A Survey on Concept Drift Adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art

A survey on machine learning for recurring concept drifting data streams

The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time

Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance

Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the nonstationary nature and the likelihood of drastic structural changes in financial markets. The most recent literature suggests the use of conventional machine learning and statistical approaches for this. However, these techniques are unable or slow to adapt to non-stationarities and may require re-training over time, which is computationally expensive and brings financial risks. This thesis proposes a set of adaptive algorithms to deal with high-frequency data streams and applies these to the financial domain. We present approaches to handle different types of concept drifts and perform predictions using up-to-date models. These mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The core experiments of this thesis are based on the prediction of the price movement direction at different intraday resolutions in the SPDR S&P 500 exchange-traded fund. The proposed algorithms are benchmarked against other popular methods from the data stream mining literature and achieve competitive results. We believe that this thesis opens good research prospects for financial forecasting during market instability and structural breaks. Results have shown that our proposed methods can improve prediction accuracy in many of these scenarios. Indeed, the results obtained are compatible with ideas against the efficient market hypothesis. However, we cannot claim that we can beat consistently buy and hold; therefore, we cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue

Game Challenge: A Factorial Analysis Approach

Video games that customize to a player\u27s experience level and abilities have the potential to allow a broader range of players to become engaged and maintain interest as they progress in experience level. A game that uniquely customizes the player\u27s experience could attract additional demographics to gaming, which will result in a distinct edge in marketability and potential revenue. This thesis examines a subsection of adaptive gaming systems from the perspective of identifying game factors that alter the level of difficulty. Our focus is to provide a solution useful to both research and commercial gaming communities by developing a system that simulates results offline yet can be integrated into online play. While online performance is the main goal of an adaptive system, the offline simulation provides several benefits. Offline simulation allows the elimination of insignificant factors from inclusion in the training and evolution phase of machine learning algorithms. In addition it provides commercial games with a useful tool or method for performing game balancing and level tuning. To test our approach we designed a test-bed version of the game Pac-Man. The experimental testbed alters environment variables to evaluate their effect on a set of selected response variables. Observing the results of several response variables provides the potential to represent multiple player states, though our focus is on controlling the difficulty for a player. The testbed will simulate the actions of both Pac-Man and the ghosts over a variety of different settings and strategies. The evaluation of a factor\u27s significance and its effect size are calculated using a factorial analysis approach. This method allows the identification of factors relevant to both individual strategies, and the set of all player strategies. Finally, as a proof of concept for both the online and adaptation prospects of this method, we developed a prototype adaptive system. Utilizing the relevant factor effects calculated in the factorial analysis, the prototype adapts to control the progress of the game towards targeted response variable intervals

Intuitive interaction: Steps towards an integral understanding of the user experience in interaction design

A critical review of traditional practices and methodologies demonstrates an underplaying of firstly the role of emotions and secondly aspects of exploration in interaction behaviour in favour of a goal orientated focus in the user experience (UX). Consequently, the UX is a commodity that can be designed, measured, and predicted. An integral understanding of the UX attempts to overcome the rationalistic and instrumental mindset of traditional Human-Computer Interaction (HCI) on several levels. Firstly, the thesis seeks to complement a functional view of interaction with a qualitative one that considers the complexity of emotions. Emotions are at the heart of engagement and connect action irreversibly to the moment it occurs; they are intettwined with cognition, and decision making. Furthermore, they introduce the vague and ambiguous aspects of experience and open it up to potentiality of creation. Secondly, the thesis examines the relationship between purposive and non-purposive user behaviour such as exploration, play and discovery. The integral position proposed here stresses the procedurally relational nature and complexity of interaction experience. This requires revisiting and augmenting key themes of HCI practice such as interactivity and intuitive design. Intuition is investigated as an early and unconscious form of learning, and unstructured browsing discussed as random interaction mechanisms as forms of implicit learning. Interactivity here is the space for user's actions, contributions and creativity, not only in the design process but also during interaction as co-authors of their experiences. Finally, I envisage integral forms of usability methods to embrace the vague and the ambiguous, in order to enrich HCI's vocabulary and design potential. Key readings that inform this position cut across contemporary philosophy, media and interaction studies and professional HCI literature. On a practical level, a series of experimental interaction designs for web-browsing aim to augment the user's experience, and create space for user's intuition

