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• Conversational Bot/QA/IR
• Knowledge graph/IE
• Machine Learning for NLP
• Machine Translation
• NLP Applications.

This two volume set of LNAI 11108 and LNAI 11109 constitutes the refereed proceedings of the 7th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2018, held in Hohhot, China, in August 2018. The 55 full papers and 31 short papers presented were carefully reviewed and selected from 308 submissions. The papers of the first volume are organized in the following topics: conversational Bot/QA/IR; knowledge graph/IE; machine learning for NLP; machine translation; and NLP applications. The papers of the second volume are organized as follows: NLP for social network; NLP fundamentals; text mining; and short papers.

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator
• Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition
• Face Recognition Benchmark with ID Photos
• Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition
• Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion
• A Demand-based Allocation Mechanism for Virtual Machine
• A Joint Hierarchy Model for Action Recognition Using Kinect
• QoS-Based Medical Program Evolution
• An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry
• The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography
• Combining CNN and MRF for Road Detection
• The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis
• A Diffeomorphic Demons Approach to Statistical Shape Modeling.

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.

• Java deep learning cookbook: train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
• Dedication
• About Packt
• Contributors
• Table of Contents
• Preface
• 1. Introduction to Deep Learning in Java
• 2. Data Extraction, Transformation, and Loading
• 3. Building Deep Neural Networks for Binary Classification
• 4. Building Convolutional Neural Networks
• 5. Implementing Natural Language Processing
• 6. Constructing an LSTM Network for Time Series
• 7. Constructing an LSTM Neural Network for Sequence Classification
• 8. Performing Anomaly Detection on Unsupervised Data
• 9. Using RL4J for Reinforcement Learning
• 10. Developing Applications in a Distributed Environment
• 11. Applying Transfer Learning to Network Models
• 12. Benchmarking and Neural Network Optimization
• Other Books You May Enjoy
• Index.

Deep Learning is a trending topic in AI currently, as it allows you to make faster and more accurate predictions using the power of neural networks. This book will teach you the process of neural network design, and show you how to develop efficient deep learning applications using Deeplearning4j through practical and easy to implement recipes.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Chapter 1: Machine Learning for Cybersecurity
• Technical requirements
• Train-test-splitting your data
• Getting ready
• How to do it ...
• How it works ...
• Standardizing your data
• Getting ready
• How to do it ...
• How it works ...
• Summarizing large data using principal component analysis
• Getting ready
• How to do it ...
• How it works ...
• Generating text using Markov chains
• Getting ready
• How to do it ...
• How it works ...

• Performing clustering using scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Training an XGBoost classifier
• Getting ready
• How to do it ...
• How it works ...
• Analyzing time series using statsmodels
• Getting ready
• How to do it ...
• How it works ...
• Anomaly detection with Isolation Forest
• Getting ready
• How to do it ...
• How it works ...
• Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Hyperparameter tuning with scikit-optimize
• Getting ready
• How to do it ...

• How it works ...
• Chapter 2: Machine Learning-Based Malware Detection
• Technical requirements
• Malware static analysis
• Computing the hash of a sample
• Getting ready
• How to do it ...
• How it works ...
• YARA
• Getting ready
• How to do it ...
• How it works ...
• Examining the PE header
• Getting ready
• How to do it ...
• How it works ...
• Featurizing the PE header
• Getting ready
• How to do it ...
• How it works ...
• Malware dynamic analysis
• Getting ready
• How to do it ...
• How it works ...
• Using machine learning to detect the file type

• Scraping GitHub for files of a specific type
• Getting ready
• How to do it ...
• How it works ...
• Classifying files by type
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two strings
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams
• Getting ready
• How to do it ...
• How it works ...
• Selecting the best N-grams
• Getting ready
• How to do it ...
• How it works ...
• Building a static malware detector
• Getting ready

• How to do it ...
• How it works ...
• Tackling class imbalance
• Getting ready
• How to do it ...
• How it works ...
• Handling type I and type II errors
• Getting ready
• How to do it ...
• How it works ...
• Chapter 3: Advanced Malware Detection
• Technical requirements
• Detecting obfuscated JavaScript
• Getting ready
• How to do it ...
• How it works ...
• Featurizing PDF files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams quickly using the hash-gram algorithm
• Getting ready
• How to do it ...
• How it works ...
• See also
• Building a dynamic malware classifier

This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats.

