2017 | Buch

# Introduction to Artificial Intelligence

verfasst von: Prof. Dr. Wolfgang Ertel

Verlag: Springer International Publishing

Buchreihe : Undergraduate Topics in Computer Science

2017 | Buch

verfasst von: Prof. Dr. Wolfgang Ertel

Verlag: Springer International Publishing

Buchreihe : Undergraduate Topics in Computer Science

This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning.

Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW).

Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.

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Abstract

The term artificial intelligence (AI) stirs emotions. For one thing there is our fascination with intelligence, which seemingly imparts to us humans a special place among life forms. Questions arise such as “What is intelligence?”, “How can one measure intelligence?” or “How does the brain work?”. All these questions are meaningful when trying to understand artificial intelligence. However, the central question for the engineer, especially for the computer scientist, is the question of the intelligent machine that behaves like a person, showing intelligent behavior. Beside discussing these issues, this introductory chapter gives a brief sketch of the history of AI.

Abstract

In propositional logic, as the name suggests, propositions are connected by logical operators. The statement “the street is wet” is a proposition, as is “it is raining”. These two propositions can be connected to form the new proposition “if it is raining the street is wet”. Written more formally: “it is raining ⇒ the street is wet”. Introducing important concepts such as syntax, semantics, interpretation, model, correctness, completeness, calculus, etc., this chapter serves as a “warm up” for first order predicate logic in Chap. 3. The language of propositional logic will also be used in Chap. 7 as a basis for probabilistic logic.

Abstract

In the 1930s Kurt Gödel Alonso Church, and Alan Turing laid important foundations for logic and, theoretical computer science. Of particular interest for AI are Gödel’s theorems. The completeness theorem states that first-order predicate logic is complete. This means that every true statement that can be formulated in predicate logic is provable using the rules of a formal calculus. Using programmable computers, on this basis, automatic theorem provers could later be constructed as implementations of formal calculi. We introduce the language of first-order predicate logic, develop the resolution calculus and show how automated theorem provers can be built and applied to prove relevant problems in every day reasoning and software engineering.

Abstract

Until the end of the 20th century many logicians believed that theorem provers for first-order logic will be the major component of intelligent agents. Almost all successful modern AI applications however use different formalisms. This is due to some severe problems with first-order logic that we will explain in this chapter.

Abstract

Due to the problems with first-order logic mentioned in Chap. 4, pure logic can not solve most realistic AI problems. Logic programming as a fusion of logic and procedural programming provides the programmer with means for controlling and optimizing logical reasoning. Using the programming language PROLOG, we invite the reader to solve some simple relational problems and puzzles. As a particular highlight, we introduce constraint logic programming, which enables us to elegantly solve for example nontrivial scheduling problems.

Abstract

Many AI problems, like automated theorem proving, game playing, planning or routing, involve combinatorial search in large discrete spaces. We introduce the classical uninformed and heuristic search algorithms such as for example A^{★} and apply them to simple examples. Game search techniques like minimax and alpha-beta pruning and their application in chess computers are discussed.

Abstract

Reasoning under uncertainty with limited resources and incomplete knowledge plays a big role in everyday situations and also in many technical applications of AI. Probabilistic reasoning is the modern AI method for solving these problems. After a brief introduction to probability theory we present the powerful method of maximum entropy and Bayesian networks which are used in many applications. The medical diagnosis expert system Lexmed, developed by the author, is used to demonstrate the power of these formalisms.

Abstract

One of the major AI applications is the development of intelligent autonomous robots. Since flexibility and adaptivity are important features of really intelligent agents, research into learning mechanisms and the development of machine learning algorithms is one of the most important branches of AI. After motivating and introducing basic concepts of machine learning like classification and approximation, this chapter presents basic supervised learning algorithms such as the perceptron, nearest neighbor methods and decision tree induction. Unsupervised clustering methods and data mining software tools complete the picture of this fascinating field.

Abstract

Neural networks are networks of nerve cells in the brains of humans and animals. The human brain has about 100 billion nerve cells. We humans owe our intelligence and our ability to learn various motor skills and intellectual capabilities to the brain’s complex relays and adaptivity. The nerve cells and their connections are responsible for awareness, associations, thoughts, consciousness and the ability to learn. Mathematical models of neural networks and their implementation on computers are nowadays used in many applications such as pattern recognition or robot learning. The power of this fascinating bionics branch of AI is demonstrated on some popular network models applied to various tasks.

Abstract

The challenging task of autonomously learning skills without the help of a teacher, solely based on feedback from the environment to actions, is called reinforcement learning. Still being an active area of research, some impressive results can be shown on robots. Reinforcement learning enables robots to learn motor skills as well as simple cognitive behavior. We use a simple robot with only two degrees of freedom to demonstrate the strengths of the value iteration and Q-learning algorithms, as well as their limitations.

Abstract

For all the exercises in the book we provide solutions. These solutions are intended to help those who actively work on the exercises to check the correctness of their solutions. In the spirit of Leonardo da Vinci’s saying “Studying without passion damages the brain!”, we want to encourage the reader to really work on the exercises before peeking into the solutions.