### Table 2-1: Logic-Based Formalisms

1997

"... In PAGE 18: ... Table2 -2: Logic-Based Formalisms Criteria OBJ Larch Temporal Model none none Automated Tools few some Reliability good good Proof System axiomatic axiomatic Industrial Strength some great Methods of Veri. theorem prov.... ..."

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### Table 2-1: Logic-Based Formalisms

1997

"... In PAGE 3: ... Table2 -2: Logic-Based Formalisms Criteria OBJ Larch Temporal Model none none Automated Tools few some Reliability good good Proof System axiomatic axiomatic Industrial Strength some great Methods of Veri. theorem proving theorem proving Concurrency interleaved interleaved Communication sync.... ..."

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### Table 2-2: Logic-Based Formalisms

1997

"... In PAGE 18: ... theorem proving theorem proving both both Concurrency none none norm exist none Communication none none norm exist none Reverse Eng. yes yes no no Table2 -1: Logic-Based Formalisms Criteria ITL DC TAM RTTL RTL Temporal Model sparse dense sparse sparse sparse Automated Tools few none none few none Reliability good good good good good Proof System axiomatic axiomatic axiomatic axiomatic axiomatic Industrial Strength great some great some some Methods of Veri. theorem prov.... ..."

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### Table 2-2: Logic-Based Formalisms

1997

"... In PAGE 3: ... both both Concurrency none none norm exist none Communication none none norm exist none Reverse Eng. yes yes no no Table2 -1: Logic-Based Formalisms Criteria ITL DC TAM RTTL Temporal Model sparse dense sparse sparse Automated Tools few none none few Reliability good good good good Proof System axiomatic axiomatic axiomatic axiomatic Industrial Strength great some great some Methods of Veri. theorem pv.... ..."

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### Table 1. Illustrating the e ect of logic-based optimizations.

1997

"... In PAGE 9: ... The \leader5 quot; system corresponds to the system used in the SPIN suite. Table1 gives the space and time gures for two di erent formulas, F1 being a least xed point formula stating that in every run of the system a leader is eventually elected, and F2 being a nested xed point formula stating that in every run of the system at most one leader is elected. In this table, for a system of given size, the rst line indicates the space and time gures with the naive encoding without any of the optimizations of the previous section, and the second line gives the corresponding gures with all the optimizations in place.... ..."

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### Table 11 Accuracy comparison with logic-based relational classifiers (FOIL, Tilde, Lime, Progol), target features (TF), and using no relational information (Prop) as a function of training size on the IPO domain

2006

"... In PAGE 30: ... To illustrate, we compare (on the IPO domain) ACORA to four logic-based relational learners including FOIL (Quinlan amp; Cameron-Jones, 1993), TILDE (Blockeel amp; Raedt, 1998), Lime (McCreath, 1999), and Progol (Muggleton, 2001). Since ILP systems typically (with the exception of TILDE) only predict the class, not the probability of class membership, we compare in Table11 the accuracy as a function of training size. We also include as a reference point the classification performance of a propositional logistic model without any background knowledge (Prop).... In PAGE 30: ... For these results, the bank identifiers were not included as model constants. The results in Table11 demonstrate that the logic-based systems simply are not applicable to this domain. The class-conditional distribution features (CCVD) improve substantially over using no relational information at all (Prop), so there indeed is important relational information to consider.... In PAGE 30: ... Since TILDE is able to predict probabilities using the class frequencies at the leaves, we can compare (in Table 12) its AUC to our results from above.15 Based on these results we must conclude that except for the EBook and the IPO domain, TILDE could not generalize a classification model from the 15 On the IPO domain TILDE improved also in terms of accuracy over the performance without banks in Table11 from 0.... ..."

### Table 1. Characteristics of some Fuzzy Logic- based User Modeling applications. Application Training Data Outcome T I /G

in Modeling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Techniques

"... In PAGE 4: ...ystems: Recent Advances Using Soft Computing Technique. Expert Systems with Applications. 29(2) 4 case, FL provides a soft filtering process based on the degree of concordance between user preferences and the elements being filtered. Table1 summarizes relevant studies and applications of FL for UM. The columns detail the application, the data, the results obtained, the type of task (T) for which the SC technique was used, i.... ..."

### Table 1 gives some rules of inference for sequence logic based on a proof system turnstileleft for the underlying logic. This minimal proof system, denoted forcesmin, will be augmented with distinct rules depending on the class of orderings.

"... In PAGE 4: ... Table1 . Axioms and rules for forcesmin.... ..."

### Table 6. Stressed vs. unstressed discrimination: fuzzy-logic-based algorithm performance. S+ primary, S- minor stressed, N unstressed vocalic nuclei.

2000

"... In PAGE 14: ... This analysis is applied here to assess the role of the di erent input features in the implemented fuzzy classi cation process. In table 7 the information gain related to the use of the input features is reported for the systems with the average performance shown in Table6 . An information gain of 1:0 indicates perfect discriminability of the output classes along this input feature; linearly decreasing values of the information gain (eq.... ..."

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