Hierarchical reinforcement learning for trading agents

Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets

Investigating HCI challenges for designing smart environments

With the advancement of technologies related to ‘Internet of Things’, we are moving towards environments characterised by full integration and semantics. Various environments are often summarized with terms such as ‘Smart City’, ‘Smart Home’, ‘Smart Buildings’ or ‘Smart Commerce’. In the meantime, technologies and standards for interoperability have been developed. However, to realise the full potential one remaining challenge is the design, integration and interoperability of many elements into a smart environment. In order to address this challenge, researchers have proposed concepts for Information Systems Design and Enterprise Architectures. By inspecting interaction challenges -in particular activities in which Humans are involved- during the design process, we endeavour in this paper to identify key challenges for designing smart environments. In order to address the challenges we propose a conversational approach that supports the main design phases and allows professionals to interact during the design phases for smart environments

IT Leadership in Transition - The Impact of Digitalization on Finnish Organizations

Digitalization is transforming business models across industries. As information technology (IT) is becoming embedded in products and services, IT leadership has an increasingly dualistic role in supporting the organization and also serving its customers' changing needs. The ACIO research program studied how Finnish industry and public sector organizations utilize information technology in developing and managing critical business capabilities. The focus was on understanding and analyzing contemporary approaches to IT leadership. This research report summarizes some of the key research findings, providing scholars and practitioners with insights into and understanding of digitalization and changes in IT leadership in Finnish informationintensive organizations

The Future of Electronic Commerce: A Shift from the EC Channel to Strategic Electronic Commerce

Poor business performance and lost equity values have cast doubt for some on the future viability of electronic commerce. Most of this attention focuses on the EC channel, just one aspect of electronic commerce. The paper examines the effectiveness of the EC channel for seven types of products and infers that for four of them, the EC channel is unlikely to be the best way to sell and deliver goods and services. Much of the value to be had from this channel has already been captured and overinvestment in it may result in continued contraction. A more mature EC model, the Strategic Electronic Commerce Model (SECM), is proposed that provides a framework for balanced EC investments across the value chain and continued opportunities for EC investment

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices as a time series task. We proposed utilizing two different approaches to address this problem, namely, 1) machine learning (ML)-based models and 2) deep learning (DL)-based models. Thus, this research tuned a set of well-known predictive models on a real-life dataset. Those models are ensemble learning-based models, random forest, Ridge, Lasso, and Linear regression. Moreover, two new DL architectures based on gated recurrent unit (GRU) and long short-term memory (LSTM) models are proposed. Then, the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics, where the evaluation includes both numerical and visual comparisons of the examined models. The obtained results show that the ensemble learning models outperformed the classic machine learning-based models (e.g., linear regression and random forest) and the DL-based models

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area