Sequential Symbolic Regression with Genetic Programming

This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Automated NDT inspection for large and complex geometries of composite materials

Large components with complex geometries, made of composite materials, have become very common in modern structures. To cope with future demand projections, it is necessary to overcome the current non-destructive testing (NDT) bottlenecks encountered during the inspection phase of manufacture. This thesis investigates several aspects of the introduction of automation within the inspection process of complex parts. The use of six-axis robots for product inspection and non-destructive testing systems is the central investigation of this thesis. The challenges embraced by the research include the development of a novel controlling approach for robotic manipulators and of novel path-planning strategies. The integration of robot manipulators and NDT data acquisition instruments is optimized. An effective and reliable way to encode the NDT data through the interpolated robot feedback positions is implemented. The viability of the new external control method is evaluated experimentally. The observed maximum position and orientation errors are respectively within 2mm and within 1 degree, over an operating envelope of 3m³. A new software toolbox (RoboNDT), aimed at NDT technicians, has been developed during this work. RoboNDT is intended to transform the robot path-planning problem into an easy step of the inspection process. The software incorporates the novel path-planning algorithms developed during this research and is shaped to overcome practical limitations of current OLP software. The software has been experimentally validated using scans on real high value aerospace components. RoboNDT delivers tool-path errors that are lower than the errors given by commercial off-line path-planning software. For example the variability of the standoff is within 10 mm for the tool-paths created with the commercial software and within 4.5 mm for the RoboNDT tool-paths, over a scanned area of 1.6m². The output of this research was used to support a 3-year industrial project, called IntACom and led by TWI on behalf of major aerospace sponsors. The result is a demonstrator system, currently in use at TWI Technology Centre, which is capable of inspecting complex geometries with high throughput. The IntACom system can scan real components 2.8 times faster than traditional 3-DoF scanners deploying phased-array inspection and 6.7 times faster than commercial gantry systems deploying traditional single-element inspection.Large components with complex geometries, made of composite materials, have become very common in modern structures. To cope with future demand projections, it is necessary to overcome the current non-destructive testing (NDT) bottlenecks encountered during the inspection phase of manufacture. This thesis investigates several aspects of the introduction of automation within the inspection process of complex parts. The use of six-axis robots for product inspection and non-destructive testing systems is the central investigation of this thesis. The challenges embraced by the research include the development of a novel controlling approach for robotic manipulators and of novel path-planning strategies. The integration of robot manipulators and NDT data acquisition instruments is optimized. An effective and reliable way to encode the NDT data through the interpolated robot feedback positions is implemented. The viability of the new external control method is evaluated experimentally. The observed maximum position and orientation errors are respectively within 2mm and within 1 degree, over an operating envelope of 3m³. A new software toolbox (RoboNDT), aimed at NDT technicians, has been developed during this work. RoboNDT is intended to transform the robot path-planning problem into an easy step of the inspection process. The software incorporates the novel path-planning algorithms developed during this research and is shaped to overcome practical limitations of current OLP software. The software has been experimentally validated using scans on real high value aerospace components. RoboNDT delivers tool-path errors that are lower than the errors given by commercial off-line path-planning software. For example the variability of the standoff is within 10 mm for the tool-paths created with the commercial software and within 4.5 mm for the RoboNDT tool-paths, over a scanned area of 1.6m². The output of this research was used to support a 3-year industrial project, called IntACom and led by TWI on behalf of major aerospace sponsors. The result is a demonstrator system, currently in use at TWI Technology Centre, which is capable of inspecting complex geometries with high throughput. The IntACom system can scan real components 2.8 times faster than traditional 3-DoF scanners deploying phased-array inspection and 6.7 times faster than commercial gantry systems deploying traditional single-element inspection

LED wristbands for cell-based crowd evacuation: an adaptive exit-choice guidance system architecture

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system's expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems.Ministerio de Economía, Industria y Competitivida

Error management in ATLAS TDAQ : an intelligent systems approach

This thesis is concerned with the use of intelligent system techniques (IST) within a large distributed software system, specifically the ATLAS TDAQ system which has been developed and is currently in use at the European Laboratory for Particle Physics(CERN). The overall aim is to investigate and evaluate a range of ITS techniques in order to improve the error management system (EMS) currently used within the TDAQ system via error detection and classification. The thesis work will provide a reference for future research and development of such methods in the TDAQ system. The thesis begins by describing the TDAQ system and the existing EMS, with a focus on the underlying expert system approach, in order to identify areas where improvements can be made using IST techniques. It then discusses measures of evaluating error detection and classification techniques and the factors specific to the TDAQ system. Error conditions are then simulated in a controlled manner using an experimental setup and datasets were gathered from two different sources. Analysis and processing of the datasets using statistical and ITS techniques shows that clusters exists in the data corresponding to the different simulated errors. Different ITS techniques are applied to the gathered datasets in order to realise an error detection model. These techniques include Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and a comparison of the respective advantages and disadvantages is made. The principle conclusions from this work are that IST can be successfully used to detect errors in the ATLAS TDAQ system and thus can provide a tool to improve the overall error management system. It is of particular importance that the IST can be used without having a detailed knowledge of the system, as the ATLAS TDAQ is too complex for a single person to have complete understanding of. The results of this research will benefit researchers developing and evaluating IST techniques in similar large scale distributed systems

Automated Assessment on Clinical Drawing Test for Diagnosis and Analysis of Parkinson’s Disease using Evolutionary Algorithm

The deadly Parkinson’s disease is always a focused area in medical research, even as today there is no cure for such disease. Patient does not have any vivid symptoms until the late stage of the disease when the condition has been threatening patient’s life already. Current diagnosing approach on Parkinson’s disease at early stage often includes test sets in questionnaire form with verdicts from clinical examiners. Such conventional approach has subjective assessment standard as well as verdicts with examiners’ personal judgement, which inevitably may contain human errors. In addition, such assessment method often takes several days for one patient, making it very inefficient. This research project proposed an objective approach by using machine learning to assess one of the tests from the questionnaire – the clinical drawing test, which can classify patients’ drawing performance automatically and is very efficient. This approach also allows the algorithm to catch the smallest detail in the drawing whilst minimise human errors from human-orientated assessments. The result proves that given the same assessment, the algorithm tends to perform better than human verdicts in terms of distinguishing patients in different stages. What’s more, the algorithm proposed in this thesis has an overall advantage over some conventional algorithm models, which not only optimised the computational effort, but also can allow clinical experts to understand how the figure data are used by the algorithm and assist them in further research in Parkinson’s disease