Ryan J. Meuth

A proposition on memes and meta-memes in computing for higher-order learning

In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.

Adaptive task allocation for search area coverage

Many operations require an area search area function, including search-and-rescue, surveillance, hazard detection, structures or sites inspection and agricultural spraying. Furthermore, these area search applications often involve varying vehicle and environmental conditions. This paper explores the problem of optimizing the behavior of a swarm of heterogeneous robotic vehicles executing a search area coverage task. Each vehicle is equipped with a sensing apparatus and the swarm must collectively explore an occluded environment to achieve a required probability of detection for each location in the search area. The problem is further complicated with the introduction of dynamic vehicle and environmental properties making adaptability a necessary requirement in order to achieve a high level of mission assurance using unmanned vehicles. Novel methods for search space decomposition and task allocation are presented, with simulated and real-world results utilizing the Boeing Vehicle Swarm Technology Laboratory.

Memetic Mission Management

This paper presents novel area coverage algorithms that have been validated using Boeing VSTL hardware. Even though the multi-vehicle search area coverage problem is large and complex, several new memetic computing methods have been presented that decompose, allocate and optimize the exploration of a search area for multiple heterogeneous vehicles. These new methods were shown to have good performance and quality, and as they are defined in a general way, these methods are applicable to many other problem domains. The methods have been combined into a mission-planner architecture that is able to adaptively control the behavior of multiple vehicles with dynamic vehicle capabilities and environments for mission assurance. The topic of mission-planning architectures and optimization of swarms of autonomous vehicles is a young and exciting field with many opportunities for research. More computationally efficient methods for decomposition may be useful, as well as the application of next-generation meta-learning architectures for path planning. In addition to the existing collision avoidance, path de-confliction during planning can improve safety and efficiency.

Memetic Mission Management [Application Notes]

This paper presents novel area coverage algorithms that have been validated using Boeing VSTL hardware. Even though the multi-vehicle search area coverage problem is large and complex, several new memetic computing methods have been presented that decompose, allocate and optimize the exploration of a search area for multiple heterogeneous vehicles. These new methods were shown to have good performance and quality, and as they are defined in a general way, these methods are applicable to many other problem domains. The methods have been combined into a mission-planner architecture that is able to adaptively control the behavior of multiple vehicles with dynamic vehicle capabilities and environments for mission assurance. The topic of mission-planning architectures and optimization of swarms of autonomous vehicles is a young and exciting field with many opportunities for research. More computationally efficient methods for decomposition may be useful, as well as the application of next-generation meta-learning architectures for path planning. In addition to the existing collision avoidance, path de-confliction during planning can improve safety and efficiency.

LabRat ™ : Miniature robot for students, researchers, and hobbyists

LabRat^™ is an autonomous, self-contained mobile robot kit with batteries, motors, two bumper whisker sensors, and three infrared proximity sensors that double as channels for “Rat-to-Rat” communication. The vehicle determines its position with an optical sensor that detects movement in both lateral directions. The LabRat^™ design is completely open source, including software examples and libraries. LabRat^™ is designed to fit inside the body of a computer mouse and has applications in the classroom, the lab and the home. The device has been successfully used in an undergraduate robotics class.

An Agent-Based computational model of a self-organizing project management paradigm for research teams

We propose a new research organization management paradigm to increase throughput of projects by allowing researchers to choose their own projects through self-organization. Our methods draw upon the field of Agent-Based computational social science where Artificial Life and simulated societies have been used to study complex systems including economies and financial markets. Modeling the researchers as individual agents, we simulate our new management structure against a more traditional organization where the researchers are broken into departments based on their skills and assigned projects by management. Our results, measuring the amount of time it takes a research organization to serve a given number of contracts, show promise in the less hierarchical approach.