Avtonomno modeliranje robotskih akcij z odkrivanjem abstraktnih konceptov

The thesis presents a novel approach to autonomous learning of STRIPS-like robot action models that contain newly discovered abstract concepts. A learnt concept is abstract if it is not explicitly expressed in the learning data (eg. movability, “lower than”, weighted, etc.). We developed STRUDEL, a method that clusters robot actions into groups and then discovers abstract concepts as conditions that classify actions to their respective effects. The definitions of these concepts are used to build the final action model. Both the action model and the concept definitions can be expressed recursively. A new clustering algorithm ACES is used for clustering in STRUDEL. ACES clusters logical action descriptions by similarity of ther structure. Specifically it uses matching of graphs constructed from literals in the action effect descriptions as a distance measure. Performance of STRUDEL and ACES are demonstrated on several experimental domains. Finally, we describe HYPER/CA, a new inductive logic programming (ILP) system, developed as an upgrade of the HYPER system. HYPER/CA was developed to handle learning from noisy data in STRUDEL. We compare it with state-of-the-art ILP systems on several typical ILP learning tasks. Results on the test problems show that HYPER/CA, though quite slower then the algorithms used in the comparison, can attain similar accuracy while building smaller hypotheses

Constructing survival curves from censored data with machine learning methods

In the present thesis I introduce and evaluate a new machine learning method for estimating survival functions from survival analysis data. Firstly, I describe the field of survival analysis and the problems it deals with. I introduce and define the basic terms of survival analysis, like survival function and survival curve. I also define censored data, a speciallity of survival analysis data, and explain their importance and the learning problems they cause. As a reference method I describe the Kaplan-Meier estimator, a well-known statistical method for estimating survival curves, that serves as a conceptual basis for the new proposed method. I close the introduction with a short overview of the advances of machine learning in the field of survival analysis, concluding that so far there are no well established meachine learning methods in this field. I continue with an in depth description of the proposed method and it's potential advantages. To test the new method thoroughly I start with a series of tests on artificially generated data from a physics domain. The new method proves itself useful and can match the accuracy of the Kaplan-Meier estimator. I discuss the problem of nonmonotonic survival curve estimations, that can be obtained using the proposed method. All the tests are repeated on a set of real medical data describing the prognostic value of protein markers for survival of metastatic breast cancer patients. The results further confirm the proposed method as useful. In conclusion I present the possibilities of improving the proposed method and suggest other prospects of using machine learning techniques in survival analysis

Implementation of Cognitive Digital Twins in Connected and Agile Supply Networks—An Operational Model

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models

Implementation of cognitive digital twins in connected and agile supply networks—an operational model

Summarization: Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.Παρουσιάστηκε στο: Applied Science

Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring

Summarization: Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.Παρουσιάστηκε στο: Applied Science