Discovering Dialog Rules by means of an Evolutionary Approach

Designing the rules for the dialog management process is oneof the most resources-consuming tasks when developing a dialog system. Although statistical approaches to dialog management are becoming mainstream in research and industrial contexts, still many systems are being developed following the rule-based or hybrid paradigms. For example, when developers require deterministic system responses to keep total control on the decisions made by the system, or because the infrastructure employed is designed for rule-based systems using technologies currently used in commercial platforms. In this paper, we propose the use of evolutionary algorithms to automatically obtain the dialog rules that are implicit in a dialog corpus. Our proposal makes it possible to exploit the benefits of statistical approaches to build rule-based systems. Our proposal has been evaluated with a practical spoken dialog system, for which we have automatically obtained a set of fuzzy rules to successfully manage the dialog.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR project:https://menhir-project.eu

A data-driven approach to spoken dialog segmentation

In This Paper, We Present A Statistical Model For Spoken Dialog Segmentation That Decides The Current Phase Of The Dialog By Means Of An Automatic Classification Process. We Have Applied Our Proposal To Three Practical Conversational Systems Acting In Different Domains. The Results Of The Evaluation Show That Is Possible To Attain High Accuracy Rates In Dialog Segmentation When Using Different Sources Of Information To Represent The User Input. Our Results Indicate How The Module Proposed Can Also Improve Dialog Management By Selecting Better System Answers. The Statistical Model Developed With Human-Machine Dialog Corpora Has Been Applied In One Of Our Experiments To Human-Human Conversations And Provides A Good Baseline As Well As Insights In The Model Limitation

APPLICATION OF FORMAL SAFETY ASSESSMENT FOR DRY DOCKING EVOLUTION

This research has evaluated the rules, guidelines and regulations related to docking a ship in floating-graving yards. Historical failure data analysis is carried out to identify associated components, equipment and the area of defects related to ship docking evolution problems. The current status of ship docking evolution is reviewed and possible sources which cause accidents are recognised. The major problems identified in this research are associated with risk modelling under circumstances where high levels of uncertainty exist. Following the identification of research needs, this work has developed several analytical models for the application of Formal Safety Assessment (FSA). Such models are subsequently demonstrated by their corresponding case studies with regards to application of FSA for ship docking evolution. Firstly, in this research a generic floating-graving docking model is constructed for the purpose of hazard identification and risk estimation. The hazards include various scenarios, identified from literature reviewed as the major contributors to ship docking failures. Then risk estimation is carried out utilising fault tree (FT) – FSA where there is sufficient data. Secondly, with increased lack of data, risk estimation is carried out using FT-Bayesian network (BN) where interdepencies exists amongst identified hazards. This risk estimation method is validated with the appropriate case study identified. Thirdly, fuzzy rule base and evidential reasoning approaches are used for risk estimation in terms of three risk parameters to select the major causes of component failure that can lead to pontoon deck failure in a floating dock. Possible risk control options (RCOs) are introduced, based on their effectiveness, to select the best RCO for minimising the risks. Finally, a cost benefit assessment is conducted to select the best risk control option using BN, where selections are based on economic terms. The four subjective novel FSA application methodologies in ship docking evolution are constructed from existing theoretical techniques and applied to real situations where data collection is otherwise not possible. The construction of the novel methodologies and the case study applications are the major contribution to knowledge in this thesis. It is concluded that the methodologies proposed possess significant potential for the application of FSA for ship docking evolution based on the validations of their corresponding case studies, which may also be applied with domain specification knowledge tailored to facilitate FSA application in other shipping industry sectors

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Economic indicators used for EU projects, in other criteria of aggregation than national / regional

Economical and social indicators are created and published for national and regional dimensions. Nowadays, both local and territorial indicators are really able to define more adequate the stage of social and economical development and to illustrate the impact of European programs and projects in fields like: long lasting development, entrepreneurial development, scientific research development and strategies, education and learning resources, IT resources, dissemination of European culture etc. If in the first part, there is only quantitative information, offered by our National Institute of Statistics (NIS), in the following few examples of some useful economical and social indicators provide a dynamic vision in defining objectives, methods and implementation Thus the need for a quantitative framework of local and territorial indicators demands for an original statistical methodology.gross domestic product, indicators in macro, mezo and micro economics, weight of selected, factors, representative methodology

Economic indicators used for EU projects, in other criteria of aggregation than national / regional

Economical and social indicators are created and published for national and regional dimensions. Nowadays, both local and territorial indicators are really able to define more adequate the stage of social and economical development and to illustrate the impact of European programs and projects in fields like: long lasting development, entrepreneurial development, scientific research development and strategies, education and learning resources, IT resources, dissemination of European culture etc. If in the first part, there is only quantitative information, offered by our National Institute of Statistics (NIS), in the following few examples of some useful economical and social indicators provide a dynamic vision in defining objectives, methods and implementation Thus the need for a quantitative framework of local and territorial indicators demands for an original statistical methodology