### Table 1. Algorithm of provider-hosts selection

2003

"... In PAGE 7: ... According to the location criterion, the provider-agent that will be associated with the P-service p:si (i2[2;p]) depends on the provider-agent that has been se- lected to o er the predecessor P-service p:si 1. The user-agent proceeds accord- ing to the algorithm of Table1 (it is assumed that lt; p:si 1; pro:agti 1; type gt; is established). When the user-agent nishes working on the P-service p:si, the provider-agent and invocation strategy of that P-service are known.... ..."

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### Table 1. Schematic representation of the algorithms underlying interval constraint solving (left) vs. basic DPLL SAT (right). The close analogy suggests a tight integration into a DPLL-style algo- rithm manipulating large Boolean combinations of arithmetic formulae via a homogeneous treat- ment of Boolean and arithmetic parts.

in Efficient solving of large non-linear arithmetic constraint systems with complex boolean structure

2007

"... In PAGE 8: ... 4. Integrating interval constraint propagation and SAT As can be seen from Table1 , branch-and-prune algorithms based on interval constraint propagation (ICP) with interval splitting and the core algorithm of DPPL SAT solving share a similar structure. This similarity motivates a tighter integration of propositional SAT and arithmetic reasoning than in classical lazy theorem proving.... ..."

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### Table 2. Effect of Caching on Performance of DPLL

2004

"... In PAGE 12: ... However, on other instances, especially the flat75, flat100, and iscas89 families, performance dropped significantly after caching was turned off. See the results in Table2 . Note that CUDD does better than DPLL on the iscas89 family overall, but not on all the 18 instances.... In PAGE 12: ... Note that CUDD does better than DPLL on the iscas89 family overall, but not on all the 18 instances. In particular, all of those included in Table2 are instances where DPLL outperforms CUDD. We investigate next the performance of DPLL and CUDD on randomly gen- erated 3-CNFs with varying clauses-to-variables ratios.... ..."

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### Table 5 Tested selection strategies

"... In PAGE 13: ...ebsite introduced in Sect. 5.2. For comparison purposes, we also evaluated one set of manually specified rules and a random recommender selection giving a total of six ap- proaches (see Table5 ). For every user session AWESOME randomly selected one of the strategies; the chosen strategy is additionally recorded in the recommendation log file for evaluation purposes.... ..."

### Table 1. The tag selection strategies

"... In PAGE 8: ...Table1... ..."

### Table 1. Parameter setting for the algorithms that is used in all problems. The fltness sharing, selection and elitist strategy listed here is not used in the implementation of CNSGA and CCCNSGA.

2004

"... In PAGE 5: ...2 and the co-operative co-evolutionary multi-objective optimisation algorithms (CC- MOAs) to solve T1{T6 problems and the topology design problem will be pre- sented. The parameter setting for the algorithms that is used in all problems is displayed in Table1 . It is noted that after all repeated runs have completed, the flnal non-dominated solutions are then retrieved from either the individuals in the last generation from each run of a EMOA or the solutions in the preserved non-dominated solution set from every CCMOA run.... ..."

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### Table 3 Model selection strategy applied to classiFFcation tasks. Algorithm K-NN. (16K 615)

2002

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### Table 3: Algorithm for belief expansion with random action selection

2006

"... In PAGE 15: ... To sample over the simplex, we cannot simply sample each b(s) independently over [0, 1] (this would violate the constraint that summationtext s b(s) = 1). Instead, we use the algorithm described in Table3 (see Devroye, 1986, for more details including proof of uniform coverage). This random point selection strategy, unlike the other strategies presented below, does not focus on reachable beliefs.... ..."

Cited by 2

### Table 3: Algorithm for belief expansion with random action selection

2006

"... In PAGE 15: ... To sample over the simplex, we cannot simply sample each b(s) independently over [0, 1] (this would violate the constraint that summationtext s b(s) = 1). Instead, we use the algorithm described in Table3 (see Devroye, 1986, for more details including proof of uniform coverage). This random point selection strategy, unlike the other strategies presented below, does not focus on reachable beliefs.... ..."

Cited by 2