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- Genetic Algorithm; Cyclic Control; Hexapod; Greedy Selection; Gait; Evolutionary Robotics; Learning Control; Cyclic Genetic Algorithm (1)
- Genetic Algorithm; Cyclic Control; Quadruped; Gait; Evolutionary Robotics; Learning Control; Cyclic Genetic Algorithm (1)
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- Genetic; Cyclic Control; Quadruped; Gait; Evolutionary Robotics; Learning Control; Genetic Algorithm (1)
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Articles 1 - 17 of 17
Full-Text Articles in Computer Engineering
Evolving Expert Agent Parameters For Capture The Flag Agent In Xpilot, Gary Parker, Sarah Penrose
Evolving Expert Agent Parameters For Capture The Flag Agent In Xpilot, Gary Parker, Sarah Penrose
Computer Science Faculty Publications
Xpilot is an open source, 2d space combat game. Xpilot-AI allows a programmer to write scripts that control an agent playing a game of Xpilot. It provides a reasonable environment for testing learning systems for autonomous agents, both video game agents and robots. In previous work, a wide range of techniques have been used to develop controllers that are focused on the combat skills for an Xpilot agent. In this research, a Genetic Algorithm (GA) was used to evolve the parameters for an expert agent solving the more challenging problem of capture the flag.
Evolving Predator Control Programs For An Actual Hexapod Robot Predator, Gary Parker, Basar Gulcu
Evolving Predator Control Programs For An Actual Hexapod Robot Predator, Gary Parker, Basar Gulcu
Computer Science Faculty Publications
In the development of autonomous robots, control program learning systems are important since they allow the robots to adapt to changes in their surroundings. Evolutionary Computation (EC) is a method that is used widely in learning systems. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve a simulated predator robot to test the effectiveness of a learning system in the predator/prey problem. The learned control program performed search, chase, and capture behavior using 64 sensor states relative to the nearest obstacle and the target, a simulated prey robot. In this paper, we present …
Automation Techniques For Intelligent Environments - Prediction Of Building Activity Patterns Using A Cyclic Genetic Algorithm, Gary Parker, David T. Alpert
Automation Techniques For Intelligent Environments - Prediction Of Building Activity Patterns Using A Cyclic Genetic Algorithm, Gary Parker, David T. Alpert
Computer Science Faculty Publications
This work involves learning the use schedule of an academic building in order to intelligently control various aspects of the environment. Motion sensors are used to monitor and record the activity of each of the rooms in the building. After a basic preprocessing of the data, a Cyclic Genetic Algorithm (CGA) is used to pick out the patterns of use of the rooms. The CGA is seen as ideal for such a problem because of its ability to find repetitive cyclic patterns in the data. Our results show that a CGA has the ability to pick out such patterns and …
Investigating The Effects Of Learning Speeds On Xpilot Agent Evolution, Gary Parker, Phil Fritzsche
Investigating The Effects Of Learning Speeds On Xpilot Agent Evolution, Gary Parker, Phil Fritzsche
Computer Science Faculty Publications
In this paper we present a comparison of the effects of varying play speeds on a genetic algorithm in the space combat game Xpilot. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller for an Xpilot combat agent at varying frames per second to determine an optimal speed for learning. The controller is a rule-based system modified to work with a genetic algorithm that learns numeric parameters for the agent’s rule base. The goal of this research is to increase the quality and speed of standard learning algorithms in Xpilot as well as determine …
Fitness Biasing For The Box Pushing Task, Gary Parker, Jim O'Connor
Fitness Biasing For The Box Pushing Task, Gary Parker, Jim O'Connor
Computer Science Faculty Publications
Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the controller for a robot learning the box pushing task. The robot that was built for this task, was measured to create an accurate model. The model was used in simulation to test the effectiveness of Anytime Learning with Fitness Biasing for the box pushing task. This work is the first step in new research where an automated system to test the viability of Fitness …