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

Towards Prescriptive Analytics in Cyber-Physical Systems

More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid. In today’s CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the system’s operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken. This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS – which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system. At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (user’s) intention, and actions to change the behaviour (state) of a physical system, respectively. At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS. At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them “inside a database”, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case. In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future.Mehr und mehr wird heute unsere physische Welt überwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. Zukünftige CPS werden voraussichtlich weit komplexer, größer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie – in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, Zuverlässigkeit und Sicherheit bringen – anzutreffen sein. Um ihr volles Potenzial entfalten zu können, müssen CPS unter anderem mit der Unterstützung automatisierter Planungs- und Steuerungsfunktionalität ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren. Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgeführt. Leider ist diese Aufgabe nur schwer zu bewältigen, und es ist für den Menschen schlicht unmöglich, jede Aktion in einem zukünftigen CPS auf Basis der Komplexität, des Umfangs und der Volatilität eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften müssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) Vorschläge der wichtigsten auszuführenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fällenden Entscheidungen. Diese Arbeit beschäftigt sich mit der Planung und Kontrolle im Rahmen großer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur Verfügung. In Anbetracht der Komplexität von CPS leistet diese Arbeit Beiträge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems. Auf CPS-Ebene umfassen die Beiträge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und Fähigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verändern. Auf Agenten-Ebene umfassen die Beiträge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren Unterstützung der Funktionalität von PrescriptiveCPS entwickelt wurden. Der Schwerpunkt dieser Arbeit bilden die Beiträge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas für die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines Flexibilitätsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-ähnlichen Anfragen zu formulieren und sie „in einer Datenbank zu lösen“, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische Benutzeroberfläche (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. Darüber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfähige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer „What-if“-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der Beiträge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall. Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der Energiedomäne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese Beiträge einen wesentlichen Schritt in der zukünftigen Entwicklung planender CPS darstellen.Mere og mere af vores fysiske verden bliver overvåget og kontrolleret af såkaldte cyber-fysiske systemer (CPSer). Disse er sammensætninger af netværksbaserede autonome IT (cyber) og fysiske (physical) agenter, såsom sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis små og meget begrænsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at være langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en række områder såsom transport, medicin, produktion og energi, hvor de vil give mange fordele, såsom øget effektivitet, bæredygtighed, pålidelighed og sikkerhed. For at frigøre CPSernes fulde potentiale, skal de bl.a. udstyres med støtte til automatiseret planlægning og kontrol, hvor beregningsagenter i samspil og løbende planlægger og styrer deres handlinger på en intelligent og velkoordineret måde for at sikre og optimere en fysisk proces, såsom elforsyningen i elnettet. I nuværende CPSer er styringen typisk automatiseret, mens planlægningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlægge hver handling i et fremtidigt CPS på grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. På grund af disse egenskaber, skal kontrol og planlægning være kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjælp af det sæt af værktøjer, der understøtter præskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at få forslagene fra de mest fremtrædende sæt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sådanne handlinger blev udført. Denne afhandling omhandler planlægning og kontrol i forbindelse med store multi-agent CPSer. Baseret på en smart-grid use case, præsenterer afhandlingen det såkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nær-realtid og leverer (menneskelige) brugere præskriptiveanalyseværktøjer til at analysere og håndtere det underliggende fysiske system (eller proces). I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til følgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS, (2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system. På CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her består IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af såkaldte fleksibilitet (flexibility), beslutning (decision) og præskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer på fremtiden, og repræsenterer en kapacitet, en (brugers) intention, og måder til at ændre adfærd (tilstand) af et fysisk system. På agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understøtte funktionaliteten af PrescriptiveCPS. Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og præskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af præskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemløsning (optimization problem solving) der gør det muligt at formulere optimeringsproblemer ved hjælp af SQL-lignende forespørgsler og at løse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og præskriptive modeller under (bløde eller hårde) tidsbegrænsninger, og (5) en grafisk brugergrænseflade (GUI) til visuelt at analysere fleksibilitet og præskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domæne-specifikke og in-DBMS generiske prognosemetoder der gør det muligt at forudsige instanser af fleksibilitet modeller baseret på historiske data, og (2) kraftfulde måder at analysere tidligere-, nutids- og fremtidsbaserede såkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og præskriptive modelinstanser gemt i en database. De fleste af bidragene på dette niveau er baseret på et smart-grid brugsscenarie. Sammenfattende giver afhandlingen (1) modellen for et CPS med planlægningsmulighed, (2) design og eksperimentel evaluering af præskriptive analyse teknikker der gør det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiområdet, der viser, hvordan de indførte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgør et betydeligt skridt i retning af at udvikle CPSer til planlægningsbrug i fremtiden

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results