Postprocessing Optimization of RRT* Using Machine Learning and Information Theory for Robotic Navigation

Machine learning and game theory are two important directions of AI. The former usually assumes data is independent of the models to be learned; the latter usually assumes agents are fully rational. In many modern Internet applications, like sponsored search and crowdsourcing, the two basic assumptions are violated and new challenges are posed to both machine learning and game theory. To better model and study such applications, we need to go beyond conventional machine learning and game theory (mechanism design), and adopt a new approach called mechanism learning with mechanism induced data. Specifically, we propose to learn a behavior model from data to describe how real agents play the complicated game, instead of making the full-rationality assumption. Then we propose to optimize the mechanism by using the learned behavior models to predict the future behaviors of agents in response to the new mechanism. Because the above process couples mechanism learning and behavior learning in a loop, new algorithms and theories are needed to perform the task and guarantee the asymptotical performance. As shown in this paper, there are many interesting research topics along this direction, many of which are still open problems, waiting for researchers in our community to deeply investigate.

A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts

The special focus of this paper is to discuss a likely intersection of machine learning, artificial intelligence (AI) technology, and game theory, pointing at the importance of this synthesis both in mathematics and engineering. As these domains develop various means of addressing decision-making problems become more and more sophisticated and can be used in different areas such as economics, security, and social sciences. We will also address selected game-theoretic issues including the concept of decision making in terms of Nash equilibria or in the distinction of games as being cooperative or non-cooperative and how they work in synergy with machine learning approaches bringing in reinforcements and deep learning to leverage forecasting and strategizing. The paper makes references to problem-oriented branches of studies such as autonomous systems or market strategies stressing the importance of the novel direction for further studies. Within the scope of machine learning and game theory the goal is to implement better complex models which utilize real world intricacies for enhancing decision making within a populated agent environment.

Cyberspace is emerging as a critical living environment, significantly influencing sustainable human development. Internet public opinion is a crucial aspect of cyberspace governance, serving as the most important form of expressing popular will. However, perceiving public opinion can be challenging due to its complex and elusive nature. In this paper, we propose a novel framework for perceiving popular will, managing public opinion, and influencing people’s behavior, based on machine learning and game theory approaches. Our framework leverages deep learning techniques to analyze public opinion, active learning methods to reduce costs, and game theory to make optimal management decisions. We verify the effectiveness of our framework using empirical data collected from Chinese provinces Y and G, and provide theoretical support by analyzing the interrelationship between public opinion, online public opinion, and people’s behavior. Our framework can be applied inexpensively to studies in other regions, thereby offering valuable insights into cyberspace governance and public opinion management.

Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory.

Machine Learning (ML) and Artificial Intelligence (AI), in general, are based on search algorithms. In this paper, we use paradoxically the same search techniques again to look for the general theory of machine learning itself. In other words, we search for a unifying machine learning theory. For this purpose, we turn out for a help to the general theory of computation, called $-calculus, that is based on meta-search and super-turing/hypercomputational models. We hope that in such a way, we can unify machine learning and this should be useful in the development of new methods, algorithms, embedded devices and computer programs in the future. Firstly, we overview main machine learning areas as our training examples, and by applying our background knowledge we hand pick-up a hypothetical reasonable theory of ML. Next, we justify that this is a good generalization of ML. The open research question remains whether by applying various ML techniques we can induce automatically the optimal ML theory from the hypothesis space of possible theories. Thus, hopefully, the follow-up paper would be titled “In search of the optimal machine learning theory”.KeywordsMachine learningAISearchUnifying theory$-Calculuskω-optimization

In this paper, we explore the possibility that machine learning approaches to natural-language processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.

Support vector machine (SVM), as a machine learning method based on data, is a theory of machine learning's law based on data in a limited number of data. We introduce the framework of multi-node cooperative interference detection and recognition algorithm, supply SVM to this field and realize cooperation among each node by transmitting interference signal's feature parameters. Based on simulation result, this method makes it possible to improve system's detection and recognition performance in the case of reducing cooperative detection and recognition's cost.

BackgroundFollowing visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.MethodsWe demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples.We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment.ResultsThe trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble.ConclusionsWe use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.

