SpringerBriefs in Intelligent Systems – serie

The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification.The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition.

The study of multi-winner voting provides the principled analysis of this task. Approval-based committee voting rules (in short: ABC rules) are multi-winner voting rules particularly suitable for practical use.

The area of Explainable Artificial Intelligence (XAI) is concerned with providing methods and tools to improve the interpretability of black-box learning models. While several approaches exist to generate explanations, they are often lacking robustness, e.g., they may produce completely different explanations for similar events. This phenomenon has troubling implications, as lack of robustness indicates that explanations are not capturing the underlying decision-making process of a model and thus cannot be trusted.This book aims at introducing Robust Explainable AI, a rapidly growing field whose focus is to ensure that explanations for machine learning models adhere to the highest robustness standards. We will introduce the most important concepts, methodologies, and results in the field, with a particular focus on techniques developed for feature attribution methods and counterfactual explanations for deep neural networks.As prerequisites, a certain familiarity with neural networks and approaches within XAI is desirable but not mandatory. The book is designed to be self-contained, and relevant concepts will be introduced when needed, together with examples to ensure a successful learning experience.

This open access book provides an overview of the current state of financial argument mining and financial text generation, and presents the authors’ thoughts on the blueprint for NLP in finance in the agent AI era. Financial documents contain numerous causal inferences and subjective opinions.

instead, it offers a set of practical strategies to work with them—mobilizing the capabilities of generative AI to support causal reasoning tailored to disciplinary norms and constraints. Generative AI can engage in multimodal causal inference—connecting language with images, charts, simulations, and numerical data.

Online search engines are an essential tool for seeking information, but results returned from these search engines can contain undesirable forms of bias with respect to protected attributes such as gender or race. These biases can exist due to the word embeddings used by search engines, the design of re-ranking algorithms, the development of retrieval algorithms, or a variety of other reasons. Classical information retrieval (IR) methods, such as query recommendation or query expansion, were designed to produce the most relevant results. However, if such biases are present in the system, then these methods will also deliver biased results.IR systems/recommender systems also play a major role in social media algorithms, where platforms have pivoted away from friend-follow timelines to “for you” timelines containing algorithmically-selected content. If these algorithms are biased (towards, say, maximizing screen time to show ads, maximizing user interaction to likes, comments), then they may push end users towards clickbait or non-mainstream trending topics. This book presents an overview of modern IR and discusses the work done to mitigate biases in IR systems. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user.Awareness of how information retrieval systems work, ways to mitigate bias in search results, and the tradeoffs between accuracy and bias metrics in search results will help readers understand real-world search engines.

This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.

This book provides a comprehensive and structured exploration of the fundamental mechanisms that govern metaheuristic optimization methods. Rather than cataloging existing algorithms, it focuses on the building blocks—the operators and strategies—that enable metaheuristics to efficiently navigate complex search spaces. By analyzing the principles of exploration, exploitation, and their dynamic interaction, the book reveals how the balance between these processes determines algorithmic performance, convergence, and robustness.The book introduces the theoretical foundations of optimization and the architecture common to most metaheuristic algorithms. Readers are guided through the core concepts of search space analysis, stochastic behavior, and the general structure shared by population-based methods. This foundation prepares the ground for a detailed examination of how exploration and exploitation operate as complementary forces within optimization processes. Exploration operators—such as randomization, chaotic dynamics, opposition-based learning, and mutation—are presented as tools for promoting diversity and global discovery. The authors then focuse on exploitation, examining how greedy selection, local refinement, leader-based attraction, and adaptive step-size control enhance convergence toward high-quality solutions. The discussion subsequently extends to dual-role operators that integrate both behaviors, including crossover and hybrid recombination, demonstrating how they dynamically shift between global and local search depending on the problem landscape.The final chapters synthesize these ideas to show how combinations of operators can be strategically designed to create hybrid and adaptive metaheuristics. Readers will learn how operator synergy influences performance, how hybrid frameworks can integrate complementary search mechanisms, and how self-adaptive strategies allow algorithms to evolve their own balance between exploration and exploitation.By shifting the focus from individual algorithm names to the mechanisms that make them work, this book provides a unified framework for understanding, comparing, and designing metaheuristic methods. It equips readers with the conceptual tools to analyze the internal dynamics of optimization processes and to construct their own customized search strategies for complex real-world problems.Written in a clear and accessible style, this book is intended for graduate students, researchers, and practitioners in computer science, engineering, and applied mathematics who wish to deepen their understanding of metaheuristic design principles and develop more efficient, adaptive optimization algorithms.

This book introduces a new logic-based multi-paradigm programming language that integrates logic programming, functional programming, dynamic programming with tabling, and scripting, for use in solving combinatorial search problems, including CP, SAT, and MIP (mixed integer programming) based solver modules, and a module for planning that is implemented using tabling.The book is useful for undergraduate and graduate students, researchers, and practitioners.

This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.

This book explains how the logic of theory change employs formal models in the investigation of changes in belief states and databases. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, and criticism of the model.

This book presents TDF (Tactics Development Framework), a practical methodology for eliciting and engineering models of expert decision-making in dynamic domains.

This book examines how two distinct strands of research on autonomous robots, evolutionary robotics and humanoid robot research, are converging. The book will be valuable for researchers and postgraduate students working in the areas of evolutionary robotics and bio-inspired computing.

This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics.  The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, andmachine learning practitioners.