Artificial intelligence and literary creativity: inside the mind of BRUTUS, a storytelling machine

	Author(s):	Selmer Bringsjord, David Ferrucci
	Collection:
	Publisher:	L. Erlbaum Associates
	Year:	2000
	Langue:	English
	Pages:	230 pages
	Size:	1.78 MB
	Extension:	PDF

[tab] [content title="Summary"] **Description:** Is human creativity something AI can never replicate? Many people believe that while AI systems may match or exceed human expertise in specific domains, true feats of brilliance—those that require creativity—will always be beyond the grasp of machines. However, this book challenges that assumption. The authors aim to push artificial intelligence toward a future where machines can not only generate basic text, but create first-rate fiction that rivals the work of human geniuses. This book chronicles five years of work on *BRUTUS.1*, a story-generating system that was designed to explore the boundaries of machine creativity. The authors delve into the technical and philosophical aspects of their project, which was driven by three main goals: 1. **Theoretical Exploration**: To answer the age-old question of whether humans are fundamentally different from machines. Can machines, with the right programming, replicate the emotional depth and innovative thought that is typically associated with human creativity? 2. **Challenging Conventional Wisdom**: To challenge the belief that creativity is inherently outside the scope of logic. The authors aim to bridge the gap between the logical operations of machines and the emotional, intuitive realm of human creativity. 3. **Practical Application**: To lay the groundwork for the future of AI as a creative partner. Machines capable of producing high-level creative works could significantly enhance human endeavors in fields such as literature, art, and innovation. In this book, readers will discover the results of the BRUTUS.1 project and the broader implications of AI's role in creativity. It's a thought-provoking exploration for anyone interested in the intersection of technology and the arts, as well as those curious about the potential of AI to collaborate with humans in creative fields. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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Legal Knowledge and Information Systems: JURIX 2006 - the Nineteenth Annual Conference

	Author(s):	T.M. Van Engers
	Collection:
	Publisher:	IOS Press
	Year:	2006
	Langue:	English
	Pages:	179 pages
	Size:	1.96 MB
	Extension:	PDF

[tab] [content title="Summary"] This volume presents the Proceedings of the Nineteenth JURIX Conference on Legal Knowledge and Information Systems (JURIX 2006). The large number of submissions highlights the continued growth and activity of the interdisciplinary community within the field. As in previous editions, this volume covers a wide range of topics, reflecting the diverse nature of AI & Law research. Argumentation, a fundamental aspect of legal reasoning, remains a central focus. It is no surprise, therefore, that several researchers have delved into computational theories of argumentation, with four papers dedicated to this topic in the current volume. Another key theme in the legal domain is the use of written legal sources, which have long been the subject of AI & Law research. These studies explore everything from structuring and accessing legal texts to applying natural language processing techniques for understanding the semantics of legal language. This book includes four papers specifically on legal sources. A critical area within AI and AI & Law is knowledge representation and ontologies, which are gaining increasing attention due to advancements in Semantic Web research. Four papers in this volume explore these topics in depth. In addition, the volume features three papers on various applications of AI in legal contexts, as well as four short papers covering a range of other topics relevant to the field. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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Encyclopedia of decision making and decision support technologies

	Author(s):	Frederic Adam, Frederic Adam, Patrick Humphreys
	Collection:
	Publisher:	Information Science Reference
	Year:	2008
	Langue:	English
	Pages:	1020 pages
	Size:	16.30 MB
	Extension:	PDF

[tab] [content title="Summary"] As organizational decision-making plays a crucial role in a company's success, there is a growing need for a comprehensive and coherent resource that brings together the latest research on decision support systems. *The Encyclopedia of Decision Making and Decision Support Technologies* addresses this need by providing an extensive overview of current research in the field, covering both human and computer support for managerial decision-making. This volume explores a range of critical topics related to decision support, including support for operational, tactical, and strategic decisions, as well as the dynamics of individual versus computer system support structures. It delves into both individual and group decision-making processes, with a special focus on multi-criteria decision-making, which involves evaluating and making decisions based on multiple, often conflicting, criteria. The encyclopedia serves as an essential reference for academicians, researchers, and professionals seeking to understand the state-of-the-art in decision support technologies. By integrating human and technological perspectives, it offers a comprehensive examination of the tools, methodologies, and theories that shape decision-making in organizations today. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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Linear Genetic Programming

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	Author(s):	Markus F. Brameier, Wolfgang Banzhaf
	Collection:
	Publisher:	Springer
	Year:	2007
	Langue:	English
	Pages:	315 pages
	Size:	1.74 MB
	Extension:	PDF

