An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data and use that data to perform self-directed tasks that meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a customer support AI agent that wants to resolve customer queries. The agent will automatically ask the customer different questions, look up information in internal documents and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human. In the gripping pages of 'The Intelligent Agents of Artificial Intelligence' readers embark on an enlightening journey into the core of artificial intelligence and machine learning, exploring the technologies that are reshaping our society, industries, and day-to-day lives. This book offers a clear, detailed exploration of the fundamental concepts of AI, from the basic algorithms that enable machine learning to the sophisticated systems that drive today's most innovative applications. Intelligent Agents breaks down complex ideas into understandable segments, guiding readers through the maze of neural networks, deep learning, natural language processing, and computer vision. Each chapter delves into how AI impacts fields such as healthcare, finance, and customer service, providing real-world examples and forecasting future trends. Intelligent agents are one of the most promising business tools in our information rich world. An intelligent agent consists of a software system capable of performing intelligent tasks within a dynamic and unpredictable environment. They can be characterised by various attributes including: autonomous, adaptive, collaborative, communicative, mobile, and reactive. Many problems are not well defined and the information needed to make decisions is not available. These problems are not easy to solve using conventional computing approaches. Here, the intelligent agent paradigm may play a major role in helping to solve these problems. This book, written for application researchers, covers a broad selection of research results that demonstrate, in an authoritative and clear manner, the applications of agents within our information society. This book is more than a technical guide; it is a critical examination of the ethical challenges and societal implications of AI, discussing issues like privacy, automation, and the future of work. "Intelligent Agents" is essential reading for anyone looking to understand not just how AI works, but how it influences the world we live in and the way we think about technology and human capability. This book is about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents.
Dr. Robert Steele, Associate Professor (Senior Lecturer) in Artificial Intelligence at the University of Bath. He works at the intersection of Natural Language Processing, reasoning, linguistics and AI safety, asking a simple but stubbornly hard question: how do large language models actually learn to use language and reason, and what does that mean for how we deploy them in the real world? His research treats usage-based learning as the foundation for how language competence and bounded reasoning emerge in large language models. On the linguistic side, he draws on Construction Grammar (CxG) to understand what kinds of constructions modelsr eally acquire; on the modelling side, he develops Context-Directed Extrapolation, a framework for explaining so-called "emergent abilities" not as magic or AGI, but as models extrapolating from statistical priors when guided by context. This work has reshaped parts of the debate around LLM capabilities, influencing research, policy discussions (including the UK AI Safety Summit), and industry practice. His works are organised into three strands: Emergent reasoning and limits of LLMs: mapping what models can and cannot do, and when scaling stops being a silver bullet. Linguistically grounded evaluation: using insights from theoretical linguistics, specifically construction grammar to probe abstraction, generalisation and comprehension in LLMs. Applications in sensitive domains: from debiasing speech systems and supporting public services, to studying how LLMs may nudge users towards extremist content, and designing scaffolds (like divergent chain of thought and frame-based retrieval) that make models more reliable, interpretable and standard-aligned. Before starting his PhD in automated question answering at the University of Birmingham, Dr. Steele founded and headed a social media data analytics company based out of Singapore.
Preface
1. ARTIFICIAL INTELLIGENCE AND AGENTS
2. AGENT ARCHITECTURES AND HIERARCHICAL CONTROL
3. STATES AND SEARCHING
4. FEATURES AND CONSTRAINTS
5. PROPOSITIONS AND INFERENCE
6. REASONING UNDER UNCERTAINTY
7. LEARNING: OVERVIEW AND SUPERVISED LEARNING
8. PLANNING WITH CERTAINTY
9. PLANNING UNDER UNCERTAINTY
10. MULTIAGENT SYSTEMS
Bibliography
Index
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