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By M. Tim Jones

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2: The depth-first search algorithm. isEmptyStack(s_p) ) { node = popStack( s_p ); printf(“%d\n”, node); if (node == goal) break; for (to = g_p->nodes-1 ; to > 0 ; to--) { if (getEdge( g_p, node, to ) ) { pushStack( s_p, to ); } } } destroyStack( s_p ); return; } int main() { graph_t *g_p; 33 34 Artificial Intelligence g_p = createGraph( 8 ); init_graph( g_p ); dfs( g_p, 0, 5 ); destroyGraph( g_p ); return 0; } A search algorithm is characterized as exhaustive when it can search every node in the graph in search of the goal.

27 Uninformed Search TREES, GRAPHS, AND REPRESENTATION A short tour of trees and graphs and their terminology is in order before exploring the various uninformed search methods. A graph is a finite set of vertices (or nodes) that are connected by edges (or arcs). A loop (or cycle) may exist in a graph, where an arc (or edge) may lead back to the original node. Graphs may be undirected where arcs do not imply a direction, or they may be directed (called a digraph) where a direction is implicit in the arc.

The only disk that may move is the small disk at the top of Peg A. For this disk, only two legal moves are possible, from Peg A to Peg B or C. From this state, there are three potential moves: 1. Move the small disk from Peg C to Peg B. 2. Move the small disk from Peg C to Peg A. 3. Move the medium disk from Peg A to Peg B. The first move (small disk from Peg C to Peg B), while valid is not a potential move, as we just moved this disk to Peg C (an empty peg). Moving it a second time serves no purpose (as this move could have been done during the prior transition), so there’s no value in doing this now (a heuristic).

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