• Vehicle Navigation Systems
• Localization and Mapping
• Path Planning
• Sensing and Tracking Systems
• Identification and Motion Control of Robotic Vehicles
• Coordinated and Cooperative Control of Multi-Vehicle Systems.

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Frontmatter
• Remerciements
• Sommaire
• Préface
• 1. Qu'est-ce au juste que l'intelligence artificielle?
• 2. Avec l'intelligence artificielle générale pour Graal
• 3. Langage, créativité et émotions
• 4. Les réseaux de neurones artificiels
• 5. Les robots et la vie artificielle (A-life)
• 6. Mais, est-ce réellement de l'intelligence?
• 7. La Singularité
• Références
• Lectures complémentaires
• Index

L'intelligence artificielle, qui consiste à faire faire aux ordinateurs ce que peut faire l'esprit humain et ses applications touchent tous les aspects de notre vie et se trouvent tout autour de nous. Ce petit livre nous rappelle que les résultats de l'intelligence artificielle ont été inestimables pour les biologistes, les psychologues et les linguistes en les aidant à comprendre les processus de la mémoire, de l'apprentissage et du langage sous un angle nouveau. L'intelligence artificielle a aussi alimenté et aiguisé les débats philosophiques concernant la nature de l'esprit, l'intelligence et le caractère unique des êtres humains. Ainsi, Margaret Boden, leader dans le domaine de la recherche en intelligence artificielle, passe en revue ici tous les défis philosophiques et technologiques soulevés par l'intelligence artificielle, et tente de répondre à la question si complexe : les programmes pourront-ils être un jour réellement intelligents, créatifs, ou même conscients ?

• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
• (source: Nielsen Book Data)

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
(source: Nielsen Book Data)

• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
• (source: Nielsen Book Data)

The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
(source: Nielsen Book Data)

• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
• (source: Nielsen Book Data)

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. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
(source: Nielsen Book Data)

• The Lovelace Test
• Three types of creativity
• Ready steady go
• Algorithms, the secret to modern life
• From top-down to bottom-up
• Algorithmic evolution
• Painting by numbers
• Learning from the masters
• The art of mathematics
• The mathematician's telescope
• Music: the process of sounding mathematics
• The song-writing formula
• Deepmathematics
• Language games
• Let AI tell you a story
• Why we create: a meeting of minds.

Most books on AI focus on the future of work. But now that algorithms can learn and adapt, does the future of creativity also belong to well-programmed machines? To answer this question, Marcus du Sautoy takes us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines.-- Provided by publisher.

• Table of Contents Understanding Rewards-Based Learning Dynamic Programming and the Bellman Equation Monte Carlo Methods Temporal Difference Learning Exploring SARSA Going Deep with DQN Going Deeper with DDQN Policy Gradient Methods Optimizing for Continuous Control All about Rainbow DQN Exploiting ML-Agents DRL Frameworks 3D Worlds From DRL to AGI.
• (source: Nielsen Book Data)

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book DescriptionWith the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent's productivity. As you advance, you'll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you'll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learn Understand how deep learning can be integrated into an RL agent Explore basic to advanced algorithms commonly used in game development Build agents that can learn and solve problems in all types of environments Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem Develop game AI agents by understanding the mechanism behind complex AI Integrate all the concepts learned into new projects or gaming agents Who this book is forIf you're a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Related Learning Paradigms Lifelong Supervised Learning Continual Learning and Catastrophic Forgetting Open-World Learning Lifelong Topic Modeling Lifelong Information Extraction Continuous Knowledge Learning in Chatbots Lifelong Reinforcement Learning Conclusion and Future Directions Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

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. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
(source: Nielsen Book Data)

• 1. Models for mobile agent computing
• Introduction
• What is a mobile agent
• Why mobile agents
• An algorithmic model for mobile agents
• Mobile agents
• Distributed networks
• Resource measures
• Mobile agent rendezvous
• Outline of the book
• Comments and bibliographic remarks.

