art results in classical planning and more recently in the Atari video games. In this paper, we use the algorithm for selecting actions in the games of the general video-game AI competition (GVG-AI) which, unlike classical planning problems and the Atari games, are stochastic. We evaluate a variation of the algorithm over 30 games under different time windows using the number of wins as the performance measure. We find that IW(1) does better than the sample MCTS and OLMCTS controllers for all time windows with the performance gap growing with the window size. The exception are the puzzle-like games where all the algorithms do poorly. For such problems, we show that much better results can be obtained with the IW(2) algorithm, which is like IW(1), except that states are pruned in the breadth-first search when they fail to make true a new pair of atoms.

Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art for AI in modern strategy video games which have large state and action spaces. The first is a novel method for performing hierarchical search using a portfolio of algorithms to reduce the search space while maintaining strong action candidates. The second contribution is an overall AI architecture for strategy video games using this portfolio search method. The proposed methods are used as the AI system for Prismata, an online turn-based strategy game by Lunarch Studios. This system is evaluated using three experiments: on-line play vs.~human players, off-line AI tournaments to test the relative strengths of the AI bots, and a survey to determine user satisfaction of the system so far. Our result show that this system achieves a skill level in the top 25% of human players on the ranked ladder, can be modified quickly to create different difficulty settings, is robust to changes in game unit properties, and creates an overall AI experience which is user rated more enjoyable than those currently found in similar video games.

Real-Time Strategy (RTS) games have shown to be very resilient to standard  adversarial tree search techniques. Recently, a few approaches to tackle their complexity have emerged that use game state or move abstractions, or both. Unfortunately, the supporting experiments were either limited to simpler RTS environments ( u RTS, SparCraft) or lack testing against state-of-the-art game playing agents. Here, we propose Puppet Search , a new adversarial search framework based on scripts that can expose choice points to a look-ahead search procedure. Selecting a combination of a script and decisions for its choice points represents a move to be applied next. Such moves can be executed in the actual game, thus letting the script play, or in an abstract representation of the game state which can be used by an adversarial tree search algorithm. Puppet Search returns a principal variation of scripts and choices to be executed by the agent for a given time span. We implemented the algorithm in a complete StarCraft bot. Experiments show that it matches or outperforms all of the individual scripts that it uses when playing against state-of-the-art bots from the 2014 AIIDE StarCraft competition.

In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in videogames, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present InvJPS, an “inventory-driven” pathfinding approach based on the highly successful grid-based Jump-Point-Search (JPS) algorithm. We show, formally and experimentally, that the InvJPS preserves JPS’s optimality guarantees and its symmetry breaking advantages in inventory-based variants of game maps.

Community Question Answering (CQA) websites are a popular tool for internet users to fulfill diverse information needs. Posted questions can be multiple sentences long and span diverse domains. They go beyond factoid questions and can be conversational, opinion-seeking and experiential questions, that might have multiple, potentially conflicting, useful answers from different users. In this paper, we describe a large-scale formative study to collect commonsense properties of questions and answers from 18 diverse communities from stackexchange.com. We collected 50,000 human judgments on 500 question-answer pairs. Commonsense properties are features that humans can extract and characterize reliably by using their commonsense knowledge and native language skills, and no special domain expertise is assumed. We report results and suggestions for designing human computation tasks for collecting commonsense semantic judgments.

Automated systems that aid in the development of Multiple Choice Questions (MCQs) have value for both educators, who spend large amounts of time creating novel questions, and students, who spend a great deal of effort both practicing for and taking tests. The current approach for measuring question difficulty in MCQs relies on models of how good pupils will perform and contrasts that with their lower-performing peers. MCQs can be difficult in many ways. This paper looks specifically at the effect of both the number of words in the question stem and in the answer options on question difficulty. This work is based on the hypothesis that questions are more difficult if the stem of the question and the answer options are semantically far apart. This hypothesis can be normalized, in part, with an analysis of the length of texts being compared. The MCQs used in the experiments were voluntarily authored by university students in biology courses. Future work includes additional experiments utilizing other aspects of this extensive crowdsourced data set.

Dialog between drivers and speech-based vehicle interfaces can be used as an instrument to find out what drivers might be concerned, confused or curious about in driving simulator studies. Eliciting on-going conversation with drivers about topics that go beyond navigation, control of entertainment systems, or other traditional driving related tasks is important to getting drivers to engage with the activity in an open-ended fashion. In a structured improvisational Wizard of Oz study that took place in a highly immersive driving simulator, we engaged participant drivers (N=6) in an autonomous driving course where the vehicle spoke to drivers using computer-generated natural language speech. Using microanalyses of the drivers’ responses to the car’s utter- ances, we identify a set of topics that are expected and treated as appropriate by the participants in our study, as well as a set of topics and conversational strategies that are treated as inappropriate. We also show that it is just these unexpected, inappropriate utterances that eventually increase users’ trust in the system, make them more at ease, and raise the system’s acceptability as a communication partner.

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.