In this thesis we study the computational complexity of MinCSP - an optimization version of the Constraint Satisfaction Problem (CSP). The input to a MinCSP is a set of variables and constraints applied to these variables, and the goal is to assign values (from a fixed domain) to the variables while minimizing the solution cost, i.e. the number of unsatisfied constraints. We are specifically interested in MinCSP with infinite domains of values. Infinite-domain MinCSPs model fundamental optimization problems in computer science and are of particular relevance to artificial intelligence, especially temporal and spatial reasoning. The usual way to study computational complexity of CSPs is to restrict the types of constraints that can be used in the inputs, and either construct fast algorithms or prove lower bounds on the complexity of the resulting problems. The vast majority of interesting MinCSPs are NP-hard, so standard complexity-theoretic assumptions imply that we cannot find exact solutions to all inputs of these problems in polynomial time with respect to the input size. Hence, we need to relax at least one of the three requirements above, opting for either approximate solutions, solving some inputs, or using super-polynomial time. Parameterized algorithms exploits the latter two relaxations by identifying some common structure of the interesting inputs described by some parameter, and then allowing super-polynomial running times with respect to that parameter. Such algorithms are feasible for inputs of any size whenever the parameter value is small. For MinCSP, a natural parameter is optimal solution cost. We also study parameterized approximation algorithms, where the requirement for exact solutions is also relaxed. We present complete complexity classifications for several important classes of infinite-domain constraints. These are simple temporal constraints and interval constraints, which have notable applications in temporal reasoning in AI, linear equations over finite and infinite fields as well as some commutative rings (e.g., the rationals and the integers), which are of fundamental theoretical importance, and equality constraints, which are closely related to connectivity problems in undirected graphs and form the basis of studying first-order definable constraints over infinite domains. In all cases, we prove results as follows: we fix a (possibly infinite) set of allowed constraint types C, and for every finite subset of C, determine whether MinCSP(), i.e., MinCSP restricted to the constraint types in , is fixed-parameter tractable, i.e. solvable in f(k) · poly(n) time, where k is the parameter, n is the input size, and f is any function that depends solely on k. To rule out such algorithms, we prove lower bounds under standard assumptions of parameterized complexity. In all cases except simple temporal constraints, we also provide complete classifications for fixed-parameter time constant-factor approximation.

This thesis explores, through a mixed-methods approach, what happens when companion robots are deployed in care homes for older adults by looking at different perspectives from key stakeholders. Nine studies are presented with decision makers in municipalities, care staff and older adults, as participants, and the studies have primarily been carried out in the field in care homes and activity centres, where both qualitative (e.g., observations and workshops) and quantitative data (surveys) have been collected. The thesis shows that companion robots seem to be here to stay and that they can contribute to a higher quality of life for some older adults. It further presents some challenges with a certain discrepancy between what decision makers want and what staff might be able to facilitate. For future research and use of companion robots, it is key to evaluate each robot model and potential use case separately and develop clear routines for how they should be used, and most importantly, let all stakeholders be part of the process. The knowledge contribution is the holistic view of how different actors affect each other when emerging robot technology is introduced in a care environment. Den här avhandlingen utforskar vad som händer när sällskapsrobotar införs på omsorgsboenden för äldre genom att titta på perspektiv från olika intressenter. Nio studier presenteras med kommunala beslutsfattare, vårdpersonal och äldre som deltagare. Studierna har i huvudsak genomförts i fält på särskilda boenden och aktivitetscenter där både kvalitativa- (exempelvis observationer och workshops) och kvantitativa data (enkäter) har samlats in. Avhandlingen visar att sällskapsrobotar verkar vara här för att stanna och att de kan bidra till en högre livskvalitet för vissa äldre. Den visar även på en del utmaningar med en viss diskrepans mellan vad beslutsfattare vill införa och vad personalen har möjlighet att utföra i sitt arbete. För framtida forskning och användning av sällskapsrobotar är det viktigt att utvärdera varje robotmodell och varje användningsområde var för sig och ta fram tydliga rutiner för hur de ska användas, och viktigast av allt, låta alla intressenter vara en del av processen. Kunskapsbidraget med avhandlingen är en helhetssyn på hur olika aktörer påverkar varandra när ny robotteknik introduceras i en vårdmiljö

