The correct answer is: Both RBFS & SMA* will use limited amount of memory.
A Breadth-First Search (BFS) algorithm starts at the root node and expands all of its neighbors before expanding any other nodes. This means that it only needs to keep track of the nodes that it has already visited, which can be stored in a queue. A Depth-First Search (DFS) algorithm, on the other hand, starts at the root node and expands all of its neighbors before returning to the parent node. This means that it needs to keep track of the nodes that it has already visited, as well as the path that it has taken to reach those nodes. This can be stored in a stack.
A Best-First Search (BFS) algorithm is a type of search algorithm that expands the node with the highest estimated cost-to-go first. This means that it needs to keep track of the estimated cost-to-go for each node, as well as the path that it has taken to reach those nodes. This can be stored in a priority queue.
A Hill-climbing search algorithm is a type of search algorithm that starts at a random node and then moves to the neighboring node with the lowest cost. This means that it only needs to keep track of the current node and the neighboring nodes. This can be stored in a list.
A Uniform-Cost Search (UCS) algorithm is a type of search algorithm that expands the node with the lowest estimated cost first. This means that it needs to keep track of the estimated cost for each node, as well as the path that it has taken to reach those nodes. This can be stored in a priority queue.
A Branch-and-Bound (B&B) algorithm is a type of search algorithm that divides the search space into smaller and smaller subspaces until the goal state is found. This means that it needs to keep track of the current subspace, as well as the subspaces that have already been explored. This can be stored in a tree.
A Simulated Annealing (SA) algorithm is a type of search algorithm that starts at a random node and then moves to neighboring nodes with a probability that depends on the difference in cost between the current node and the neighboring node. This means that it only needs to keep track of the current node and the neighboring nodes. This can be stored in a list.
A Genetic Algorithm (GA) is a type of search algorithm that starts with a population of random solutions and then iteratively selects the best solutions to create the next generation of solutions. This means that it needs to keep track of the current population of solutions, as well as the best solutions that have been found so far. This can be stored in a list.
A Tabu Search (TS) algorithm is a type of search algorithm that starts at a random node and then moves to neighboring nodes that are not in the tabu list. The tabu list is a list of nodes that have been visited recently. This means that it needs to keep track of the current node, the neighboring nodes, and the tabu list. This can be stored in a list, a hash table, or a graph.
A Constraint Satisfaction Problem (CSP) is a problem that can be modeled as a set of constraints on a set of variables. A solution to a CSP is an assignment of values to the variables that satisfies all of the constraints. A backtracking algorithm is a type of search algorithm that can be used to solve CSPs. Backtracking starts at a random assignment of values to the variables and then backtracks when it finds a constraint that is violated. This means that it needs to keep track of the current assignment of values to the variables, as well as the constraints that have been violated. This can be stored in a list, a hash table, or a graph.
A Dynamic Programming (DP) algorithm is a type of search algorithm that can be used to solve problems that have overlapping subproblems. A DP algorithm stores the solutions to the subproblems so that they can be reused when solving the larger problem. This means that it needs to keep track of the solutions to the subproblems. This can be stored in a table, a hash table, or a graph.
A Greedy Algorithm is a type of search algorithm that always chooses the best option at each step. This means that it only needs to keep track of the current option and the best option that has been found so far. This can be stored in a list, a hash table, or a graph.
A Heuristic Algorithm is a type of search algorithm that uses a heuristic function to estimate the cost of reaching the goal state from the current state. This means that it needs to keep track of the current state, the goal state, and the heuristic function. This can be stored in a list, a hash table, or a graph.
A Local Search Algorithm is a type of search algorithm that starts at a random state and then moves to