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By M. Tim Jones

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Instead, the direction of the arcs is noted in the matrix. For example, node B connects to node A, but node A has no associated connection to node B. An interesting property of the adjacency matrix can be found by reviewing the rows and columns in isolation. For example, if we review a single row, we can identify the nodes to which it connects. For example, row C shows only a connection to node F (as indicated by the one in that cell). But if we review the column for node C, we find the nodes that have arcs connecting to node C.

7. What event is most commonly attributed to leading to AI’s winter? 8. What is meant by Scruffy and Neat approaches to AI? 9. After AI’s winter, what was most unique about AI’s re-emergence? 10. This book explores AI from the systems approach. Define the systems approach and how this perspective is used to explore AI. Chapter 2 UNINFORMED SEARCH U ninformed search, also called blind search and naïve search, is a class of general purpose search algorithms that operate in a bruteforce way. These algorithms can be applied to a variety of search problems, but since they don’t take into account the target problem, are inefficient.

1. 1: Common orders of search functions. ) Linear (consistent with the number of nodes) Logarithmic Quadratic Geometric Combinatorial Big-O notation provides a worst-case measure of the complexity of a search algorithm and is a common comparison tool for algorithms. We’ll compare the search algorithms using space complexity (measure of the memory required during the search) and time complexity (worst-case time required to find a solution). We’ll also review the algorithm for completeness (can the algorithm find a path to a goal node if it’s present in the graph) and optimality (finds the lowest cost solution available).

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