The functions for a node n are: g(n)- the cost to reach the node (from the start) h(n) - the cost to get from the node to the goal often an estimate). f(n) - an evaluation function which is the sum of g(n) and h(n) Select the best choice for each informed search strategy. - A. linear _tries to expand the node that is closest to the goal, on the grounds that this is likely to lead to a solution quickly. Thus, it evaluates nodes by using just the heuristic function; that is, f (n) = h(n). B. A* search _evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost to get from the node to the goal: f(n)= g(n) +h(n). C. RBFS D. IDA* -_is identical to UNIFORM COST SEARCH except that this uses g(n) + h(n) instead of g(n). - is a simple recursive algorithm that attempts to mimic the operation of standard best-first search, but using E. memory-efficient only linear space. E overestimating _ expands nodes with minimal f(n). G. consistent _ is complete and optimal, provided that h(n) is admissible (for TREE-SEARCH) or consistent (for GRAPH-SEARCH). H. Greedy best-first search - expands nodes with minimal h(N). It is not optimal but is often efficient. I. admissible - If h(n) is__, then the values of f(n) along any path are nondecreasing. - _ is the simplest way to reduce memory requirements for A* is to adapt the idea of iterative deepening where the cutoff used is f(n) rather than the depth. - h(n) which is_ is one that never overestimates the cost to reach the goal. - because the shortest path between Straight-line distance (for example, in Romanian routing problem) is_ any two points is a straight line. - This condition is also called sometimes monotonicity. - A heuristic h(n) is if, for every node n and every successor n' of n generated by any action a, the estimated cost of reaching the goal from n is no greater than the step cost of getting to n' plus the estimated cost of reaching the goal from n'