Magy Seif El-Nasr
Other affiliations: Texas A&M University, Northeastern University, Northwestern University ...read more
Bio: Magy Seif El-Nasr is an academic researcher from University of California, Santa Cruz. The author has contributed to research in topics: Game design & Game mechanics. The author has an hindex of 24, co-authored 176 publications receiving 3139 citations. Previous affiliations of Magy Seif El-Nasr include Texas A&M University & Northeastern University.
Papers published on a yearly basis
TL;DR: This paper proposes a new computational model of emotions that can be incorporated into intelligent agents and other complex, interactive programs and demonstrates empirically through a computer simulation of a pet that the adaptive components of the model are crucial to users' assessments of the believability of the agent's interactions.
Abstract: Emotions are an important aspect of human intelligence and have been shown to play a significant role in the human decision-making process. Researchers in areas such as cognitive science, philosophy, and artificial intelligence have proposed a variety of models of emotions. Most of the previous models focus on an agent's reactive behavior, for which they often generate emotions according to static rules or pre-determined domain knowledge. However, throughout the history of research on emotions, memory and experience have been emphasized to have a major influence on the emotional process. In this paper, we propose a new computational model of emotions that can be incorporated into intelligent agents and other complex, interactive programs. The model uses a fuzzy-logic representation to map events and observations to emotional states. The model also includes several inductive learning algorithms for learning patterns of events, associations among objects, and expectations. We demonstrate empirically through a computer simulation of a pet that the adaptive components of the model are crucial to users' assessments of the believability of the agent's interactions.
30 Mar 2013
TL;DR: Game Analytics - Maximizing the Value of Player Data is the first book on the topic of game analytics; the process of discovering and communicating patterns in data towards evaluating and driving action, improving performance and solving problems in game development and game research.
Abstract: Developing a successful game in todays market is a challenging endeavor. Thousands of titles are published yearly, all competing for players time and attention. Game analytics has emerged in the past few years as one of the main resources for ensuring game quality, maximizing success, understanding player behavior and enhancing the quality of the player experience. It has led to a paradigm shift in the development and design strategies of digital games, bringing data-driven intelligence practices into the fray for informing decision making at operational, tactical and strategic levels. Game Analytics - Maximizing the Value of Player Data is the first book on the topic of game analytics; the process of discovering and communicating patterns in data towards evaluating and driving action, improving performance and solving problems in game development and game research. Written by over 50 international experts from industry and research, it covers a comprehensive range of topics across more than 30 chapters, providing an in-depth discussion of game analytics and its practical applications. Topics covered include monetization strategies, design of telemetry systems, analytics for iterative production, game data mining and big data in game development, spatial analytics, visualization and reporting of analysis, player behavior analysis, quantitative user testing and game user research. This state-of-the-art volume is an essential source of reference for game developers and researchers. Key takeaways include: Thorough introduction to game analytics; covering analytics applied to data on players, processes and performance throughout the game lifecycle.In-depth coverage and advice on setting up analytics systems and developing good practices for integrating analytics in game-development and -management.Contributions by leading researchers and experienced professionals from the industry, including Ubisoft, Sony, EA, Bioware, Square Enix, THQ, Volition, and PlayableGames. Interviews with experienced industry professionals on how they use analytics to create hit games.
01 Jan 2006
TL;DR: The use of modifying, or modding, existing games as a means to learn computer science, mathematics, physics, and aesthetic principles and how different engines can be used to focus students on the acquisition of particular skills and concepts is described.
Abstract: There has been a recent increase in the number of game environments or engines that allow users to customize their gaming experiences by building and expanding game behavior. This article describes the use of modifying, or modding, existing games as a means to learn computer science, mathematics, physics, and aesthetic principles. We describe two exploratory case studies of game modding in classroom settings to illustrate skills learned by students as a result of modding existing games. We also discuss the benefits of learning computer sciences skills (e.g., 3D graphics/mathematics, event-based programming, software engineering, etc.) through large design projects and how game design motivates students to acquire and apply these skills. We describe our use of multiple game modding environments in our classes. In addition, we describe how different engines can be used to focus students on the acquisition of particular skills and concepts.
26 May 1999
TL;DR: The approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent which has a wide range of applications from designing intelligent training systems to video games and animation tools.
Abstract: The ability to express emotions is important for creating believable interactive characters. To simulate emotional expressions in an interactive environment, an intelligent agent needs both an adaptive model for generating believable responses, and a visualization model for mapping emotions into facial expressions. Recent advances in intelligent agents and in facial modeling have produced effective algorithms for these tasks independently. We describe a method for integrating these algorithms to create an interactive simulation of an agent that produces appropriate facial expressions in a dynamic environment. Our approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent which has a wide range of applications from designing intelligent training systems to video games and animation tools.
10 Apr 2010
TL;DR: A set of cooperative patterns identified based on analysis of fourteen cooperative games are presented and validated through inter-rater agreement, and several effective cooperative patterns and lessons for future cooperative game designs are identified.
Abstract: Cooperative design has been an integral part of many games. With the success of games like Left4Dead, many game designers and producers are currently exploring the addition of cooperative patterns within their games. Unfortunately, very little research investigated cooperative patterns or methods to evaluate them. In this paper, we present a set of cooperative patterns identified based on analysis of fourteen cooperative games. Additionally, we propose Cooperative Performance Metrics (CPM). To evaluate the use of these CPMs, we ran a study with a total of 60 participants, grouped in 2-3 participants per session. Participants were asked to play four cooperative games (Rock Band 2, Lego Star Wars, Kameo, and Little Big Planet). Videos of the play sessions were annotated using the CPMs, which were then mapped to cooperative patterns that caused them. Results, validated through inter-rater agreement, identify several effective cooperative patterns and lessons for future cooperative game designs.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Jan 2003
TL;DR: In this paper, Sherry Turkle uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, virtual reality, and the on-line way of life.
Abstract: From the Publisher: A Question of Identity Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.