This paper focuses on task assignments to workers in mobile crowdsoucing systems. The current method does not work so well since it considers only workers who are ready to work at the time of optimization. Our method handles workers' day-long schedules, creates a `time-extended' worker-task graph that expresses the relationships between workers and tasks over a time period and finds the best set of worker-task-time triples. Our evaluation using real world visiting logs shows it increases the rate of assigned tasks by more than 8.2% compared with a state-of-the-art assignment method.

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.

We propose novel algorithms for the problem of crowdsourcing binary labels. Such binary labeling tasks are very common in crowdsourcing platforms, for instance, to judge the appropriateness of web content or to flag vandalism. We propose two unsupervised algorithms: one simple to implement albeit derived heuristically, and one based on iterated bayesian parameter estimation of user reputation models. We provide mathematical insight into the benefits of the proposed algorithms over existing approaches, and we confirm these insights by showing that both algorithms offer improved performance on many occasions across both synthetic and real-world datasets obtained via Amazon Mechanical Turk.

An open area of research for AI in games is how to provide unique gameplay experiences that present specialized game content to users based on their preferences, in-game actions, or the system's goals. The area of procedural content generation (PCG) focuses on creating or modifying game worlds, assets, and mechanics to generate tailored or personalized game experiences. Similarly, the area of interactive narrative (IN) focuses on creating or modifying story worlds, events, and domains to generate tailored or personalized story experiences. In this paper we describe a game engine that utilizes a PCG pipeline to generate and control a range of gameplay experiences from an underlying IN experience management construct.

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.

Learning about a new area of knowledge is challenging for novices partly because they are not yet aware of which topics are most important. The Internet contains a wealth of information for learning the underlying structure of a domain, but relevant sources often have diverse structures and emphases, making it hard to discern what is widely considered essential knowledge vs. what is idiosyncratic. Crowdsourcing offers a potential solution because humans are skilled at evaluating high-level structure, but most crowd micro-tasks provide limited context and time. To address these challenges, we present Crowdlines, a system that uses crowdsourcing to help people synthesize diverse online information. Crowdworkers make connections across sources to produce a rich outline that surfaces diverse perspectives within important topics. We evaluate Crowdlines with two experiments. The first experiment shows that a high context, low structure interface helps crowdworkers perform faster, higher quality synthesis, while the second experiment shows that a tournament-style (parallelized) crowd workflow produces faster, higher quality, more diverse outlines than a linear (serial/iterative) workflow.  

Plan synthesis often requires complete domain models and initial states as input. In many real world applications, it is difficult to build domain models and provide complete initial state beforehand. In this paper we propose to turn to the crowd for help before planning. We assume there are annotators available to provide information needed for building domain models and initial states. However, there might be a substantial amount of discrepancy within the inputs from the crowd. It is thus challenging to address the planning problem with possibly noisy information provided by the crowd. We address the problem by two phases. We first build a set of Human Intelligence Tasks (HITs), and collect values from the crowd. We then estimate the actual values of variables and feed the values to a planner to solve the problem.

A key challenge in participatory sensing systems has been the design of incentive mechanisms that motivate individuals to contribute data to consuming applications. Emerging trends in urban development and smart city planning indicate the use of citizen reports to gather insights and identify areas for transformation. Consumers of these reports (e.g. city agencies) typically associate non-uniform utility (or values) to different reports based on the spatio-temporal context of the reports. For example, a report indicating traffic congestion near an airport, in early morning hours, would tend to have much higher utility than a similar report from a sparse residential area. In such cases, the design of an incentive mechanism must motivate participants, via appropriate rewards (or payments), to provide higher utility reports when compared to less valued ones. The main challenge in designing such an incentive scheme is two-fold: (i) lack of prior knowledge of participants in terms of their availability (i.e. who are in the vicinity) and reporting behaviour (i.e. what are the rewards expected); and (ii) minimizing payments to the reporters while ensuring that the desired number of reports are collected. In this paper, we propose STOC-PISCES, an algorithm that guarantees a stochastic optimal solution in the generalized setting of an unknown set of participants, with non-deterministic availabilities and stochastically rational reporting behaviour. The superior performance of STOC-PISCES in experimental settings, based on real-world data, endorses its adoption as an incentive strategy in participatory sensing applications like smart city management.

Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.

We address the problem of acquiring reliable ratings of items such as restaurants or movies from the crowd. We propose a crowdsourcing platform that takes into consideration the workers’ skills with respect to the items being rated and assigns workers the best items to rate. Our platform focuses on acquiring ratings from skilled workers and for items that only have a few ratings. We evaluate the effectiveness of our system using a real-world dataset about restaurants.