Mathematical modelling

Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions.

Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks.

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This book gathers peer-reviewed contributions submitted to the 21st European Conference on Mathematics for Industry, ECMI 2021, which was virtually held online, hosted by the University of Wuppertal, Germany, from April 13th to April 15th, 2021.

This book gathers contributions from the fourth edition of the Conference on "e;Philosophy and Theory of Artificial Intelligence"e; (PT-AI), held on 27-28th of September 2021 at Chalmers University of Technology, in Gothenburg, Sweden.

The investigation of the role of mechanical and mechano-chemical interactions in cellular processes and tissue development is a rapidly growing research field in the life sciences and in biomedical engineering.

This monograph is focused mostly on the exposition of analytical methods for the solution of problems of strong phase change.

This monograph is devoted to Eulerian models for fluid-structure interaction by applying the original point of view of level set methods.

This book collects a range of contributions on nonlinear dynamics and complexity, providing a systematic summary of recent developments, applications, and overall advances in nonlinearity, chaos, and complexity.

This contributed volume contains a collection of articles on state-of-the-art developments on the construction of theoretical integral techniques and their application to specific problems in science and engineering.

From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans-swinging between their augmentation and replacement.

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques.

This book introduces a class of alignment models based on the so-called Cucker-Smale system as well as its kinetic and hydrodynamic counterparts.

This book comprises chapters authored by experts who are professors and researchers in internationally recognized universities and research institutions.

Learning to solve sequential decision-making tasks is difficult.

heterogeneous graphs.

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry.

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks.

Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic.

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?

Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation.

Graphical models (e.

The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving.

Social choice theory deals with aggregating the preferences of multiple individuals regarding several available alternatives, a situation colloquially known as voting.

Human decision-making often transcends our formal models of "e;rationality.

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation.

Many real-world decision problems have multiple objectives.

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc.

This book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives.

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization.

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available.

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher.

General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "e;runtime"e; (n other words, they don't know the rules until the game starts).

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security.

Judgment aggregation is a mathematical theory of collective decision-making.

Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field.

Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services.

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals.

From driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes.

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases.

Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR).

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns.

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics.

Cooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible.

Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity.

Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms.