Learning Human Behavior From Observation For Gaming Applications

The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value

Neuroevolution in Games: State of the Art and Open Challenges

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table (Table 1

Autonomous characters in virtual environments: The technologies involved in artificial life and their affects on perceived intelligence and playability of computer games

Computer games are viewed by academics as un֊grounded hack and patch experiments. "The industry lacks the formalism and requirement for a "perfect" solution often necessary in the academic world " [Woob]. Academic Artifical Intelligence (AI) is often viewed as un-implementable and narrow minded by the majority of ทon-AI programmer. "Historically, AI tended to be focused, containing detailed problems and domain-specific techniques. This focus makes for easier study - or engineering - of particular solutions. " [СһаОЗ .By implementing several well known AI techniques into the same gaming environment and judging users reactions this project aims to make links between the academic nature of AI, as well as investigate the nature of practical implementation in a gaming environment. An online Java implemented version of the 1970'ร classic Space Invaders has been developed and tested, with the Aliens being controlled by 6 different approaches to modelling AI functions. In total information from 334 individuals games was recorded. Different types of games AI can create highly varied gaming experience as highlighted by the range of values and high standard deviation values seen in the results. The link between complex behaviour, complex control systems and perceived intelligence was not supported. A positive correlation identified between how fun the users found the game and how intelligent they perceived the Aliens to be, would seem to be logical. As games get visually more and more impressive, the need for intelligent characters cannot be denied because it is one of the few way in which games can set themselves apart from the competition. Conclusions identified that computer games must remain focussed on their end- goal, that of producing a fun game. Whilst complex and clever AI can help to achieve it, the AI itself can never overshadow the end result

An Information-Theoretic Framework for Consistency Maintenance in Distributed Interactive Applications

Distributed Interactive Applications (DIAs) enable geographically dispersed users to interact with each other in a virtual environment. A key factor to the success of a DIA is the maintenance of a consistent view of the shared virtual world for all the participants. However, maintaining consistent states in DIAs is difficult under real networks. State changes communicated by messages over such networks suffer latency leading to inconsistency across the application. Predictive Contract Mechanisms (PCMs) combat this problem through reducing the number of messages transmitted in return for perceptually tolerable inconsistency. This thesis examines the operation of PCMs using concepts and methods derived from information theory. This information theory perspective results in a novel information model of PCMs that quantifies and analyzes the efficiency of such methods in communicating the reduced state information, and a new adaptive multiple-model-based framework for improving consistency in DIAs. The first part of this thesis introduces information measurements of user behavior in DIAs and formalizes the information model for PCM operation. In presenting the information model, the statistical dependence in the entity state, which makes using extrapolation models to predict future user behavior possible, is evaluated. The efficiency of a PCM to exploit such predictability to reduce the amount of network resources required to maintain consistency is also investigated. It is demonstrated that from the information theory perspective, PCMs can be interpreted as a form of information reduction and compression. The second part of this thesis proposes an Information-Based Dynamic Extrapolation Model for dynamically selecting between extrapolation algorithms based on information evaluation and inferred network conditions. This model adapts PCM configurations to both user behavior and network conditions, and makes the most information-efficient use of the available network resources. In doing so, it improves PCM performance and consistency in DIAs

Characterizing the Effects of Local Latency on Aim Performance in First Person Shooters

Real-time games such as first-person shooters (FPS) are sensitive to even small amounts of lag. The effects of network latency have been studied, but less is known about local latency -- that is, the lag caused by local sources such as input devices, displays, and the application. While local latency is important to gamers, we do not know how it affects aiming performance and whether we can reduce its negative effects. To explore these issues, we tested local latency in a variety of real-world gaming systems and carried out a controlled study focusing on targeting and tracking activities in an FPS game with varying degrees of local latency. In addition, we tested the ability of a lag compensation technique (based on aim assistance) to mitigate the negative effects. To motivate the need for these studies, we also examined how aim in FPS differs from pointing in standard 2D tasks, showing significant differences in performance metrics. Our studies found local latencies in the real-world range from 23 to 243~ms that cause significant and substantial degradation in performance (even for latencies as low as 41~ms). The studies also showed that our compensation technique worked well, reducing the problems caused by lag in the case of targeting, and removing the problem altogether in the case of tracking. Our work shows that local latency is a real and substantial problem -- but game developers can mitigate the problem with appropriate compensation methods

Spartan Daily, February 11, 1988

Volume 90, Issue 9https://scholarworks.sjsu.edu/spartandaily/7668/thumbnail.jp

Spartan Daily, February 11, 1988

Volume 90, Issue 9https://scholarworks.sjsu.edu/spartandaily/7668/thumbnail.jp

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion