Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Anytime learning (1)
- Evolutionary robotics (1)
- 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)
- Genetic algorithm (1)
-
- Genetic; Cyclic Control; Quadruped; Gait; Evolutionary Robotics; Learning Control; Genetic Algorithm (1)
- Greedy selection; hill-climbing; genetic algorithms; combinatorial optimization (1)
- Learning control (1)
- Xpilot; Xpilot-AI; Anytime Learning; Punctuated Anytime Learning; Fitness Biasing; Genetic Algorithm; Real-time Learning; Video Game Learning; On-line Learning (1)
- Xpilot; Xpilot-AI; Punctuated Anytime Learning; Fitness Biasing; Genetic Algorithm; Real-time Learning; Video Game Learning; On-line learning; Autonomous system control (1)
Articles 1 - 7 of 7
Full-Text Articles in Computer Engineering
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.