The provided document is a research paper on 3D animation simulation based on machine learning, focusing on the application of machine learning in three-dimensional human animation technology. The paper discusses the importance of virtual reality technology based on computer three-dimensional animation in various fields such as film production, game development, sports simulation, and simulated training. The authors emphasize the significance of three-dimensional human animation technology in portraying subtle changes in human movement and simulating human emotions contained in movement. The paper also explores the application of machine learning methods in processing human body movement data and synthesizing new realistic movement sequences. Various machine learning theories and methods, including regression, function approximation, dimension reduction, classification, clustering, and decision-making, are discussed in relation to their application in three-dimensional human animation technology. The document also reviews the research status of human body movement synthesis technology at home and abroad, highlighting the contributions of various researchers in the field. The authors conclude that machine learning methods have unique advantages in processing human body movement data and can significantly improve the utilization rate of movement capture samples, thereby reducing the cost and time cycle of three-dimensional human animation production..

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density (η), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry.

This article examines what the rise in machine learning (ML) systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems have some capacity for agency and for engaging in forms of collective machine behaviour, in which ML systems interact with other machines. However, ML-based collective machine behaviour is irreducible to human decision-making and thereby challenges established sociological notions of financial markets (including that of embeddedness). I argue that such behaviour can nonetheless be analysed through an adaptation of sociological theories of interaction and collective behaviour.

Deep learning has transformed various sectors, introducing new applications and opportunities. However, the underlying physical mechanisms or mathematical theories responsible for its success remain fundamental questions. This inquiry explores the connection between deep learning algorithms and established scientific principles with the aim of uncovering the mysteries behind their remarkable capabilities. By bridging the gap between deep learning, neural networks, and scientific knowledge, we can develop robust and interpretable models with enhanced capabilities. This ongoing research involves collaboration across diverse fields to unveil the hidden intricacies of deep learning algorithms and their links to physical phenomena. The ultimate goal is to contribute to the potential of the journal by examining the theory, design and application of neural networks and machine learning, focusing on the effectiveness of neural network paradigms for deep learning and their connections to physical events. By examining the intersection of deep learning, neural networks, and physical phenomena, we aim to advance our understanding and use of neural networks and machine learning in many areas of space, pushing the boundaries of excellence in science and engineering

The concrete mix design is a crucial step in ensuring the quality of concrete used in construction projects. Traditional mix design methods rely on trial and error, which can be time-consuming and escalate construction costs. In recent years, machine learning technology has been developed to predict concrete properties and optimize mix designs for high-quality concrete. This study aims to explore the application of machine learning in high-quality concrete mix design, considering theoretical conditions, methods, and related research. The focus of this research is to test the use of machine learning techniques in predicting concrete properties and optimizing mix designs for high-quality concrete. The theory and concepts of machine learning will be applied to concrete mix design datasets and relevant properties. The methods employed will include data pre-processing, feature selection, and model training and evaluation. The performance of the machine learning model will be compared with traditional mix design methods to determine its effectiveness. Furthermore, the results and benefits of this study will demonstrate the potential advantages of using machine learning in determining high-performance concrete mix designs. By accurately predicting concrete properties and optimizing mix designs, construction projects can be completed more efficiently and with higher quality. This technology also has the potential to reduce costs associated with trial and error methods and minimize the environmental impact of concrete production. The success of this study will pave the way for further research and development in the field of machine learning for high-performance concrete mix designs.

Autonomous and automated driving development has been making quick progress within the last years and many large automotive companies have announced initiatives to offer self-driving cars soon, some as early as 2020. During the first decades, these automated cars will have to share the road with human drivers. A way to increase safety and comfort for these automated vehicles and improve current advanced driver-assistance systems (ADAS), is to take the expected future behavior of other traffic participants into account. This increases the time a vehicle can use to react to changes in its environment and therefore allows for more comfortable or safe actions to be chosen. Relevant research areas to achieve this goal include computer vision, machine learning, situational awareness, decision making and many more. In this thesis, we contribute and evaluate new ideas for some of the challenges in the toolchain required for predicting the behavior of other traffic participants and deciding what to do with these. We used supervised machine learning techniques for both reactive predictions (short-term) and motivation-based predictions (long-term) and contribute to the question how the data used for machine learning can be labeled, comparing manual to semi-automated to automated approaches. This is especially interesting when subjective data is involved, e.g. at which point in time a motivation arose in a driver, even though he did not act on it yet. How to use and what to actually do with predictions in vehicles equipped with ADAS or automated vehicles is the next contribution. We evaluated many heuristics and a new, complex decision model based on decision theory. As use case, an adaptive cruise control system was enhanced by our prototype implementation of predictors and decision algorithms. We used an automated prototype research car in a case study and compared the effects on safety and comfort using objective data evaluation and subjective feedback from the study participants. We are able to show that even simple prediction and decision algorithms are able to improve the current status quo considerably and that the more advanced models work even better, but at the cost of substantial complexity increases.