Game Solving with Online Fine-Tuning

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter. AlphaZero-based heuristics can be highly inaccurate when evaluating these out-of-distribution positions, which occur throughout the entire search. To address this issue, this paper investigates applying online fine-tuning while searching and proposes two methods to learn tailor-designed heuristics for game solving. Our experiments show that using online fine-tuning can solve a series of challenging 7x7 Killall-Go problems, using only 23.54% of computation time compared to the baseline without online fine-tuning. Results suggest that the savings scale with problem size. Our method can further be extended to any tree search algorithm for problem solving. Our code is available at https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Automating Industrial Event Stream Analytics: Methods, Models, and Tools

Industrial event streams are an important cornerstone of Industrial Internet of Things (IIoT) applications. For instance, in the manufacturing domain, such streams are typically produced by distributed industrial assets at high frequency on the shop floor. To add business value and extract the full potential of the data (e.g. through predictive quality assessment or maintenance), industrial event stream analytics is an essential building block. One major challenge is the distribution of required technical and domain knowledge across several roles, which makes the realization of analytics projects time-consuming and error-prone. For instance, accessing industrial data sources requires a high level of technical skills due to a large heterogeneity of protocols and formats. To reduce the technical overhead of current approaches, several problems must be addressed. The goal is to enable so-called "citizen technologists" to evaluate event streams through a self-service approach. This requires new methods and models that cover the entire data analytics cycle. In this thesis, the research question is answered, how citizen technologists can be facilitated to independently perform industrial event stream analytics. The first step is to investigate how the technical complexity of modeling and connecting industrial data sources can be reduced. Subsequently, it is analyzed how the event streams can be automatically adapted (directly at the edge), to meet the requirements of data consumers and the infrastructure. Finally, this thesis examines how machine learning models for industrial event streams can be trained in an automated way to evaluate previously integrated data. The main research contributions of this work are: 1. A semantics-based adapter model to describe industrial data sources and to automatically generate adapter instances on edge nodes. 2. An extension for publish-subscribe systems that dynamically reduces event streams while considering requirements of downstream algorithms. 3. A novel AutoML approach to enable citizen data scientists to train and deploy supervised ML models for industrial event streams. The developed approaches are fully implemented in various high-quality software artifacts. These have been integrated into a large open-source project, which enables rapid adoption of the novel concepts into real-world environments. For the evaluation, two user studies to investigate the usability, as well as performance and accuracy tests of the individual components were performed

Social media adoption by microbusinesses

The social media implementation process (initiation, adoption, adaptation, acceptance, use and incorporation) is examined in correlation with the five factors (individual, organisational, technological, environmental and performance). Numerous existing theories from the innovation, technology adoption and performance measurement literature are used to derive probable relation between the implementation process and the five factors. Such expansive scope and comprehensive theory development has been articulated but never attempted. To manage the large scope, microbusinesses are selected purposefully due to their limited business processes. The research design reflects the need for relevance by using Lewin’s action research (traditional social change model) as the primary method augmented by participant observation (physical and online). Data collection uses a mix of unstructured, semi-structured and structured interviews assisted by structured observation. Data analysis uses a set of routines, such as tabulation, categorisation, abstraction and verification, involving prediction and testing. The research finds that a collaborative process to address concerns, along with quick start and self-training, helped to adopt social media. Participants needed to focus on concrete experience, work-place learning and personal knowledge for learning to use social media. Usefulness arising from improved communication, fitness and medium richness was the dominant indicator for acceptance and use. Continued use relied on satisfaction and habit of the user. Individual characteristics and personality factors both seemed to be a poor indicator of adoption with weak links towards extroversion. Microbusinesses suffered primarily from context and mental mode related challenges for social media use. Type of business, such as service shops, had a greater probability of success. Social media positively affected relationship marketing in terms of service quality. Business activity associated with specialisation seemed to perform poorly with social media. Finally, performance measurement techniques included finding the capability of social media to meet survival objectives, improve capacity utilisation and business resale value