Learning and input selection of human strategy in controlling a single wheel robot.

by Wai-Kuen Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Robot Concept --- p.1Chapter 1.2 --- Motivations --- p.3Chapter 1.3 --- Related Work --- p.5Chapter 1.4 --- Overview --- p.6Chapter 2 --- Single Wheel Robot --- p.8Chapter 2.1 --- Mathematical Model --- p.8Chapter 2.1.1 --- Coordinate Frame --- p.9Chapter 2.1.2 --- Equations of Motion --- p.10Chapter 2.1.3 --- Model Simplification --- p.12Chapter 2.2 --- Hardware Descriptions --- p.13Chapter 2.2.1 --- Actuators --- p.14Chapter 2.2.2 --- Sensors --- p.14Chapter 2.2.3 --- Communication Subsystem --- p.15Chapter 2.2.4 --- Computer Subsystem --- p.16Chapter 2.3 --- Software Descriptions --- p.16Chapter 2.3.1 --- Operating System --- p.17Chapter 2.3.2 --- Software Architecture --- p.18Chapter 3 --- Human-based Control --- p.21Chapter 3.1 --- Why Human-based Control --- p.21Chapter 3.2 --- Modeling Human Control Strategy --- p.22Chapter 3.2.1 --- Human Control Strategy --- p.22Chapter 3.2.2 --- Neural Network for Modeling --- p.23Chapter 3.2.3 --- Learning Procedure --- p.24Chapter 3.3 --- Task Descriptions --- p.28Chapter 3.4 --- Modeling HCS in Controlling the Robot --- p.29Chapter 3.4.1 --- Model Input and Output --- p.30Chapter 3.4.2 --- Human-based Controller --- p.31Chapter 3.5 --- Result and Discussion --- p.31Chapter 4 --- Input Selection --- p.38Chapter 4.1 --- Why Input Selection --- p.38Chapter 4.2 --- Model Validation --- p.39Chapter 4.2.1 --- Why Model Validation --- p.39Chapter 4.2.2 --- Root Mean Square Error Measure --- p.40Chapter 4.3 --- Experimental Setup --- p.40Chapter 4.4 --- Model-based Method --- p.41Chapter 4.4.1 --- Problem Definition --- p.41Chapter 4.4.2 --- Input Representation --- p.43Chapter 4.4.3 --- Sensitivity Analysis --- p.44Chapter 4.4.4 --- Experimental Result --- p.47Chapter 4.5 --- Model-free Method --- p.51Chapter 4.5.1 --- Problems Definition --- p.51Chapter 4.5.2 --- Factor Analysis --- p.54Chapter 4.5.3 --- Experimental Result --- p.63Chapter 4.6 --- Model-based Method versus Model-free Method --- p.66Chapter 5 --- Conclusion and Future Work --- p.71Chapter 5.1 --- Contributions --- p.71Chapter 5.2 --- Future Work --- p.72Chapter Appendix A --- Dynamic Model of the Robot --- p.74Chapter A.1 --- Kinematic Constraints: Holonomic and Nonholonomic --- p.74Chapter A.1.1 --- Coordinate Frame --- p.74Chapter A.2 --- Robot Dynamics --- p.76Chapter A.2.1 --- Single Wheel --- p.77Chapter A.2.2 --- Internal Mechanism and Spinning Flywheel --- p.77Chapter A.2.3 --- Lagrangians of the System --- p.78Chapter Appendix B --- Similarity Measure --- p.80Bibliography --- p.8

Issues in designing learning by teaching systems

Abstract: Learning by teaching systems are a relatively recent approach to designing Intelligent Learning Environments that place learners in the role of tutors. These systems are based on the practice of peer tutoring where students take on defined roles of tutor and tutee. An architecture for learning by teaching systems is described that does not require the domain model of an Intelligent Tutoring System. However a mutual communication language is needed and is defined by a conceptual syntax that delimits the domain content of the dialogue. An example learning by teaching system is described for the domain of qualitative economics. The construction and testing of this system inform a discussion of the major design issues involved: the nature of the learnt model, the form of the conceptual syntax, the control of the interaction and the possible introduction of domain knowledge. 1

Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization

Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and fuzzy. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, according to its dimensions. The ENG model analysis shows that the net evolves as a scale-free graph, and justifies in a deeply physical sense- the term gas here used.Comment: 16 pages, 8 figure

Self-Supervised and Controlled Multi-Document Opinion Summarization

We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries We also provide an ablation study, which shows the importance of the control setup in controlling hallucinations and achieve high sentiment and topic alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi