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CONTROLLING RANDOMNESS: USING PROCEDURAL GENERATION TO INFLUENCE PLAYER UNCERTAINTY IN VIDEO GAMES
Title
CONTROLLING RANDOMNESS: USING PROCEDURAL GENERATION TO INFLUENCE PLAYER UNCERTAINTY IN VIDEO GAMES.
Creator
Fort, Travis, McDaniel, Rudy, University of Central Florida
Abstract / Description
As video games increase in complexity and length, the use of automatic, or procedural, content generation has become a popular way to reduce the stress on game designers. However, the usage of procedural generation has certain consequences; in many instances, what the computer generates is uncertain to the designer. The intent of this thesis is to demonstrate how procedural generation can be used to intentionally affect the embedded randomness of a game system, enabling game designers to...
Show moreAs video games increase in complexity and length, the use of automatic, or procedural, content generation has become a popular way to reduce the stress on game designers. However, the usage of procedural generation has certain consequences; in many instances, what the computer generates is uncertain to the designer. The intent of this thesis is to demonstrate how procedural generation can be used to intentionally affect the embedded randomness of a game system, enabling game designers to influence the level of uncertainty a player experiences in a nuanced way. This control affords game designers direct control over complex problems like dynamic difficulty adjustment, pacing, or accessibility. Game design will be examined from the perspective of uncertainty and how procedural generation can be used to intentionally add or reduce uncertainty. Various procedural generation techniques will be discussed alongside the types of uncertainty, using case studies to demonstrate the principles in action.
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Date Issued
2015
Identifier
CFH0004772, ucf:45386
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH0004772
MULTIAGENT LEARNING THROUGH INDIRECT ENCODING
Title
MULTIAGENT LEARNING THROUGH INDIRECT ENCODING.
Creator
D'Ambrosio, David, Stanley, Kenneth, University of Central Florida
Abstract / Description
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby...
Show moreDesigning a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other.In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent's location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(lamba) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding.
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Date Issued
2011
Identifier
CFE0003661, ucf:48812
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003661
On RADAR DECEPTION, AS MOTIVATION FOR CONTROL OF CONSTRAINED SYSTEMS
Title
On RADAR DECEPTION, AS MOTIVATION FOR CONTROL OF CONSTRAINED SYSTEMS.
Creator
Hajieghrary, Hadi, Jayasuriya, Suhada, Xu, Yunjun, Das, Tuhin, University of Central Florida
Abstract / Description
This thesis studies the control algorithms used by a team of ECAVs (Electronic Combat Air Vehicle) to deceive a network of radars to detect a phantom track. Each ECAV has the electronic capability of intercepting the radar waves, and introducing an appropriate time delay before transmitting it back, and deceiving the radar into seeing a spurious target beyond its actual position. On the other hand, to avoid the errors and increase the reliability, have a complete coverage in various...
Show moreThis thesis studies the control algorithms used by a team of ECAVs (Electronic Combat Air Vehicle) to deceive a network of radars to detect a phantom track. Each ECAV has the electronic capability of intercepting the radar waves, and introducing an appropriate time delay before transmitting it back, and deceiving the radar into seeing a spurious target beyond its actual position. On the other hand, to avoid the errors and increase the reliability, have a complete coverage in various atmosphere conditions, and confronting the effort of the belligerent intruders to delude the sentinel and enter the area usually a network of radars are deployed to guard the region. However, a team of cooperating ECAVs could exploit this arrangement and plans their trajectories in a way all the radars in the network vouch for seeing a single and coherent spurious track of a phantom. Since each station in the network confirms the other, the phantom track is considered valid. This problem serves as a motivating example in trajectory planning for the multi-agent system in highly constrained operation conditions. The given control command to each agent should be a viable one in the agent limited capabilities, and also drives it in a cumulative action to keep the formation.In this thesis, three different approaches to devise a trajectory for each agent is studied, and the difficulties for deploying each one are addressed. In the first one, a command center has all information about the state of the agents, and in every step decides about the control each agent should apply. This method is very effective and robust, but needs a reliable communication. In the second method, each agent decides on its own control, and the members of the group just communicate and agree on the range of control they like to apply on the phantom. Although in this method much less data needs to communicate between the agents, it is very sensitive to the disturbances and miscalculations, and could be easily fell apart or come to a state with no feasible solution to continue. In the third method a differential geometric approach to the problem is studied. This method has a very strong backbone, and minimizes the communication needed to a binary one. However, less data provided to the agents about the system, more sensitive and infirm the system is when it faced with imperfectionalities. In this thesis, an object oriented program is developed in the Matlab software area to simulate all these three control strategies in a scalable fashion. Object oriented programming is a naturally suitable method to simulate a multi-agent system. It gives the flexibility to make the code more close to a real scenario with defining each agent as a separated and independent identity. The main objective is to understand the nature of the constrained dynamic problems, and examine various solutions in different situations. Using the flexibility of this code, we could simulate several scenarios, and incorporate various conditions on the system. Also, we could have a close look at each agent to observe its behavior in these situations. In this way we will gain a good insight of the system which could be used in designing of the agents for specific missions.
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Date Issued
2013
Identifier
CFE0004857, ucf:49683
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004857
AUTOMATIC GENERATION OF SUPPLY CHAIN SIMULATION MODELS FROM SCOR BASED ONTOLOGIES
Title
AUTOMATIC GENERATION OF SUPPLY CHAIN SIMULATION MODELS FROM SCOR BASED ONTOLOGIES.
Creator
Cope, Dayana, Sepulveda, Jose, University of Central Florida
Abstract / Description
In today's economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed...
Show moreIn today's economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed supply chain environments. The integrated methodology allows for informed decision making in a fast, sharable and easy to use format. The methodology was implemented by developing a stand alone tool that allows users to define a supply chain simulation model using SCOR based ontologies. The ontology includes the supply chain knowledge and the knowledge required to build a simulation model of the supply chain system. A simulation model is generated automatically from the ontology to provide the flexibility to model at various levels of details changing the model structure on the fly. The methodology implementation is demonstrated and evaluated through a retail oriented case study. When comparing the implementation using the developed methodology vs. a "traditional" simulation methodology approach, a significant reduction in definition and execution time was observed.
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Date Issued
2008
Identifier
CFE0002009, ucf:47625
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002009
Modeling and Simulation of All-electric Aircraft Power Generation and Actuation
Title
Modeling and Simulation of All-electric Aircraft Power Generation and Actuation.
Creator
Woodburn, David, Wu, Xinzhang, Batarseh, Issa, Georgiopoulos, Michael, Haralambous, Michael, Chow, Louis, University of Central Florida
Abstract / Description
Modern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by...
Show moreModern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by predicting the dynamic heat generated and the power flow in the EMA. Chapter 2 of this dissertation describes the nonlinear, lumped-element, integrated modeling of a permanent magnet (PM) motor used in an EMA. This model is capable of representing transient dynamics of an EMA, mechanically, electrically, and thermally.Inductance is a primary parameter that links the electrical and mechanical domains and, therefore, is of critical importance to the modeling of the whole EMA. In the dynamic mode of operation of an EMA, the inductances are quite nonlinear. Chapter 3 details the careful analysis of the inductances from finite element software and the mathematical modeling of these inductances for use in the overall EMA model.Chapter 4 covers the design and verification of a nonlinear, transient simulation model of a two-step synchronous generator with three-phase rectifiers. Simulation results are shown.
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Date Issued
2013
Identifier
CFE0005074, ucf:49975
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005074