This thesis presents Machine Psychology as an interdisciplinary paradigm that integrates learning psychology principles with an adaptive computer system for the development of Artificial General Intelligence (AGI). By synthesizing behavioral psychology with a formal intelligence model, the Non-Axiomatic Reasoning System (NARS), this work explores the potential of operant conditioning paradigms to advance AGI research. The thesis begins by introducing the conceptual foundations of Machine Psychology, detailing its alignment with the theoretical constructs of learning psychology and the formalism of NARS. It then progresses through a series of empirical studies designed to systematically investigate the emergence of increasingly complex cognitive behaviors as NARS interacts with its environment. Initially, operant conditioning is established as a foundational principle for developing adaptive behavior with NARS. Subsequent chapters explore increasingly sophisticated cognitive capabilities, all studied with NARS using experimental paradigms from operant learning psychology: Generalized identity matching, Functional equivalence, and Arbitrarily Applicable Relational Responding. Throughout this research, Machine Psychology is demonstrated to be a promising framework for guiding AGI research, allowing both the manipulation of environmental contingencies and the system’s intrinsic logical processes. The thesis contributes to AGI research by showing how using operant psychological paradigms with NARS can enable cognitive abilities similar to human cognition. These findings set the stage for AGI systems that learn and adapt more like humans, potentially advancing the creation of more general and flexible AI. Denna avhandling introducerar Maskinpsykologi som ett tvärvetenskapligt område där principer från inlärningspsykologi integreras med ett adaptivt datorsystem. Genom att kombinera forskning från beteendepsykologi med en formell modell för intelligens (Non-Axiomatic Reasoning System; NARS), undersöker avhandlingen hur operant betingning kan användas för att driva utvecklingen av Artificiell General Intelligens (AGI) framåt. Avhandlingen börjar med att förklara grunderna i Maskinpsykologi och hur dessa relaterar till både inlärningspsykologi och NARS. Därefter presenteras en serie experiment som systematiskt undersöker hur allt mer komplexa kognitiva beteenden kan uppstå när NARS interagerar med sin omgivning. Till att börja med etableras operant betingning som en central metod för att utveckla adaptiva beteenden med NARS. I de följande kapitlen utforskas hur NARS, genom experiment inspirerade av operant inlärningspsykologi, kan utveckla mer avancerade kognitiva förmågor som till exempel generaliserad identitetsmatchning, funktionell ekvivalens och så kallade arbiträrt applicerbara relationsresponser. Denna forskning visar att Maskinpsykologi är ett lovande verktyg för att vägleda AGI-forskning, eftersom det möjliggör att både påverka omgivningsfaktorer och styra systemets interna logiska processer. Avhandlingen bidrar till AGI-forskning genom att visa hur operanta psykologiska metoder, tillämpade på NARS, kan möjliggöra kognitiva förmågor som liknar mänskligt tänkande. Dessa insikter öppnar nya möjligheter för att utveckla AI-system som kan lära sig och anpassa sig på ett mer mänskligt sätt, vilket kan leda till skapandet av mer generell och flexibel AI.

More and more services are moving to the cloud, attracted by the promise of unlimited resources that are accessible anytime, and are managed by someone else. However, hosting every type of service in large cloud datacenters is not possible or suitable, as some emerging applications have stringent latency or privacy requirements, while also handling huge amounts of data. Therefore, in recent years, a new paradigm has been proposed to address the needs of these applications: the edge computing paradigm. Resources provided at the edge (e.g., for computation and communication) are constrained, hence resource management is of crucial importance. The incoming load to the edge infrastructure varies both in time and space. Managing the edge infrastructure so that the appropriate resources are available at the required time and location is called orchestrating. This is especially challenging in case of sudden load spikes and when the orchestration impact itself has to be limited. This thesis enables edge computing orchestration with increased resource-awareness by contributing with methods, techniques, and concepts for edge resource management. First, it proposes methods to better understand the edge resource demand. Second, it provides solutions on the supply side for orchestrating edge resources with different characteristics in order to serve edge applications with satisfactory quality of service. Finally, the thesis includes a critical perspective on the paradigm, by considering sustainability challenges. To understand the demand patterns, the thesis presents a methodology for categorizing the large variety of use cases that are proposed in the literature as potential applications for edge computing. The thesis also proposes methods for characterizing and modeling applications, as well as for gathering traces from real applications and analyzing them. These different approaches are applied to a prototype from a typical edge application domain: Mixed Reality. The important insight here is that application descriptions or models that are not based on a real application may not be giving an accurate picture of the load. This can drive incorrect decisions about what should be done on the supply side and thus waste resources. Regarding resource supply, the thesis proposes two orchestration frameworks for managing edge resources and successfully dealing with load spikes while avoiding over-provisioning. The first one utilizes mobile edge devices while the second leverages the concept of spare devices. Then, focusing on the request placement part of orchestration, the thesis formalizes it in the case of applications structured as chains of functions (so-called microservices) as an instance of the Traveling Purchaser Problem and solves it using Integer Linear Programming. Two different energy metrics influencing request placement decisions are proposed and evaluated. Finally, the thesis explores further resource awareness. Sustainability challenges that should be highlighted more within edge computing are collected. Among those related to resource use, the strategy of sufficiency is promoted as a way forward. It involves aiming at only using the needed resources (no more, no less) with a goal of reducing resource usage. Different tools to adopt it are proposed and their use demonstrated through a case study.

This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.

An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.