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Table 3 Classes of knowledge work indicators identified in the observation data
"... In PAGE 7: ... By doing this we aim at describing how work was affected by the factors identified; and identifying situations of explicit/implicit use of the body of development process knowledge and how these interact. We identified a set of indicator classes (see Table3 ) which comprise a number of different instances of knowledge work indicators. We use the term artefacts to comprise the notion of artefacts in the Rational unified process, see e.... ..."
Table 1. Degrees of formal and informal knowledge. Degree Model Interface Example
"... In PAGE 3: ...7 The granularity of this knowledge then lies in the middle between the extremes of coarsely representing business documents and representing them too finely. As Table1 illus- trates, there are several degrees of formality between formal and informal knowledge. In Table 1, we use the term content-struc- tured documents to refer to XML structures that are tightly (sometimes explicitly, some- times implicitly) linked to a domain model.... In PAGE 3: ... As Table 1 illus- trates, there are several degrees of formality between formal and informal knowledge. In Table1 , we use the term content-struc- tured documents to refer to XML structures that are tightly (sometimes explicitly, some- times implicitly) linked to a domain model. For instance, XML-EDI documents come with a predefined structure alluding to a stan- dard framework for exchanging data, such as invoices, healthcare claims, or project status reports.... ..."
Table 3.7: The transfer and interaction requirements of tutorial instruction to be met, and those met thus far in the description of Instructo-Soar. from explanations; in addition, the burden on the instructor is increased. Table 3.7 indicates the transfer and interaction requirements of tutorial instruction that have been met at this point in the description of Instructo-Soar. Chapter 4 completes the description of Instructo-Soar by describing its use of explicitly situated instructions, and its coverage of each of the outstanding knowledge types remaining to be covered in Table 3.7.
Table 5 - Mediadv Knowledge Base setting 1 result 1 setting 2 result 2 setting 3 result 3 setting 4 result 4
"... In PAGE 72: ... Indeed, it is straightforward to show that, on Boolean domains, DHET=NDL apos;=DN apos;. Table5 - ILA - Linear Applications Application DN apos; NDL apos; DHET iris 90.5 94.... In PAGE 123: ...urther representative experiments on new situations (i.e., not explicitly encoded in the knowledge bases) were performed. They are summarized in Table5 and Table 6. In each table the first column contains the list of attributes used in the knowledge base.... ..."
Table 1: Approaches to the representation of relational knowledge. Relation term Bindings
2001
"... In PAGE 4: ... Even if not reducible one to the other, all of these ways of representing relations comprise a highly similar class. Table1 summarizes the various approaches. All assume that the speci cation of how the objects in a relation are related is by explicitly labeling them as being in that relation.... ..."
Cited by 1
Table 6. Timings of group operations with ARMulator ARM7TDMI@80MHz (explicit formulae)
2003
"... In PAGE 11: ...ase of HECC. For genus-3 curves the polynomial h(x) equals one. The group orders range from 2162 to 2190. Table6 presents timings for divisor addition, divisor doubling and scalar multiplication on the ARMulator. To our knowledge these are the first published timings for HECC on an embedded processor.... ..."
Cited by 35
Table 6. Timings of group operations with ARMulator ARM7TDMI@80MHz (explicit formulae)
"... In PAGE 11: ...ase of HECC. For genus-3 curves the polynomial h(x) equals one. The group orders range from 2162 to 2190. Table6 presents timings for divisor addition, divisor doubling and scalar multiplication on the ARMulator. To our knowledge these are the first published timings for HECC on an embedded processor.... ..."
Table 4: Average number of captures for MAFNP prey (standard deviations are presented in parentheses). never resolved, because each of the predators continues to bounce o of each other. However, if the predators are employing the MD strategy, then eventually predator 3 will non{deterministically decide to move South. This illustrates the basic di erence between these two strategies. On closer inspection, the STGP evolved strategy reveals itself as a special case of the MD strategy in which one of the move options available to the MD strategy is deterministically preferred over the other options. In summary, these set of experiments demonstrate that evolution is a viable mechanism for generating individual behavior strategies that lead to e ective group performance. This is particularly illuminating because both little domain knowledge is provided and we do not allow for explicit communication between agents in the group. This bodes well for the possibility of evolving coordination strategies for other multiagent domains.
1995
"... In PAGE 8: ... Note that STGP, MDO, and MD are all strategies in which once the prey is captured, it never escapes. Table4 presents the results from allowing the prey to move with the MAFNP algorithm. Combined with the results for the Random prey, Table 3, we observe that in general, the predator algorithms can be ranked as: MD, STGP, MDO, MNO, and MN.... In PAGE 9: ... With the MAFNP prey, leaving a capture position can result in a chase to re{occupy a capture position. From Table4 and Table 3, we see that the Random prey is easier to capture than the MAFNP prey. Either the capture rate improves, as exempli ed by MN and MNO, or the steps to capture decreases, as shown by MD.... ..."
Cited by 10
Table 1: Potential problems in the knowledge bases detected by EXPECT. Each component of EXPECT is able to detect an error, recover from the error if possible, and report the error apos;s type and context in which it occurred. EXPECT apos;s knowledge acquisition tool uses this information to support users in resolving errors.
"... In PAGE 2: ... The information about errors is explicitly represented, and we nd it is very useful as a means for the problem solver to provide feedback to the knowledge acquisi- tion tool regarding the status of the current knowledge base. Table1 describes some of these errors. Figure 2 shows a snapshot of expect apos;s user interface.... ..."
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