Poker as a Skill Game: Rational vs Irrational Behaviors

In many countries poker is one of the most popular card games. Although each variant of poker has its own rules, all involve the use of money to make the challenge meaningful. Nowadays, in the collective consciousness, some variants of poker are referred to as games of skill, others as gambling. A poker table can be viewed as a psychology lab, where human behavior can be observed and quantified. This work provides a preliminary analysis of the role of rationality in poker games, using a stylized version of Texas Hold'em. In particular, we compare the performance of two different kinds of players, i.e., rational vs irrational players, during a poker tournament. Results show that these behaviors (i.e., rationality and irrationality) affect both the outcomes of challenges and the way poker should be classified.Comment: 15 pages, 5 figure

Agents with faces : a study on the effects of personification of software agents

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (p. 129-133).by Tomoko Koka.M.S

Understanding Behavior via inverse reinforcement learning

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Do People Change their Behavior when the Handler is next to the Robot?

It is increasingly common for people to work alongside robots in a variety of situations. When a robot is completing a task, the handler of the robot may be present. It is important to know how people interact with the robot when the handler is next to the robot. Our study focuses on whether handler’s presence can affect human’s behavior toward the robot. Our experiment targets two different scenarios (handler present and handler absent) in order to find out human’s behavior change toward the robot. Results show that in the handler present scenario, people are less willing to interact with the robot. However, when people do interact with the robot, they tend to interact with both the handler and the robot. This suggests that researchers should consider the presence of a handler when designing for human-robot interactions

Extensible graphical game generator

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Vita.Includes bibliographical references (leaves 162-167).An ontology of games was developed, and the similarities between games were analyzed and codified into reusable software components in a system called EGGG, the Extensible Graphical Game Generator. By exploiting the similarities between games, EGGG makes it possible for someone to create a fully functional computer game with a minimum of programming effort. The thesis behind the dissertation is that there exist sufficient commonalities between games that such a software system can be constructed. In plain English, the thesis is that games are really a lot more alike than most people imagine, and that these similarities can be used to create a generic game engine: you tell it the rules of your game, and the engine renders it into an actual computer game that everyone can play.by Jon Orwant.Ph.D

Partially Observable Monte Carlo Planning in Public Game Tree for complex imperfect information game King UP

In the past, Game AI has been a challenging area where humans devote much effort to it. The game could be classified by whether players could fully observe the state of the game into two different game types: perfect information game and imperfect information game. While perfect information game has been studied well, like Go and chess, the research on imperfect-information games is limited to poker and dice. The board game King Up is a sort of imperfect information game with more complicated rules and attributes, such as partial observation, multiple players, partly shared goals, mixed strategy (cooperate and compete temporarily for goals), and a mixture of dynamic game and static game (vote and move). Until now, King UP has not been solved by existing methods. Extended from Partially Observable Monte Carlo Planning, we propose a novel algorithm called POMCP in a Public Game Tree to solve the problem, in which we also combine the techniques from CFR with POMCP. The public game tree is a data structure used to store belief over partial observable information and could be applied in most imperfect information games with multiple players; inspired by CFR, we propose local search and decision estimation processes to compute the value function over correlated state and action; we also implement two decision estimation methods, deterministic policy by Upper Confidence Bound and stochastic policy by Regret Matching. In the experiment, we evaluate the convergency situation of the algorithm. Also, we evaluate the algorithm’s performance by further experiments like tournaments between different decision estimations and verify the generalization by Rock-PaperScissors. At last, we rethink the motivation, insights, and contributions. Our algorithm could work with less prior knowledge and perform well in complex, imperfectinformation games with multiple players like King UP and mixture game types (dynamic and static game)

