"... In PAGE 5: ... Table1 : Additional assistance provided by Echo. Echo for Adaptive Decision Aid In the visitor hosting domain described earlier, each problem consists of 1) a set of domain features that describe the situation and 2) a set of decisions that should be made to solve the problem.... ..."

"... In PAGE 8: ...Table 4: Tool generation capabilities of representative language development systems. 3 Existing Language Development Systems Table4 summarizes the tool generation capabilities of the representative lan- guage development systems listed in Table 2. All of them can generate lexical scanners, parsers, and prettyprinters, many of them can produce syntax-directed editors, typecheckers, and interpreters, and a few can produce various kinds of software renovation tools.... In PAGE 9: ... It can generate a typechecker, an interpreter, and a debugger. Table4 is far from complete. Some other language development systems are SIS [30], PSP [32], GAG [24], SPS [41], MESS [28], Actress [11], Pregmatic [10], LDL [20], and Eli [26].... ..."

"... In PAGE 8: ...Table 4: Tool generation capabilities of representative language development systems. 3 Existing Language Development Systems Table4 summarizes the tool generation capabilities of the representative lan- guage development systems listed in Table 2. All of them can generate lexical scanners, parsers, and prettyprinters, many of them can produce syntax-directed editors, typecheckers, and interpreters, and a few can produce various kinds of software renovation tools.... In PAGE 9: ... It can generate a typechecker, an interpreter, and a debugger. Table4 is far from complete. Some other language development systems are SIS [30], PSP [32], GAG [24], SPS [41], MESS [28], Actress [11], Pregmatic [10], LDL [20], and Eli [26].... ..."

"... In PAGE 21: ... Leaders also updated their cognitive template based on 2 Italics indicate the addition of a new guideline whereas italics and underline indicate the elaboration of an existing guideline. See also Table5... In PAGE 27: ... Over time the continual updating of cognitive templates results in outcomes that are less likely to be surprising. Therefore, learning from experience begins to stabilize when the firm is no longer surprised by what happens to it, and the pattern of behavior generates expected and effective outcomes (see Table5 ). For example, the diminishment of surprise over multiple country entry experiences was evident at e-Smart.... In PAGE 32: ... Rather the key takeaway from the experience was simply to Avoid lunatics. Therefore the prior routine of taking a US product and shove it to Europe, did not change, but was repeated again at the same level of abstraction throughout several more country entries (See Table5 to view the consistency of abstraction in template structure across country entries for ImageTech). Overall, internal attribution of firms for surprise and unexpected negative outcomes helps capabilities further develop by actively varying abstraction levels in their structure.... ..."

"... In PAGE 12: ... Proposition 3.3 Let ; ` f;g ; f;g ContEnv Cont Com Cont the relation inductively de nedbytheprevious rules plus rules (Goto) and (Block) given in Table3 . Then, for any 2 ContEnv, C command of Goto, p;; r continuations, `fpg C frg i j= fpg C frg: Proof.... ..."

"... In PAGE 16: ...Table2 hosts a comparison of RDF/S query languages according to five axes: Modeling constructs supported, Ontology Querying, Data Querying, Data/Ontology Querying and Additional Features provided. Regarding the first comparison axis (Modeling constructs), we can observe from Table 2 that all query languages taking part in the comparison, i.... In PAGE 16: ...supported, Ontology Querying, Data Querying, Data/Ontology Querying and Additional Features provided. Regarding the first comparison axis (Modeling constructs), we can observe from Table2 that all query languages taking part in the comparison, i.e.... In PAGE 16: ... Furthermore, the ability of query languages to perform Data querying is of major importance. Based on this set of criteria, we can note from Table2 that all query language provide constructs for finding the extent of a class or property, either directly or transitively. What most query languages do not support is set-based operations (union, intersection, difference) as well as arithmetic operations on data values.... In PAGE 16: ...ime. The basic criterion for judging this ability is the use of generalized path expressions. Generalized path expressions are very useful primitives because they allow data and ontology to be uniformly queried. As indicated from Table2 , only RQL is capable of incorporating knowledge from ontologies into data querying. In fact, RQL features generalized path expressions with variables on labels of both nodes and edges.... In PAGE 16: ... To evaluate the effectiveness of the query languages when used in large-scale Semantic Web applications we can also use a set of criteria referring to Additional Features supported by the query languages. Table2 records whether the query languages under evaluation support aggregate, grouping and sorting functions. More specifically, RDFQL supports only a count function, VERSA supports min and max, while RQL features min, max, count, average and sum functions, as known from relational databases.... ..."