2,597 research outputs found
A Real-time Strategy Agent Framework and Strategy Classifier for Computer Generated Forces
This research effort is concerned with the advancement of computer generated forces AI for Department of Defense (DoD) military training and education. The vision of this work is agents capable of perceiving and intelligently responding to opponent strategies in real-time. Our research goal is to lay the foundations for such an agent. Six research objectives are defined: 1) Formulate a strategy definition schema effective in defining a range of RTS strategies. 2) Create eight strategy definitions via the schema. 3) Design a real-time agent framework that plays the game according to the given strategy definition. 4) Generate an RTS data set. 5) Create an accurate and fast executing strategy classifier. 6) Find the best counterstrategies for each strategy definition. The agent framework is used to play the eight strategies against each other and generate a data set of game observations. To classify the data, we first perform feature reduction using principal component analysis or linear discriminant analysis. Two classifier techniques are employed, k-means clustering with k-nearest neighbor and support vector machine. The resulting classifier is 94.1% accurate with an average classification execution speed of 7.14 us. Our research effort has successfully laid the foundations for a dynamic strategy agent
Virtual Battlespace Behavior Generation Through Class Imitation
Military organizations need realistic training scenarios to ensure mission readiness. Developing the skills required to differentiate combatants from non-combatants is very important for ensuring the international law of armed conflict is upheld. In Simulated Training Environments, one of the open challenges is to correctly simulate the appearance and behavior of combatant and non-combatant agents in a realistic manner. This thesis outlines the construction of a data driven agent that is capable of imitating the behaviors of the Virtual BattleSpace 2 behavior classes while our agent is configured to advance to a geographically specific goal. The approach and the resulting agent promotes and motivates the idea that Opponent and Non-Combatant behaviors inside of simulated environments can be improved through the use of behavioral imitation
Learning Opportunities 2010/2011
The graduation requirements of the Illinois Mathematics and Science Academy are in concert with those maintained by the State of Illinois with additional requirements as established by the IMSA Board of Trustees. Each semester students must take a minimum of 2.5 credits and a maximum of 3.5 credits. One-semester classes generally receive .5 credits and two semester classes (e.g., World Languages) generally receive 1.0 credit. Most students will take between 5 and 7 academic classes per semester. Fine Arts, Wellness, and Independent Study courses do not count towards the 2.5 credit minimum. However, if a student wishes to take 3.5 credits of academic classes, he/she may choose to enroll in a Fine Arts or Independent Study course on a Pass/Fail basis (see below)
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 productâs 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
A Cloud Based Disaster Management System
The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The systemâs purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate whatâif scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.info:eu-repo/semantics/publishedVersio
The Construction of Locative Situations: the Production of Agency in Locative Media Art Practice
This thesis is a practice led enquiry into Locative Media (LM) which argues that this emergent art practice has played an influential role in the shaping of locative technologies in their progression from new to everyday technologies. The research traces LM to its origins at the Karosta workshops, reviews the stated objectives of early practitioners and the ambitions of early projects, establishing it as a coherent art movement located within established traditions of technological art and of situated art practice. Based on a prescient analysis of the potential for ubiquitous networked location-awareness, LM developed an ambitious program aimed at repositioning emergent locative technologies as tools which enhance and augment space rather than surveil and control. Drawing on Krzysztof Ziarek\u27s treatment of avant-garde art and technology in The Force of Art , theories of technology drawn from Science and Technology Studies (STS) and software studies, the thesis builds an argument for the agency of Locative Media. LM is positioned as an interface layer which in connecting the user to the underlying functionality of locative technologies offers alternative interpretations, introduces new usage modes, and ultimately shifts the understanding and meaning of the technology. Building on the Situationist concept of the constructed situation, with reference to an ongoing body of practice, an experimental practice-based framework for LM art is advanced which accounts for its agency and, it is proposed, preserves this agency in a rapidly developing field
Imitation learning through games: theory, implementation and evaluation
Despite a history of games-based research, academia has generally regarded
commercial games as a distraction from the serious business of AI, rather than as an
opportunity to leverage this existing domain to the advancement of our knowledge.
