By Mariusz Flasiński (auth.)
In the chapters partially I of this textbook the writer introduces the elemental principles of man-made intelligence and computational intelligence. partly II he explains key AI equipment akin to seek, evolutionary computing, logic-based reasoning, wisdom illustration, rule-based structures, trend attractiveness, neural networks, and cognitive architectures. eventually, partially III, he expands the context to debate theories of intelligence in philosophy and psychology, key functions of AI platforms, and the most likely way forward for synthetic intelligence. A key function of the author's method is ancient and biographical footnotes, stressing the multidisciplinary personality of the sphere and its pioneers.
The e-book is suitable for complex undergraduate and graduate classes in machine technological know-how, engineering, and different technologies, and the appendices provide brief formal, mathematical versions and notes to aid the reader.
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Additional info for Introduction to Artificial Intelligence
For example, if finding the smallest road distance between cities is our problem, then a heuristic function, which gives a straight-line distance between cities is admissible, because it never overestimates the actual distance. This property is very important because a heuristic function, 17 A good example of hill climbing is a situation when we want to reach the top of a mountain. However, we have neither a map nor a compass, we are here at night, and a dense fog hangs over the mountains. We have only an altimeter.
For example, the rank of the root node A equals 2, because this node has two child nodes (labeled B and C). The rank of any leaf node equals 0. One can easily notice in Fig. 3a that there are four possible routes to the exit (the state I marked with a double border),9 namely A-B-C-E-D-F-I, A-B-D-F-I, AC-B-D-F-I, and A-C-E-D-F-I. (The reader can compare it with the labyrinth shown in Fig. ) The remaining leaf nodes do not represent a solution and can be divided into two groups. The first group represents cul-de-sacs: G and H (cf.
7. Let us assume that we evaluate successors of the root. After evaluating the left subtree, its root has obtained the value α = 4. α denotes the minimum score that the maximizing player is assured of. Now, we begin an evaluation of the middle subtree. Its first leaf has obtained the value 2, so temporarily its predecessor also receives this value. Let us notice that if any leaf, denoted by X, has a value greater than 2, then the value of its predecessor is still equal to 2 (we minimize). On the other hand, if the value of any leaf X is less than 2, then it does not make any difference for our evaluation, because 2 is less than α —the value of the neighbor of the predecessor.
Introduction to Artificial Intelligence by Mariusz Flasiński (auth.)