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.
semantic concept of entailment and syntactic implication. Theorem 2.2 (Deduktionstheorem) Proof Observe the truth table for implication: A B A⇒B t t t t f f f t t f f t An arbitrary implication A⇒B is clearly always true except with the interpretation A↦t,B↦f. Assume that holds. This means that for every interpretation that makes A true, B is also true. The critical second row of the truth table does not even apply in that case. Therefore A⇒B is true, which means
describe the definition of plan in logic: This definition comes out significantly more concise than in PROLOG. There are two reasons for this. For one thing, the output of the discovered plan is unimportant for logic. Furthermore, it is not really necessary to check whether the next state was already visited if unnecessary trips do not bother the farmer. If, however, \+ member(...) is left out of the PROLOG program, then there is an infinite loop and PROLOG might not find a schedule even if
for the Braitenberg vehicle. In the Encyclopedia Britannica [Bri91] one finds a Definition that goes like: AI is the ability of digital computers or computer controlled robots to solve problems that are normally associated with the higher intellectual processing capabilities of humans … But this definition also has weaknesses. It would admit for example that a computer with large memory that can save a long text and retrieve it on demand displays intelligent capabilities, for memorization of
take on the values green, blue, brown. eye_color=blue then describes an event because we are dealing with a proposition with the truth values t or f. For binary (boolean) variables, the variable itself is already a proposition. Here it is enough, for example, to write P(JohnCalls) instead of P(JohnCalls=t). Example 7.3 By this definition, the probability of rolling an even number is The following important rules follow directly from the definition. Theorem 7.1 1. P(Ω)=1. 2. P(∅)=0, which
to optimally, even with modern high-level languages such as PROLOG and Python. 1 Machine learning algorithms are even used today to program robots in a way similar to how humans learn (see Chap. 10 or [BCDS08, RGH+06]), often in a hybrid mixture of programmed and learned behavior. The task of this chapter is to describe the most important machine learning algorithms and their applications. The topic will be introduced in this section, followed by important fundamental learning algorithms in the