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Table V reports some decidability results, depending on the structure of the considered program. For IACMIs satisfying these properties, reachability is decidable. For example, reachability is decidable in the Bell and La Padula and NIST models, as stated by the following proposition.

in A logical framework for reasoning about access control models
by Elisa Bertino, Barbara Catania, Elena Ferrari, Paolo Perlasca 2003
Cited by 58

Table 3: A Routing Algorithm for n k Diagonal Mesh, k 2n + 1. 3.3 Diameter Analysis Besides facilitating the development of routing algorithms, the propositions and corol- laries described in Section 3.2 also allow formulation of the diameter of a diagonal mesh. We present such formulation in the following proposition: Proposition 6 For an N = n k diagonal mesh network, assume n; k are odd, k n and Dd is the diameter.

in unknown title
by unknown authors 1994
"... In PAGE 17: ... Based on these propositions and corollaries, we summarized routing algorithms for diagonal mesh network with N = n k nodes in Tables 2 and 3. Table 2 corresponds to n k lt; 2n and Table3 to k 2n. These routing algorithms identify all optimal directions and shortest path length between any two nodes for the two cases.... ..."
Cited by 8

Table 1: Minimal index of a 1-reducible subgroup in a nite primitive irreducible subgroup of PGL(4;C) As a rst result we obtain the following : Proposition 1. If an irreducible linear di erential equa-

in On Liouvillian Solutions of Linear Differential Equations of Order 4 and 5
by Olivier Cormier, F- Rennes Cedex 2001
"... In PAGE 2: ... Thus, for each group G given by Blichfeldt, we can compute the minimal index of a 1-reducible subgroup of G=Z(G), as explained in Section 3 of [14]. Computations were done with the computer algebra system Magma [2] and results appear in the Table1 . Notations of the groups are those used by Blichfeldt (c.... ..."
Cited by 2

Table 1: Expected size of recovered relation (jrj = 10; n = 12; p = 4; j j = 2) There are, as yet, no completely general results concerning the expected value of jr j in the overlapping case: the situation is apparently very complex and depends on the exact structure of . However, the following Proposition provides a useful formula for calculating this expected value: Proposition 6.1

in Recovering a Relation from a Decomposition Using Constraint Satisfaction
by Peter Jeavons
"... In PAGE 17: ... For each constraint there are j jp possible values, so if r may be approximated by a random selection of tuples, we may use elementary probability arguments to show that: P (w[Sj] 2 Sj(r)) = 1 ? (1 ? 1=j jp)jrj Assuming, for the moment, that the constraints all act independently, we obtain the following expression for the expected size of the recovered relation, E(jr j) (see [20]): E(jr j) = j jn(1 ? (1 ? 1=j jp)jrj)q (2) This expression may be a useful approximation when the Si do not overlap, but, is much too optimistic for the cases we are interested in, which have a high degree of overlap. Table1 compares the values obtained from Equation 2 with the results of simulations for various values of q. It can be seen that the predicted values are much too small for larger values of q.... ..."

lable propositions, we want to define goals so that an agent is required to do only those things within its control. A first attempt might simply be to restrict the ideal goal set as defined above to controllable propositions. The following example shows this to be inadequate.

in Toward a Logic for Qualitative Decision Theory
by Craig Boutilier 1994
Cited by 157

Table 4.2: Term reduction axioms 4.1 Proposition. The reduction of sequential composition is completely characterised by the following axioms:

in A Typed Functional Calculus with State
by Arend Rensink

Table 3: Summary of Propositional Logic Theorem Provers

in Reengineering Unification And T-Entailment For Mantra In C++
by Tania Kharma, Tania Kharma 1996
"... In PAGE 30: ....1.1.5 Summary of Propositional logic theorem proving The following Table3 is a summary of the theorem provers just described. The information is based on readings from [17, 19, 16, 5, 12]... ..."

Table 1: Summary of fragments of Bounded Arithmetic We will need the following easy generalization of [2, Theorem 5] to our setting (see [11]): Proposition 2.1. For i 1, SSi+1 2

in On provably disjoint NP-pairs
by Alexander A. Razborov 1994
Cited by 33

Table 1. Relaxed propositional heuristic.

in Promela Planning
by Stefan Edelkamp 2003
"... In PAGE 10: ... Solving relaxed plans can efficiently be done by building a relaxed problem graph, followed by a greedy plan generation process. Table1 depicts the implementation of the plan construction and the solution extraction phase. The first phase constructs the layered plan graph identical to the first phase of Graphplan [2].... ..."
Cited by 16

Table 2: Resolutive and Propositional predicates Here, I suggest, following Vendler, that there is a basic dichotomy between predicates whose arguments must be propositional and those for which this need not hold, (`propositional apos; here in the sense that truth or falsity must be predicable of them.). I dub the former class TF (for truth/falsity) predicates. Resolutive predicates are disjoint from TF and apparently display one important bifurcation: those predicates whose arguments can have truth/falsity predicated of them do not require their propositional arguments to be true. 28

in Resolving Questions
by Jonathan Ginzburg
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