Non-invasive wearable sensing systems for continuous health monitoring and long-term behavior modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 212-228).Deploying new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems worldwide become strained by the aging populations. This thesis presents LiveNet, a distributed mobile system based on low-cost commodity hardware that can be deployed for a variety of healthcare applications. LiveNet embodies a flexible infrastructure platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification capabilities. Using LiveNet, we are able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. Most clinical sensing technologies that exist have focused on accuracy and reliability, at the expense of cost-effectiveness, burden on the patient, and portability. Future proactive health technologies, on the other hand, must be affordable, unobtrusive, and non-invasive if the general population is going to adopt them.(cont.) In this thesis, we focus on the potential of using features derived from minimally invasive physiological and contextual sensors such as motion, speech, heart rate, skin conductance, and temperature/heat flux that can be used in combination with mobile technology to create powerful context-aware systems that are transparent to the user. In many cases, these non-invasive sensing technologies can completely replace more invasive diagnostic sensing for applications in long-term monitoring, behavior and physiology trending, and real-time proactive feedback and alert systems. Non-invasive sensing technologies are particularly important in ambulatory and continuous monitoring applications, where more cumbersome sensing equipment that is typically found in medical and clinical research settings is not usable. The research in this thesis demonstrates that it is possible to use simple non-invasive physiological and contextual sensing using the LiveNet system to accurately classify a variety of physiological conditions. We demonstrate that non-invasive sensing can be correlated to a variety of important physiological and behavioral phenomenon, and thus can serve as substitutes to more invasive and unwieldy forms of medical monitoring devices while still providing a high level of diagnostic power.(cont.) From this foundation, the LiveNet system is deployed in a number of studies to quantify physiological and contextual state. First, a number of classifiers for important health and general contextual cues such as activity state and stress level are developed from basic non-invasive physiological sensing. We then demonstrate that the LiveNet system can be used to develop systems that can classify clinically significant physiological and pathological conditions and that are robust in the presence of noise, motion artifacts, and other adverse conditions found in real-world situations. This is highlighted in a cold exposure and core body temperature study in collaboration with the U.S. Army Research Institute of Environmental Medicine. In this study, we show that it is possible to develop real-time implementations of these classifiers for proactive health monitors that can provide instantaneous feedback relevant in soldier monitoring applications. This thesis also demonstrates that the LiveNet platform can be used for long-term continuous monitoring applications to study physiological trends that vary slowly with time.(cont.) In a clinical study with the Psychiatry Department at the Massachusetts General Hospital, the LiveNet platform is used to continuously monitor clinically depressed patients during their stays on an in-patient ward for treatment. We show that we can accurately correlate physiology and behavior to depression state, as well as to track changes in depression state over time through the course of treatment. This study demonstrates how long-term physiology and behavioral changes can be captured to objectively measure medical treatment and medication efficacy. In another long-term monitoring study, the LiveNet platform is used to collect data on people's everyday behavior as they go through daily life. By collecting long-term behavioral data, we demonstrate the possibility of modeling and predicting high-level behavior using simple physiologic and contextual information derived solely from ambulatory mobile sensing technology.by Michael Sung.Ph.D

Integration and evaluation of a gaming situation for long-term human-robot interaction: playing a game of pairs with Flobi using contextual knowledge

Kipp A. Integration and evaluation of a gaming situation for long-term human-robot interaction: playing a game of pairs with Flobi using contextual knowledge. Bielefeld: Bielefeld University; 2015.This doctoral thesis proposes a system for long-term Interaction between a robot and a human using a gaming context. A robot plays a game of pairs autonomously with a human. The system was developed to evaluate how to implement social assistive robots during space missions that occur under isolation conditions. The first part of the thesis presents the system as designed and implemented. Described are the different components used to realize autonomous interaction. The study itself was conducted in cooperation with the German Aerospace Center. Throughout the study, the proposed system performed robustly, and without major system failures. The participants interacted with the system continuously, and gave it average ratings for acceptability. No significant extraneous effects, such as those related to novelty were found. Nevertheless, problems with perception and classification led to negative ratings for the system’s competence. The second part of the thesis was motivated by findings from the isolation study. Integrated is a context knowledge system to increase interest in the interactions during long-term use. This made it possible to collect data on past interactions for use with further interactions. The results of a study showed that greater commitment to gaming interaction could be promoted by using context knowledge. In addition, implemented is a remotely controlled version of the system to evaluate, whether a robot without visual or speech recognition problems who played perfectly could influence the way the system was perceived. Results showed that ratings on participant enjoyment while playing with the system decreased when the playing system was too perfect. Additionally, a robot that played too perfectly appeared to promote unfair game-play behaviors in the human, likely as an attempt to cope with disadvantages such as lower memory capacity

Decision shaping and strategy learning in multi-robot interactions

Recent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants

The social poetics of analog virtual worlds : toying with alternate realities

textWhile online virtual worlds draw increasingly wider audiences of players and scholars alike, offline games continue to evolve into more complex and socially layered forms as well. This dissertation argues that virtual worlds need not exist as online, digital environments alone and probes three genres of non-digital gaming for evidence of the virtual: tabletop role-playing games, murder-mystery events, and localized alternate reality games. More broadly, then, this dissertation is about deliberate make-belief: practiced by adults, taken seriously by participants, engaged with for long hours at a time, performed in public, and integrated into everyday social relationships. Drawing on scholars who study games as social activities (McGonigal 2006, Montola 2012) and social institutions (Goffman 1974, Searle 1995), I present three ethnographic case studies that illustrate how complex forms of social gaming can conjure and sustain environments best understood as analog virtual worlds. Through the widespread use of mobile technologies and the concerted efforts of innovators, game spaces are increasingly permeating our everyday lives on- and offline. This dissolving boundary demands anthropologists to revisit questions of how, where, and with whom we play games. Dovetailing Martin Heidegger’s notions of worlding and poiesis to the semiotics of C.S. Peirce, this dissertation investigates how new forms of social gaming demonstrate the same qualities of shared intentionality, intersubjectivity, and performance essential to the production of new social meaning and cultural forms. Following, I situate the bold ethnographic case studies of make-belief in dialogue with scholars who figure exclusively online virtual worlds (Castronova 2005, Taylor 2006, Boellstorff 2008) and argue that analyzing both on- and offline virtual worlds together can help scholars better understand the fundamental nature of social interaction and shared intentionality, those everyday mechanisms that both sustain personal relationships on the one hand and maintain our broadest and most serious social institutions on the other.Anthropolog