Auto Topic: rbfs
auto_rbfs | topic
Coverage Score
1
Mentioned Chunks
14
Mentioned Docs
2
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
rbfs
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: rbfs | CO_OCCURS | State-Space Search | 6 |
| Auto Topic: rbfs | CO_OCCURS | Informed Search | 5 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: rbfs | 5 |
| Auto Topic: rbfs | CO_OCCURS | Heuristic Function | 4 |
| Auto Topic: rbfs | CO_OCCURS | Auto Topic: unwinds | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 7 | rsion unwinds back to the alternative path. As the recursion unwinds, RBFS replaces the f -value of each node along the path with a backed-up value—the best f -value of its children. In this way, RBFS remembers Backed-up value the f -value of the best leaf in the forgotten subtre ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... w contour might contain only one new node, and the number of iterations could be equal to the number of states. Recursive best-first search (RBFS) (Figure 3.22) attempts to mimic the operation of Recursive best-first search standard best-first search, but using only linear space. RB ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... xpansions of forgotten nodes to recreate the best path and extend it one more node. Section 3.5 Informed (Heuristic) Search Strategies 113 RBFS is optimal if the heuristic function h(n) is admissible. Its space complexity is linear in the depth of the deepest optimal solution, bu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | e current f -cost limit. RBFS retains more information in memory, but it uses only linear space: even if more memory were available, RBFS has no way to make use of it. Because they forget most of what they have done, both algorithms may end up reexploring the same states many tim ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... lysis of the efficiency of IDA∗ and of its difficulties with real-valued heuristics appears in Patrick et al. (1992). The original version of RBFS (Korf, 1993) is actually somewhat more complicated than the algorithm shown in Figure 3.22, which is actually closer to an independentl ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... owest f -value if best. f > f limit then return failure, best. f alternative ←the second-lowest f -value among successors result, best. f ← RBFS( problem, best, min( f limit,alternative)) if result ̸= failure then return result, best. f Figure 3.22 The algorithm for recursive bes ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... wal, A., 137, 161, 1106 Ray, A., 966, 986, 1085 Ray, B., 737, 1097 Rayson, P., 903, 1103 Rayward-Smith, V ., 126,1110 Razavi, A., 838, 1100 RBFS (recursive best-first search), 111–113, 123 RDF (Resource Description Framework), 357 R´e, C., 665, 1092 reachable set, 379 reached (sta ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ilcea and expanding Pitesti (b) After unwinding back to Sibiu and expanding Fagaras 447 447 ∞ ∞ ∞ 417 417 Pitesti Figure 3.23 Stages in an RBFS search for the shortest route to Bucharest. The f -limit value for each recursive call is shown on top of each current node, and every n ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... A∗ itself. – IDA∗ (iterative deepening A∗ search) is an iterative deepening version of A ∗, and thus adresses the space complexity issue. – RBFS (recursive best-first search) and SMA∗ (simplified memory-bounded A∗) are robust, optimal search algorithms that use limited amounts of m ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | terative expansion well as the upper bound; the two algorithms behave identically with admissible heuristics, but RBFS expands nodes in best-first order even with an inadmissible heuristic. The idea of Bibliographical and Historical Notes 127 keeping track of the best alternative ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... r ex- pands more nodes than alpha–beta. The memory requirements make it impractical, but a linear-space version has been developed from the RBFS algorithm (Korf and Chickering, 1996). Baum and Smith (1997) propose a probability-based replacement for minimax, show- ing that it res ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... wal, A., 137, 161, 1106 Ray, A., 966, 986, 1085 Ray, B., 737, 1097 Rayson, P., 903, 1103 Rayward-Smith, V ., 126,1110 Razavi, A., 838, 1100 RBFS (recursive best-first |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 162–163 partially observable, 144–152 policy, 841, 861, 861–863 problem, 949 quiescence, 204 real-time, 145, 202–206 recursive best-first (RBFS), 111–113, 126 simulated annealing, 132–133 stochastic beam, 133 systematic, 93 tabu, 160, 182 tree, 89, 193 uniform-cost, 95, 95–96, 12 ... |
practice_exam Practice Exam/cis5210_practice_1_blank.pdf | 0.55 | 1 | ... are extended from A* to reduce memory requirements and can use all available memory? (Check all that apply) □IDA* (iterative-deepening A*) □RBFS (Recursive best-first search) □MA* (memory-bounded A*) □SMA* (Simplified MA*) CIS 4210/5210 - Practice Midterm 1 Page 8 of 17 Your name ... |