[tab] [content title="Summary"] Linear Genetic Programming (LGP) focuses on the evolution of imperative computer programs represented as linear sequences of instructions. Unlike traditional Genetic Programming (GP), which utilizes functional expressions or syntax trees, LGP uses a linear program structure as genetic material. This approach leverages its primary characteristics to enhance both execution speed and evolutionary advancement. By conducting online analysis and optimization of program code, LGP develops more efficient techniques and deepens the understanding of the methodology and its parameters. Notably, reducing the step size of structural variation and minimizing non-effective variations are crucial for discovering higher-quality, less complex solutions. This volume explores typical GP phenomena, such as non-effective code, neutral variations, and code growth, specifically through the lens of linear GP. The text is structured into three sections, each detailing methodologies and showcasing applications. Part I introduces fundamental concepts of linear GP and presents effective algorithms for analyzing and optimizing linear genetic programs during runtime. Part II delves into the design of efficient LGP methods and genetic operators inspired by findings from Part I. Part III examines more advanced techniques and phenomena, including effective step size control, diversity management, code growth, and neutral variations. This book serves as a solid introduction to linear GP while offering a comprehensive exploration of its principles and techniques. It will undoubtedly be an invaluable resource for both researchers and students in the field. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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Modern Robotics: Mechanics, Planning, and Control

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	Author(s):	Frank C. Park, Kevin M. Lynch
	Collection:
	Publisher:	Cambridge University Press
	Year:	2017
	Langue:	English
	Pages:	544 pages
	Size:	7 MB
	Extension:	PDF

[tab] [content title="Summary"] This introduction to robotics provides a unique and cohesive view of the mechanics, planning, and control of robots. It is well-suited for self-study or as part of a course, requiring only a foundational understanding of freshman-level physics, ordinary differential equations, linear algebra, and some computing knowledge. "Modern Robotics" showcases advanced screw-theoretic techniques that intuitively capture the key physical aspects of robots in a geometrical context. Each chapter concludes with numerous exercises, and there is accompanying software designed to reinforce the book's concepts, along with video lectures that enhance the classroom experience. This makes it the essential textbook for anyone interested in this captivating field. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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Machine Learning: Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition)

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	Author(s):	Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain, Ritesh Kumar, Lakhwani, Dr Kamlesh
	Collection:
	Publisher:	BPB Publications
	Year:	2021
	Langue:	English
	Pages:	294 pages
	Size:	6.34 MB
	Extension:	PDF

[tab] [content title="Summary"] ### Concepts of Machine Learning with Practical Approaches **Key Features** - Real-world examples illustrate how Machine Learning algorithms function. - Graphical and statistical representations simplify modeling for Machine Learning and Neural Networks. - Packed with Python code, numerous exercises, and model question papers for data science students. **Description** This book provides readers with fundamental concepts of Machine Learning in an accessible language. It aims to impart in-depth knowledge of various Machine Learning (ML) algorithms and their practical applications. The book covers a range of Supervised Machine Learning algorithms, including Linear Regression, Naïve Bayes, Decision Trees, K-nearest neighbors, Logistic Regression, Support Vector Machines, and Random Forests. It also explores Unsupervised Machine Learning techniques such as k-means clustering, Hierarchical Clustering, Probabilistic Clustering, Association Rule Mining, Apriori Algorithm, f-p growth algorithm, and Gaussian Mixture Models. Additionally, it discusses Reinforcement Learning algorithms like Markov Decision Processes (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy Iteration, Value Iteration, Q-Learning, and State-Action-Reward-State-Action (SARSA). The book further covers various feature extraction and selection techniques, Recommender Systems, and provides an overview of Deep Learning. **What You Will Learn** - Techniques for feature extraction and selection. - How to choose the best Machine Learning algorithm for specific problems. - Proficiency in popular Python libraries such as Scikit-learn, pandas, and matplotlib. - Practical implementation of various Machine Learning methods. **Who This Book Is For** This book is aimed at data science and analytics students, academics, and researchers interested in exploring Machine Learning concepts and applying them to real-world scenarios. Familiarity with basic statistical and programming concepts is beneficial but not required. **Table of Contents** 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommender Systems **About the Authors** **Dr. Ruchi Doshi** has over 14 years of experience in academia, research, and software development across Asia and Africa. She is currently a research supervisor at Azteca University, Mexico, and an adjunct faculty member at Jyoti Vidyapeeth Women’s University, Jaipur, India. **Kamal Kant Hiran** serves as an Assistant Professor in the School of Engineering at Sir Padampat Singhania University (SPSU), Udaipur, India, and as a Research Fellow at Aalborg University, Copenhagen, Denmark. He is a Gold Medalist in M.Tech. (Hons.) with over 16 years of academic and research experience in Asia, Africa, and Europe. **Ritesh Kumar Jain** is an Assistant Professor at Geetanjali Institute of Technical Studies (GITS), Udaipur, India, with over 15 years of teaching and research experience. **Dr. Kamlesh Lakhwani** works as an Associate Professor in Computer Science & Engineering at JECRC University, Jaipur, India, bringing 15 years of academic and research experience in Asia. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]

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