Consider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True False

Consider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True False

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter14: Game Theory

Section14.3: Linear Programming And Zero-sum Games

Problem 9P

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Consider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True False
Consider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True False The rational agent always perform the optimal action Select one: True False Fuzzy logic is useful for both commercial and practical purposes. Select one: True False
True or False: - Best-first search is optimal in the case where we have a perfect heuristic (i.e., h(?) = h∗(?), the true cost to the closest goal state). - Suppose there is a unique optimal solution. Then, A* search with a perfect heuristic will never expand nodes that are not in the path of the optimal solution.- A* search with a heuristic which is admissible but not consistent is complete.
Please answer the following question in full detail. Please be specifix about everything: You have learned before that A∗ using graph search is optimal if h(n) is consistent. Does this optimality still hold if h(n) is admissible but inconsistent? Using the graph in Figure 1, let us now show that A∗ using graph search returns the non-optimal solution path (S,B,G) from start node S to goal node G with an admissible but inconsistent h(n). We assume that h(G) = 0. Give nonnegative integer values for h(A) and h(B) such that A∗ using graph search returns the non-optimal solution path (S,B,G) from S to G with an admissible but inconsistent h(n), and tie-breaking is not needed in A∗.
What is optimality with respect to search algorithms? Select one: a. If there is a distinction between the quality of goal states, the agent will find the best one. b. Provided that a solution exists, the algorithm will find it c. An estimate of how much information the algorithm needs to store for finding a solution d. An estimate of how many search steps it takes to find a solution.
Using the image provided, please answer the following questions. (a). Find a path from a to g in the graph G using the search strategy of depth-first search. Is the returned solution path an optimal one? Give your explanation and remarks on "why-optimal" or "why-non-optimal". (b). Find a path from a to g in the graph G using the search strategy of breadth-first search. Is the returned solution path an optimal one? Give your explanation and remarks on "why-optimal" or "why-non-optimal".(c). Find a path from a to g in the graph G using the search strategy of least-cost first search. Is the returned solution path an optimal one? Give your explanation and remarks on "why-optimal" or "why-non-optimal". (d). Find a path from a to g in the graph G using the search strategy of best-first search. The heuristics for these nodes are: h(a,25); h(b, 43); h(c,5); h(d, 64); h(g, 0). Is the returned solution path an optimal one? Give your explanation and remarks on "why-optimal or "why-non-optimal".…
Suppose we have a heuristic h that over-estimates h* by at most epsilon (i.e., for all n, 0<= h(n) <= h*(n)+epsilon). Show that A* search using h will get a goal whose cost is guaranteed to be at most epsilon more than that of the optimal goal.
Insufficient Overlapping Subproblems and the Principle of Optimality (Optimal Substructure) are both acceptable queries for dynamic programming. Consider All-Pairs Shortest Paths as an illustration of the difficulty in achieving these two requirements.
If a Genetic Algorithm only finds local optimal solutions, what should be done to find a better one globally?
The heuristic path algorithm is a best-first search in which the objective function is f(n)= 3w*g(n) + (2w+1) * h(n), 0≤w<3. For what values of w is this algorithm guaranteed to be optimal?
Insufficient Overlapping Subproblems and the Principle of Optimality (Optimal Substructure) are dynamic programming questions. As an example, consider All-Pairs Shortest Paths.
Question 15 pap In class we considered the computational complexity of global localization as a hypothesis elimination problem in an idealized setting. What is this problem (briefly), how difficult is it to get an optimal solution (i.e. do you know the complexity?), and what kind of heuristic can be used to achieve a solution?