Similarly, the computer game industry still relies on techniques that were developed
several decades ago, and has shown little interest in adopting more progressive
academic approaches. In recent times, however, these attitudes have begun to change;
under- and post-graduate games development courses are increasingly common,
while the industry itself is slowly but surely beginning to recognise the potential
offered by modern machine-learning approaches, though games which actually
implement said approaches on more than a token scale remain scarce.
One area which has not yet received much attention from either academia or industry
is imitation learning, which seeks to expedite the learning process by exploiting data
harvested from demonstrations of a given task. While substantial work has been done
in developing imitation techniques for humanoid robot movement, there has been
very little exploration of the challenges posed by interactive computer games. Given
that such games generally encode reasoning and decision-making behaviours which
are inherently more complex and potentially more interesting than limb motion data,
that they often provide inbuilt facilities for recording human play, that the generation
and collection of training samples is therefore far easier than in robotics, and that
many games have vast pre-existing libraries of these recorded demonstrations, it is
fair to say that computer games represent an extremely fertile domain for imitation
learning research.
In this thesis, we argue in favour of using modern, commercial computer games to
study, model and reproduce humanlike behaviour. We provide an overview of the
biological and robotic imitation literature as well as the current status of game AI, highlighting techniques which may be adapted for the purposes of game-based
imitation. We then proceed to describe our contributions to the field of imitation
learning itself, which encompass three distinct categories: theory, implementation
and evaluation.
We first describe the development of a fully-featured Java API - the Quake2 Agent
Simulation Environment (QASE) - designed to facilitate both research and education
in imitation and general machine-learning, using the game Quake 2 as a testbed. We
outline our motivation for developing QASE, discussing the shortcomings of existing
APIs and the steps which we have taken to circumvent them. We describe QASEâs
network layer, which acts as an interface between the local AI routines and the
Quake 2 server on which the game environment is maintained, before detailing the
APIâs agent architecture, which includes an interface to the MatLab programming
environment and the ability to parse and analyse full recordings of game sessions.
We conclude the chapter with a discussion of QASEâs adoption by numerous
universities as both an undergraduate teaching tool and research platform.
We then proceed to describe the various imitative mechanisms which we have
developed using QASE and its MatLab integration facilities. We first outline a
behaviour model based on a well-known psychological model of human planning.
Drawing upon previous research, we also identify a set of believability criteria -
elements of agent behaviour which are of particular importance in determining the
âhumannessâ of its in-game appearance. We then detail a reinforcement-learning
approach to imitating the human playerâs navigation of his environment, centred
upon his pursuit of items as strategic goals. In the subsequent section, we describe
the integration of this strategic system with a Bayesian mechanism for the imitation
of tactical and motion-modelling behaviours. Finally, we outline a model for the
imitation of reactive combat behaviours; specifically, weapon-selection and aiming. Experiments are presented in each case to demonstrate the imitative mechanismsâ
ability to accurately reproduce observed behaviours.
Finally, we criticise the lack of any existing methodology to formally gauge the
believability of game agents, and observe that the few previous attempts have been
extremely ad-hoc and informal. We therefore propose a generalised approach to such
testing; the Bot-Oriented Turing Test (BOTT). This takes the form of an anonymous
online questionnaire, an accompanying protocol to which examiners should adhere,
and the formulation of a believability index which numerically expresses each agentâs
humanness as indicated by its observers, weighted by their experience and the
accuracy with which the agents were identified. To both validate the survey approach
and to determine the efficacy of our imitative models, we present a series of
experiments which use the believability test to evaluate our own imitation agents
against both human players and traditional artificial bots. We demonstrate that our
imitation agents perform substantially better than even a highly-regarded rule-based
agent, and indeed approach the believability of actual human players.
Some suggestions for future directions in our research, as well as a broader
discussion of open questions, conclude this thesis
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