Components Of Efficient Problem Solving
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Problem-Solving Methods
Researchers in Artificial Intelligence have traditionally been classified into two categories: the “neaties” and the “scruffies”. According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it “an art”, thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).
Building Intelligent Agents
Building Intelligent Agents is unique in its comprehensive coverage of the subject. The first part of the book presents an original theory for building intelligent agents and a methodology and tool that implement the theory. The second part of the book presents complex and detailed case studies of building different types of agents: an educational assessment agent, a statistical analysis assessment and support agent, an engineering design assistant, and a virtual military commander. Also featured in this book is Disciple, a toolkit for building interactive agents which function in much the same way as a human apprentice. Disciple-based agents can reason both with incomplete information, but also with information that is potentially incorrect. This approach, in which the agent learns its behavior from its teacher, integrates many machine learning and knowledge acquisition techniques, taking advantage of their complementary strengths to compensate for each others weakness. As a consequence, it significantly reduces (or even eliminates) the involvement of a knowledge engineer in the process of building an intelligent agent.
TEXT BOOK OF ARTIFICIAL INTELLIGENCE
Author: Dr. Rakesh Singh, Dr. Shuchi Dave, Prof. Sushil K. Kashaw, Prof. (Dr.) Sandeep Gangrade, Lalbihari Barik
language: en
Publisher: Shashwat Publication
Release Date: 2025-04-25
Textbook of Artificial Intelligence is a comprehensive guide for students, educators, and professionals seeking foundational and advanced knowledge in AI. It begins with a clear definition and history of Artificial Intelligence, helping readers understand its roots and evolution. The book explores real-world applications of AI across various industries including healthcare, finance, education, and autonomous systems. Core AI branches like Machine Learning, Deep Learning, NLP, Robotics, and Computer Vision are introduced with practical insights. In-depth coverage of Intelligent Agents explains their structure, types, and operating environments. The Problem Solving section walks readers through classic algorithms like BFS, DFS, A*, and adversarial search techniques. Knowledge Representation and Reasoning introduces propositional logic, predicate logic, semantic nets, and uncertainty models like Bayesian networks. Machine Learning fundamentals cover supervised, unsupervised, and reinforcement learning, alongside key algorithms and neural networks. Advanced topics like CNNs, RNNs, Transformers, GANs, and NLP tasks are well-structured for deeper understanding. Dedicated chapters on AI in real-world applications showcase use cases in robotics, vision, and recommender systems. Hands-on tools like TensorFlow, PyTorch, Keras, and data handling with Pandas and NumPy are introduced for practical learning. The book encourages ethical thinking with discussions on AI fairness, privacy, transparency, and regulation. A special focus on the future of AI covers trends like generative models, autonomous agents, and human-AI collaboration. Well-organized content helps learners connect theory to practical implementation and innovation. Step-by-step examples and algorithm breakdowns make complex topics easy to understand. Each chapter includes conceptual summaries, illustrations, and review questions for better retention. Perfect for beginners and intermediate learners, as well as educators designing AI curricula. Prepares students for research and industry careers with real-world insights and project ideas. Bridges the gap between traditional AI principles and modern AI technologies. A valuable reference for anyone passionate about building intelligent systems and exploring the world of AI.