Introduction to Artificial Intelligence, 1st Edition by Mariusz Flasiński (auth.)

By Mariusz Flasiński (auth.)

In the chapters partially I of this textbook the writer introduces the basic rules of man-made intelligence and computational intelligence. partly II he explains key AI tools corresponding to seek, evolutionary computing, logic-based reasoning, wisdom illustration, rule-based structures, development popularity, neural networks, and cognitive architectures. ultimately, partly III, he expands the context to debate theories of intelligence in philosophy and psychology, key purposes of AI platforms, and the most probably way forward for man made intelligence. A key function of the author's method is old and biographical footnotes, stressing the multidisciplinary personality of the sector and its pioneers.

The e-book is acceptable for complex undergraduate and graduate classes in desktop technological know-how, engineering, and different technologies, and the appendices supply brief formal, mathematical types and notes to aid the reader.

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K5 (b) K2 K4 K1 K7 K3 K6 Fig. 5 Search for Constraint Satisfaction Problems 47 problem is represented by the last assignment to this variable in the search tree. For example, in the left subtree starting from the assignment of B to K4, we have tried to ascribe all the possible values to K7 for all combinations of colors for K6. However, we have had to backtrack from the whole subtree. This has resulted from an incorrect assignment of B to K4. ) Only when we have changed this assignment to K4 = S (the right subtree) do we find a solution, completed when the variable K7 has received the value B.

The probabilistic nature of evolutionary computing is its essential feature. 8 As we have mentioned in Chap. 1, there are four basic groups of methods within this approach: genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. , the fundamental role of crossover in genetic algorithms and genetic programming, the fundamental role of mutation in evolution strategies, and the occurrence of only mutation in evolutionary programming) and the way of generating a new population.

K1 K3 = ! K1 B K7 = {B,S,C} B S K7 = {B,C} B S K7 = {B,C} K7 = {C} B K4 = K5 = K7 = {B} S C B S C B S ! K1 ! K2 K7 = Ø ! K1 ! K2 K6 = B S K7 = Ø K7 = B (b) K1 = K2 = ! K1 x K5 = {S,C} S K5 = {C} K3 = {S} K7 = {B} S K3 = K4 = B B B S ! K7 K5 = K6 = C B S ! K1 ! K2 B S ! K7 K7 = B Fig. 10 Search tree for the constraint satisfaction problem: a for the forward checking search method, b for the constraint propagation method As we can see in Fig. , as early as on the second level of the tree) we are able to determine admissible assignments for three variables that reduce the search tree considerably.

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