Hybridizing Novelty Search for Transfer Learning

This study investigates the impact of genotypic and behavioral diversity maintenance methods on controller evolution in multi-robot (RoboCup keep-away soccer) tasks. The focus is to examine the impact of these methods on the transfer learning of behaviors, first evolved in a source task before being transferred for further evolution in different but related target tasks. The goal is to ascertain an appropriate controller design (NE: NeuroEvolution) method for facilitating improved effectiveness given policy transfer between source and target tasks. Effectiveness is defined as the average task performance of transferred behaviors. The study comparatively tests and evaluates the efficacy of coupling policy transfer with several NE variants. Results indicate a hybrid of behavioral diversity maintenance and objective-based search yields significantly improved effectiveness for evolved behaviors across increasingly complex target tasks. Results also highlight the efficacy of coupling policy transfer with the hybrid of behavioral diversity maintenance and objective based search in order to address bootstrapping and deception problems endemic to complex tasks

Rethinking Invention: Cognition and the Economics of Technological Creativity

Economists have typically not devoted much attention to the act of invention. This paper attempts to redress this situation by exploring a form of cognition, analogical transfer, which is thought by some researchers to lie at the heart of successful creativity. An analogical transfer is said to have occurred when information and experiences from one known situation is retrieved and utilized in the search for the solution to an entirely different situation. This paper shows how such analogical thought can give rise to a theoretical framework, in which disparate factors pertaining to technological creativity can be pieced together to yield an explanation of the level of inventive output experienced.invention, technological change, technological creativity, problem solving, learning.

Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm

This thesis describes a machine learning method for automatically imitating a particular person\u27s facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen\u27s Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart\u27s Particle Swarm Optimization algorithm (PSO) and Glover\u27s Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

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Policy Transfer Methods in RoboCup Keep-Away

This study investigates multi-agent policy transfer coupled with behavior adaptation by objective and non-objective search variants of HyperNEAT in RoboCup keep-away. For comparison, evolved behaviors were compared to those adapted by RL methods: SARSA and Q-Learning, coupled with policy transfer. Keepaway was selected as it is an established multi-agent experimental platform. Similarly, the SARSA and Q-Learning methods were selected as both have been demonstrated for boosting behavior quality with policy transfer. Keep-away behaviors were gauged in terms of effectiveness and efficiency. Effectiveness was average task performance given policy transfer, where task performance was average ball control time by the keeper team. Efficiency was average number of evaluations taken to reach a minimum task performance threshold given policy transfer

Novel hybrid generative adversarial network for synthesizing image from sketch

In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement

Human Agency and Educational Research: A New Problem in Activity Theory

In this paper, I address the reconceptualization of human agency that can shift to an analysis of both distributed and multiple agency in networked learning activities. As human activity becomes increasingly dialogical, boundary-crossing, networked, hybrid, and weekly bounded forms of work and organizations, the new generation of activity theory invites us to focus educational research efforts on the evocative and supportive new forms of agency to design and implement new patterns and forms of collaborative relationships of multiple activity systems. After a conceptual overview, this paper will analyze findings from a case study on an inter-institutional, collaborative after-school learning activity for children called New School promoted by the Center for Human Activity Theory at Kansai University in Osaka. In conclusion, this paper will propose that evoking and supporting new distributed and multiple forms of critical design agency for networked educational work and organizations among different actors involved in and affected by educational practices must offer a lifeline to educational research as an intervention to break away from something old (e.g., institutional boundaries of traditional school learning isolated from society) and move toward something else (e.g., advanced networks of learning across boundaries). Such agency might include the will and courage to create school innovations so that schools can become collaborative change agents