Admissible Heuristic
admissible_heuristic | topic
Coverage Score
1
Mentioned Chunks
42
Mentioned Docs
4
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
never overestimateslower bound
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Admissible Heuristic | CO_OCCURS | Informed Search | 32 |
| Admissible Heuristic | CO_OCCURS | State-Space Search | 20 |
| Admissible Heuristic | CO_OCCURS | Algorithm Evaluation Criteria | 16 |
| Admissible Heuristic | CO_OCCURS | Problem Formulation | 13 |
| Admissible Heuristic | CO_OCCURS | Heuristic Function | 12 |
| Admissible Heuristic | CO_OCCURS | Inference | 8 |
| A* Search | CO_OCCURS | Admissible Heuristic | 6 |
| Admissible Heuristic | CO_OCCURS | Consistent Heuristic | 5 |
| Admissible Heuristic | CO_OCCURS | Bidirectional Search | 4 |
| Admissible Heuristic | CO_OCCURS | Uniform-Cost Search | 3 |
| Admissible Heuristic | CO_OCCURS | Constraint Satisfaction Problem | 3 |
| Admissible Heuristic | CO_OCCURS | Alpha-Beta Pruning | 3 |
| Admissible Heuristic | SUPPORTS_PROPERTY | A* Search | 1 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
module_resources Module Resources/Module-3---Resources (1)_unzipped/04-Informed-Search.pptx.pdf | 0.71 | 3 | ... Frontier queue as priority queue by increasing f(n) (as expected…) Key concept: Admissible heuristics A heuristic h(n) is admissible if it never overestimates the cost to reach the goal; i.e. it is optimistic • Formally:∀n, n a node: • h(n) ≤ h*(n)where h*(n) is the true cost fro ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.69 | 2 | ... obtained from the 1-2-3-4 database and the 5- 6-7-8 could be added, since the two subproblems seem not to overlap. Would this still give an admissible heuristic? The answer is no, because the solutions of the 1-2-3-4 subproblem and the 5-6-7-8 subproblem for a given state will al ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.68 | 5 | h more restrictions on the actions that can be taken. □Substituting an admissible heuristic function,h3, which dominatesh1. □Substituting an admissible heuristic function,h4, such thath4(n)< h1(n) for any noden. 21. (1 point) Which of the following algorithms are extended from A* ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 3 | ... on the goal, and bidirectional search has the same asymptotic complexity as A∗. Bidirectional search with the f2 evaluation function and an admissible heuristic h is complete and optimal. 3.6 Heuristic Functions In this section, we look at how the accuracy of a heuristic affects ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.64 | 3 | ... 5. Which of the following modifications would result in more efficient A* algorithm performance? (Check all that apply.) □Substituting an admissible heuristic function,h2, that has ab∗value of 1.5. □Creating a new problem with more restrictions on the actions that can be taken. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.62 | 2 | ... n. A∗ search is complete. 11 Whether A∗ is cost-optimal depends on certain properties of the heuristic. A key property is admissibility: an admissible heuristic is one that neverAdmissible heuristic overestimates the cost to reach a goal. (An admissible heuristic is therefore opt ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.62 | 2 | ose tiles do count toward the solution cost of the subproblem.) Then we compute an admissible heuristic hDB for each state encountered during a search simply by looking up the corresponding subproblem configuration in the database. The database itself is constructed by searching b ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.62 | 2 | ... tion. Recall from Chapter 3 that a heuristic function h(s) estimates the distance from a state s to the goal, and that if we can derive an admissible heuristic for this distance—one that does not overestimate—then we can use A ∗ search to find optimal solutions. By definition, ther ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.62 | 2 | ... fact: solving any subset of a belief state is necessarily easier than solving the belief state: if b1 ⊆ b2 then h∗(b1) ≤ h∗(b2). Hence, any admissible heuristic computed for a subset is admissible for the belief state itself. The most obvious candidates are the singleton subsets, ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ic overestimates the cost to reach a goal. (An admissible heuristic is therefore optimistic.) With 11 Again, assuming all action costs are> ϵ> 0, and the state space either has a solution or is finite. Section 3.5 Informed (Heuristic) Search Strategies 105 (a) The initial state (b ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... en the single number h(n) will be less than the sum of the cost c(n,a,a′) of the action from n to n′ plus the heuristic estimate h(n′). an admissible heuristic, A∗ is cost-optimal, which we can show with a proof by contradiction. Suppose the optimal path has costC∗, but the algor ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... fortunately there is no guarantee that this would lead to an optimal-cost solution, nor that it would be optimally efficient, even with an admissible heuristic. With bidirectional search, it turns out that it is not individual nodes but rather pairs of nodes (one from each fronti ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... on the goal, and bidirectional search has the same asymptotic complexity as A∗. Bidirectional search with the f2 evaluation function and an admissible heuristic h is complete and |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | placed tiles (blank not included). For Figure 3.25, all eight tiles are out of position, so the start state has h1 = 8. h1 is an admissible heuristic because any tile that is out of place will require at least one move to get it to the right place. 116 Chapter 3 Solving Problems ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... problem may have better solutions if the added edges provide shortcuts. Hence, the cost of▶ an optimal solution to a relaxed problem is an admissible heuristic for the original problem. Furthermore, because the derived heuristic is an exact cost for the relaxed problem, it must o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... the purposes of solving the subproblem, but moves of those tiles do count toward the solution cost of the subproblem.) Then we compute an admissible heuristic hDB for each state encountered during a search simply by looking up the corresponding subproblem |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | ... rer might know how its movement actions work and be ignorant only of the locations of obstacles. Finally, the agent might have access to an admissible heuristic functionh(s) that estimates the distance from the current state to a goal state. For example, in Figure 4.19, the agent ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.6 | 1 | a factored representation for states and ac- tions, which makes it possible to define good domain-independent heuristics. Recall that an admissible heuristic can be derived by defining a relaxed problem that is easier to solve. The exact cost of a solution to this easier problem th ... |