Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science)
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This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.
=(0.34,0.13,0.06,0.12,0.15,0.054,0.05,0.1) P=(0.4,0.07,0.08,0.1,0.09,0.11,0.03,0.12) (original distribution) Quadratic distance: d q (P a ,P)=0.0029, d q (P b ,P)=0.014 Kullback–Leibler dist.: d k (P a ,P)=0.017, d k (P b ,P)=0.09 Both distance metrics show that the network (a) approximates the distribution better than network (b). This means that the assumption that Prec and Sky are conditionally independent given Bar is less likely true than the assumption that Sky and Bar are
Chang and R. C. Lee. Symbolic Logic and Mechanical Theorem Proving . Academic Press, Orlando, 1973. [Cle79] W. S. Cleveland. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. , 74(368):829–836, 1979. [CLR90] T. Cormen, Ch. Leiserson, and R. Rivest. Introduction to Algorithms . MIT Press, Cambridge, 1990. [CM94] W. F. Clocksin and C. S. Mellish. Programming in Prolog . Springer, Berlin, 4th edition, 1994. [Coz98] F. G. Cozman. Javabayes, bayesian
processes. Intelligent systems, in the sense of Rich’s definition, cannot be built without a deep understanding of human reasoning and intelligent action in general, because of which neuroscience (see Sect. 1.1.1) is of great importance to AI. This also shows that the other cited definitions reflect important aspects of AI. A particular strength of human intelligence is adaptivity. We are capable of adjusting to various environmental conditions and change our behavior accordingly through
another door, e.g. number three, and a goat appears. The contestant is now given the opportunity to choose between the two remaining doors (one and two). What is the better choice from his point of view? To stay with the door he originally chose or to switch to the other closed door? Exercise 7.5 Using the Lagrange multiplier method, show that, without explicit constraints, the uniform distribution p 1=p 2=⋅⋅⋅=p n =1/n represents maximum entropy. Do not forget the implicitly ever-present
can extend the concept of entropy to data by the definition Now, since the information content I(D) of the dataset D is meant to be the opposite of uncertainty. Thus we define: Definition 8.5 The information content of a dataset is defined as (8.6) 8.4.3 Information Gain If we apply the entropy formula to the example, the result is During construction of a decision tree, the dataset is further subdivided by each new attribute. The more an attribute raises the information content of the