Using Cyclic Genetic Algorithms To Learn Gaits For An Actual Quadruped Robot, Gary Parker, William T. Tarimo
Using Cyclic Genetic Algorithms To Learn Gaits For An Actual Quadruped Robot, Gary Parker, William T. Tarimo
Computer Science Faculty Publications
It is a difficult task to generate optimal walking gaits for mobile legged robots. Generating and coordinating an optimal gait involves continually repeating a series of actions in order to create a sustained movement. In this work, we present the use of a Cyclic Genetic Algorithm (CGA) to learn near optimal gaits for an actual quadruped servo-robot with three degrees of movement per leg. This robot was used to create a simulation model of the movement and states of the robot which included the robot’s unique features and capabilities. The CGA used this model to learn gaits that were optimized …
Quadruped Gait Learning Using Cyclic Genetic Algorithms, Gary Parker, William T. Tarimo, Michael Cantor
Quadruped Gait Learning Using Cyclic Genetic Algorithms, Gary Parker, William T. Tarimo, Michael Cantor
Computer Science Faculty Publications
Generating walking gaits for legged robots is a challenging task. Gait generation with proper leg coordination involves a series of actions that are continually repeated to create sustained movement. In this paper we present the use of a Cyclic Genetic Algorithm (CGA) to learn gaits for a quadruped servo robot with three degrees of movement per leg. An actual robot was used to generate a simulation model of the movement and states of the robot. The CGA used the robot's unique features and capabilities to develop gaits specific for that particular robot. Tests done in simulation show the success of …
Fitness Biasing For Evolving An Xpilot Combat Agent, Gary Parker, Phil Fritzsche
Fitness Biasing For Evolving An Xpilot Combat Agent, Gary Parker, Phil Fritzsche
Computer Science Faculty Publications
In this paper we present an application of Fitness Biasing, a type of Punctuated Anytime Learning, for learning autonomous agents in the space combat game Xpilot. Fitness Biasing was originally developed as a means of linking the model to the actual robot in evolutionary robotics. We use fitness biasing with a standard genetic algorithm to learn control programs for a video game agent in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller in the background while periodic checks in normal game play are used to compensate for errors produced by running the …
The Effects Of Using A Greedy Factor In Hexapod Gait Learning, Gary Parker, William T. Tarimo
The Effects Of Using A Greedy Factor In Hexapod Gait Learning, Gary Parker, William T. Tarimo
Computer Science Faculty Publications
Various selection schemes have been described for use in genetic algorithms. This paper investigates the effects of adding greediness to the standard roulette-wheel selection. The results of this study are tested on a Cyclic Genetic Algorithm (CGA) used for learning gaits for a hexapod servo-robot. The effectiveness of CGA in learning optimal gaits with selection based on roulette-wheel selection with and without greediness is compared. The results were analyzed based on fitness of the individual gaits, convergence time of the evolution process, and the fitness of the entire population evolved. Results demonstrate that selection with too much greediness tends to …
Comparison Of A Greedy Selection Operator To Tournament Selection And A Hill Climber, Lee Graham, John Borbone, Gary Parker
Comparison Of A Greedy Selection Operator To Tournament Selection And A Hill Climber, Lee Graham, John Borbone, Gary Parker
Computer Science Faculty Publications
A new deterministic greedy genetic algorithm selection operator with very high selection pressure, dubbed the "Jugate Adaptive Method" is examined. Its performance and behavior are compared to thoseof a canonical genetic algorithm with tournament selection, and a random-restarting next-ascent stochastic hill-climber. All three algorithms are tuned using parameter sweeps to optimize their success rates on five combinatorial optimization problems, tuning each algorithm for each problem independently. Results were negative in that the new method was outperformed in nearly all experiments. Experimental data show the hill climber to be the clear winner in four of five test problems.
