(MU-CTL-01-12) Towards Model Driven Game Engineering in SimSYS: Requirements for the Agile Software Development Process Game

Software Engineering (SE) and Systems Engineering (Sys) are knowledge intensive, specialized, rapidly changing disciplines; their educational infrastructure faces significant challenges including the need to rapidly, widely, and cost effectively introduce new or revised course material; encourage the broad participation of students; address changing student motivations and attitudes; support undergraduate, graduate and lifelong learning; and incorporate the skills needed by industry. Games have a reputation for being fun and engaging; more importantly immersive, requiring deep thinking and complex problem solving. We believe educational games are essential in the next generation of e-learning tools. An extensible, freely available, engaging, problem-based game platform that provides students with an interactive simulated experience closely resembling the activities performed in a (real) industry development project would transform the SE/Sys education infrastructure. Our goal is to extend the state-of-the-art research in SE/Sys education by investigating a game development platform (GDP) from an interdisciplinary perspective (education, game research, and software/systems engineering). A meta-model has been proposed to provide a rigourous foundation that integrates the three disciplines. The GDP is intended to support the semi-automated development of collections of scripted games and their execution, where each game embodies a specific set of learning objectives. The games are scripted using a template based approach. The templates integrate three approaches: use cases; storyboards; and state machines (timed, concurrent, hierarchical state machines). The specification templates capture the structure of the game (Game, Acts, Scenes, Screens, Challenges), storyline, characters (player, non-player, external), graphics, music/sound effects, rules, and so on. The instantiated templates are (manually) transformed into XML game scripts that can be loaded into the SimSYS Game Play Engine. As a game is played, the game play events are logged; they are analyzed to automatically assess a player’s accomplishments and automatically adapt the game play script. Currently, we are manually defining a collection of games. The games are being used to ensure the GDP is flexible and reliable (i.e., the prototype can load and correctly run a variety of game scripts), the ontology is comprehensive, and the templates assist in defining well-organized, modular game scripts. In this report, we present the initial part of an Agile Software Development Process game (Act I, Scenes 1 and 2) that embodies learning objectives related to SE fundamentals (requirements, architecture, testing, process); planning with Gantt charts; working with budgets; and selecting a team for an agile development project. A student player is rewarded in the game by getting hired, scoring points, or getting promoted to lead a project. The game has a variety of settings including a classroom, job fair, and a work environment with meeting rooms, cubicles, and a water cooler station. The main non-player characters include a teacher, boss, and an evil peer. In the future, semi-automated support for creating new game scripts will be explored using a wizard interface. The templates will be formally defined, supporting automated transformation into XML game scripts that can be loaded into the SimSYS Game Engine. We also plan to explore transforming the requirements into a notation that can be imported into a commercial tool that supports Statechart simulation

Controlling Risk of Web Question Answering

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.Comment: Accepted to SIGIR'2