### Table 2 : Complete Partial Ordering of Pr(bcdjAE)

### Table 4: Subproblem Re nement t1 in sub by steps of . sol is the routine for picking solution candidates from the candidate set of the partial subplan. In case sol does not return a solution, the algorithm fails for the current branch or is continued, respectively. As island planning, subproblem re nement is a multistep planning with nested planning strategies because it involves inserting the completed subplan into . This multistep planning is all right for proof planning. In order to avoid multiple steps in subproblem planning for other domains, an alternative subproblem re nement algorithm involves 1. Pick subproblem Psub with current plan sub. 2. Re ne sub. 3. Propagate auxiliary, binding constraints, and order constraints of sub to by a propagate-constraints routine.

### Table 2. The number of completely or partially correct sequences computed by PEAKS and Lutefisk.

2003

"... In PAGE 7: ...able 1. The performance of PEAKS and Lutefisk on Albumin (bovine) MS/MS data set. The spectrum quality column s/m shows the average signal intensity of each spectrum. For the 54 MS/MS spectra, Table2 gives the numbers of sequences that PEAKS and Lutefisk computed completely correct or partially correct (with at least 6 consecutively correct amino acids). It can be seen that PEAKS performs better than Lutefisk on these 54 spectra.... In PAGE 8: ... Finally, we want to point out that all of the wrongly assigned amino acids by PEAKS are caused by mass equivalence. Some examples in Table2 are: mass (SL) = mass(TV) in precursor ... ..."

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### Table 1. Completely exploring state spaces with BFS and several reduction methods.

2006

"... In PAGE 13: ... The main question to investigate is therefore how C2c performs in comparison to C2s. Table1 depicts results obtained by completely exploring the state space of some models using BFS as search algorithm in combination with various reduction methods: no partial-order reduction at all (no), no action ignoring prevention (C2i), C2v, C2s and C2c. Note that C2i leads to an unsound reduction.... In PAGE 14: ... Completely exploring state spaces with BFS and several reduction methods. By comparing the two previous sets of experiments we observe the following phenomenon: in model marriers, algorithm BFS with C2c explores as many states as BFS with C2i ( Table1 ), while A* with C2c explores almost twice the states than A* with C2i (Table 2). In other words, the C2c proviso is refuting ample sets when the search algorithm is A* but not when it is BFS.... ..."

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### Table 1: Results of Generalized Partial Order Analysis (GPO)

1998

"... In PAGE 7: ... A numeric overview of the results is shown in Table 1. In Table1 the number of states of the complete reacha- bility graph, the number of states of the reachability graphs derived using (generalized) partial-order analysis, as well the peak BDD sizes encountered during symbolic reacha- bility analysis, are listed for various instances of the param- eterized examples. CPU times, measured on a HP K260 with 896 MB RAM, are also included.... ..."

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### Table 1: Tractable classes of the point algebra for partially ordered time [5].

"... In PAGE 19: ... xky i neither x y nor y x The point algebra for partially ordered time has been throughly investigated earlier and a total classi cation with respect to tractability has been given in Broxvall and Jonsson [4]. In Broxvall and Jonsson [5] the sets of relations in Table1 are de ned and it is proven that ?A _ A, ?B _ B, ?C _ C and D are the unique maximal tractable disjunctive classes of relations for partially ordered time. The proofs of tractability for those sets relied on a series of handmade independence proofs.... ..."

### Table 1. Coefficients for DC component: complete versus partial scan data. The pure DC component is degraded by 50% in addition to higher orders that are introduced by zero filling for unmeasured transducer locations. Complete

2004

### Table 1. Comparing the performance of four symbolic partial order reduction techniques

"... In PAGE 13: ... We also set the number of threads to 2, 3, 4 for both dining philosopher and indexer examples and compared the four methods. The detailed results are given in Table1 . In Table 1, Columns 1-3 show the name of the examples, the number of BMC unrolling steps, and whether the property is true or not.... In PAGE 13: ... The detailed results are given in Table 1. In Table1 , Columns 1-3 show the name of the examples, the number of BMC unrolling steps, and whether the property is true or not. Columns 4-7 report the runtime of the four methods.... ..."

### Table 2. Effect of the partial instances

2003

"... In PAGE 4: ... The usefulness of such justified rejections can be measured by providing our learner with partial instances. In the fol- lowing experiment ( Table2 ), the teacher provides the learner with 90 partial negative instances (after 10 complete positive ones) in the training data. We consider partial in- stances involving 2, 5, 10 variables, and report the size of the version space and of the set of clauses (effective space used to represent the general bound) after 100 instances have been given.... ..."

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### Table 5. Partial IDA* Algorithm.

2001

"... In PAGE 12: ...6 Heuristic Search Algorithm The apparent aspirant for state compaction is IDA* with transposition tables, since, in opposite to A*, it tracks the solution path on the stack, which allows to omit the predecessor link in the state description of the set of visited states. When substituting the transposition table H of already visited nodes in IDA* by bit-state, multi bit-state or hash compaction we establish the Partial IDA* algorithm as depicted in Table5 . Since neither the predecessor nor the f-value are present, in order to distinguish the current iteration from the previous ones, the bit-state table has to be re-initialized in each iteration of IDA*.... ..."

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