In the last decade, scenario-based serious-games have become a main tool for learning new skills and capabilities. An important factor in the development of such systems is the overhead in time, cost and human resources to manually create the content for these scenarios. We focus on how to create content for scenarios in medical, military, commerce and gaming applications where maintaining the integrity and coherence of the content is integral for the system's success. To do so, we present an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. We use the crowd in three basic ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We found that the generated scenarios were rated as reliable and consistent by the crowd when compared to the scenarios that were originally captured. We also compared the generated scenarios to those created by traditional planning techniques. We found that both methods were equally effective in generated reliable and consistent scenarios, yet the main advantages of our approach is that the content we generate is more varied and much easier to create. We have begun integrating this approach within a scenario-based training application for novice investigators within the law enforcement departments to improve their questioning skills.
The term scenario have several definitions depending on the context it is used. According to @cite_10 Game scenarios describe, often transparently to the user, the way and order in which game events unfold. Two types of game scenarios can be distinguished, abstract and concrete. The abstract game scenarios describe how other objects inter-relate. The concrete game scenarios are explicitly presented in the game, for example as part of the game narrative.''. In this paper, we refer to concrete scenario types. As such, we use the term scenario to describe a sequence of activity events (activities) and their descriptions (narratives).
@cite_10
2111589951 2157731580 2136794369 1877442346
Abstract Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This Synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them."This introduction is just what a growing multidisciplinary audience needs: it is concise, authoritative, up to date, and clear on the important conceptual issues." --Robert Stalnaker, MIT, Linguistics and Phil... ABSTRACTBasketball games evolve continuously in space and time as players constantly interact with their teammates, the opposing team, and the ball. However, current analyses of basketball outcomes rely on discretized summaries of the game that reduce such interactions to tallies of points, assists, and similar events. In this article, we propose a framework for using optical player tracking data to estimate, in real time, the expected number of points obtained by the end of a possession. This quantity, called expected possession value (EPV), derives from a stochastic process model for the evolution of a basketball possession. We model this process at multiple levels of resolution, differentiating between continuous, infinitesimal movements of players, and discrete events such as shot attempts and turnovers. Transition kernels are estimated using hierarchical spatiotemporal models that share information across players while remaining computationally tractable on very large data sets. In addition to estima... The author discusses games of both perfect and imperfect information at two levels of structural detail: players' local actions, and their global powers for determining outcomes of the game. Matching logical languages are proposed for both. In particular, at the "action level", imperfect information games naturally model a combined "dynamic-epistemic language"--and correspondences are found between special axioms in this language and particular modes of playing games with their information dynamics. At the "outcome level", the paper presents suitable notions of game equivalence, and some simple representation results. Copyright 2001 by Blackwell Publishing Ltd and the Board of Trustees of the Bulletin of Economic Research A games model of a programming language with higher-order store in the style of ML-references is introduced. The category used for the model is obtained by relaxing certain behavioural conditions on a category of games previously used to provide fully abstract models of pure functional languages. The model is shown to be fully abstract by means of factorization arguments which reduce the question of definability for the language with higher-order store to that for its purely functional fragment.