Concurrently Evolving Sensor Morphology And Control For A Hexapod Robot, Gary Parker, Pramod J. Nathan
Concurrently Evolving Sensor Morphology And Control For A Hexapod Robot, Gary Parker, Pramod J. Nathan
Computer Science Faculty Publications
Evolving a robot’s sensor morphology along with its control program has the potential to significantly improve its effectiveness in completing the assigned task, plus accommodates the possibility of allowing it to adapt to significant changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. The evolution was done in simulation and the tests, which were also done in simulation, showed that effective sensor morphologies and control programs could be co-learned by the system. In this paper, we describe the …
Using Evolutionary Strategies For The Real-Time Learning Of Controllers For Autonomous Agents In Xpilot-Ai, Gary Parker, Michael H. Probst
Using Evolutionary Strategies For The Real-Time Learning Of Controllers For Autonomous Agents In Xpilot-Ai, Gary Parker, Michael H. Probst
Computer Science Faculty Publications
Real-time learning is the process of an artificial intelligence agent learning behavior(s) at the same pace as it operates in the real world. Video games tend to be an excellent locale for testing real-time learning agents, as the action happens at real speeds with a good visual feedback mechanism, coupled with the possibility of comparing human performance to that of the agent's. In addition, players want to be competing against a consistently challenging opponent. This paper is a discussion of a controller for an agent in the space combat game Xpilot and the evolution of said controller using two different …
Learning Area Coverage For A Self-Sufficient Colony Robot, Gary Parker, Richard Zbeda
Learning Area Coverage For A Self-Sufficient Colony Robot, Gary Parker, Richard Zbeda
Computer Science Faculty Publications
It is advantageous for colony robots to be autonomous and self-sufficient. This requires them to perform their duties while maintaining enough energy to operate. Previously, we reported the equipping of power storage for legged robots with high capacitance capacitors, the configuration of one of these robots to effectively use its power storage in a colony recharging system, and the learning of a control program that enabled the robot to navigate to a charging station in simulation. In this work, we report the learning of a control program that allowed the simulated robot to perform area coverage in a self-sufficient framework …
Response To Changes In Key Stimuli Through The Co-Evolution Of Sensor Morphology And Control, Gary Parker, Pramod J. Nathan
Response To Changes In Key Stimuli Through The Co-Evolution Of Sensor Morphology And Control, Gary Parker, Pramod J. Nathan
Computer Science Faculty Publications
Co-evolving a robot’s sensor morphology and control program increases the potential that it can effectively complete its tasks and provides a means for adapting to changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. Although three sensor stimuli were detectable by the system, it used only two due to the relative importance of these two stimuli in completing the task. In the research presented in this paper, we used the same system, but reduced the availability of a …
Basic Control For Four Rotor Autonomous Aerial Agent, Jonathan Mclean, Gary Parker, Newell Seal
Basic Control For Four Rotor Autonomous Aerial Agent, Jonathan Mclean, Gary Parker, Newell Seal
Computer Science Faculty Publications
Aerial robotics provides many practical applications in fields such as search and rescue and surveying. In order to advance the research in aerial robotics, an inexpensive test platform is required. Our four-rotor platform provides researchers with a inexpensive, fully scalable test platform for future studies. Its completely on-board processing removes the need for a virtual tether in the form of a radio transmitter, allowing for completely autonomous operation.
Evolving Predator Control Programs For A Hexapod Robot Pursuing A Prey, Gary Parker, Basar Gulcu
Evolving Predator Control Programs For A Hexapod Robot Pursuing A Prey, Gary Parker, Basar Gulcu
Computer Science Faculty Publications
Control program learning systems for autonomous robots are important to assist in their development and to allow them to adapt to changes in their capabilities and/or the environment. A common method for learning in robotics is Evolutionary Computation (EC) and a good problem to demonstrate the effectiveness of a learning system is the predator/prey problem. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve the control program for a predator robot with a simple sensor configuration of 4 binary sensors, which yielded 16 possible sensor states. In this paper, we present the use …
Learning Navigation For Recharging A Self-Sufficient Colony Robot, Gary Parker, Richard Zbeda
Learning Navigation For Recharging A Self-Sufficient Colony Robot, Gary Parker, Richard Zbeda
Computer Science Faculty Publications
It is advantageous for colony robots to be autonomous and self-sufficient. This requires them to perform their duties while maintaining enough energy to operate. Previously, we reported the equipping of power storage for legged robots with high capacitance capacitors, the configuration of one of these robots to effectively use its power storage in a colony recharging system, and the learning of a control program that enabled the robot to navigate to a charging station in simulation. In this work, we report the learning of a control program that allowed the simulated robot to perform area coverage in a self-sufficient framework …