• 2. Deterministic rendezvous in a ring
• Introduction
• A single stationary token
• The feasibility of rendezvous
• The time complexity of rendezvous
• Memory tradeoff for rendezvous with detection
• Limits to the memory trade-off
• Movable tokens
• Comments and bibliographic remarks.

• 3. Multiple agent rendezvous in a ring
• Introduction
• Impossibility of rendezvous
• Rendezvous with detection
• Conditional solutions
• Comments and bibliographic remarks.

• 4. Randomized rendezvous in a ring
• Introduction
• Random walk algorithm
• Randomization and tokens
• Time/memory trade-offs
• Coin half tour algorithm
• Approximate counting algorithm
• Comments and bibliographic remarks.

• 5. Other models
• Introduction
• Leader election and rendezvous
• Rendezvous with failing tokens
• Rendezvous when tokens fail upon release
• Rendezvous when tokens can fail at any time
• The cost of token failure
• Flickering tokens
• Asynchronous rendezvous
• Look-compute-move
• Model and terminology
• Impossibility results
• Gathering configurations with a single multiplicity
• Gathering rigid configurations
• Gathering an odd number of robots
• Dangerous networks
• Black-hole search in an asynchronous ring
• Rendezvous in asynchronous rings in spite of a black-hole
• Comments and bibliographic remarks.

• 6. Other topologies
• Introduction
• Synchronous torus
• Memory lower bounds for rendezvous
• Rendezvous algorithms
• Trees
• Arbitrary graphs
• Comments and bibliographic remarks.

• Bibliography
• Glossary
• Authors' biographies
• Index.

Mobile agent computing is being used in fields as diverse as artificial intelligence, computational economics and robotics. Agents' ability to adapt dynamically and execute asynchronously and autonomously brings potential advantages in terms of fault-tolerance, flexibility and simplicity. This monograph focuses on studying mobile agents as modelled in distributed systems research and in particular within the framework of research performed in the distributed algorithms community. It studies the fundamental question of how to achieve rendezvous, the gathering of two or more agents at the same node of a network. Like leader election, such an operation is a useful subroutine in more general computations that may require the agents to synchronize, share information, divide up chores, etc. The work provides an introduction to the algorithmic issues raised by the rendezvous problem in the distributed computing setting. For the most part our investigation concentrates on the simplest case of two agents attempting to rendezvous on a ring network. Other situations including multiple agents, faulty nodes and other topologies are also examined. An extensive bibliography provides many pointers to related work not covered in the text. The presentation has a distinctly algorithmic, rigorous, distributed computing flavor and most results should be easily accessible to advanced undergraduate and graduate students in computer science and mathematics departments.

• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
• (source: Nielsen Book Data)

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. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
(source: Nielsen Book Data)

• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
• (source: Nielsen Book Data)

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
(source: Nielsen Book Data)

From making faster, better decisions to automating rote work to enabling robots to respond to emotions, AI and machine learning are already reshaping business and society. What should you and your company be doing today to ensure that you're poised for success and keeping up with your competitors in the age of AI' Artificial Intelligence: The Insights You Need from Harvard Business Review brings you today's most essential thinking on AI and explains how to launch the right initiatives at your company to capitalize on the opportunity of the machine intelligence revolution. Business is changing. Will you adapt or be left behind' Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues-blockchain, cybersecurity, AI, and more-each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas-and prepare you and your company for the future.

• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
• (source: Nielsen Book Data)

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. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
(source: Nielsen Book Data)

• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
• (source: Nielsen Book Data)

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
(source: Nielsen Book Data)

• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
• (source: Nielsen Book Data)

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
(source: Nielsen Book Data)