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The reason seems to be that the upper bound estimate for the probability of the point (a3 , b3 ) (which is identical to its degree of possibility) is so much higher than the IRP probability estimate Maybe intuitively we take this into account by (too optimistically ) correcting the relative values of the probability estimates, so that the point (a3 , b3 ) is preferred to the point (a1 , b1 ), for which the upper bound estimate coincides with the IRP probability estimate It is clear that one can easily make the situation more extreme by considering more contexts with focal sets overlapping on a specific point of the joint domain.

To make the IRP probability distribution prefer a point for which there was only one example, we only have to make the overlapping focal sets sufficiently large (this can easily be achieved by adding values to the domains of the attributes A and B, or, more naturally, by considering additional attributes) We expect that there is a point at which one rejects the decision suggested by the IRP probability distribution We are not sure, though, whether this is an argument in favor of possibility theory (in the interpretation considered here) In the first place, there are several situations in which the decision based on the IRP probability estimates is clearly better (see above) Secondly, even if it could be shown that on average people would prefer the decision resulting from possibility theory in cases as the one discussed, this would not prove that this decision is reasonable.

The topology of an ad hoc network can be de ned as an extension of the neighbor relation by referring to the network graph (V, E) consisting of all nodes V and all edges (v, w) that satisfy that w is a neighbor of v (see Fig 151b) Generally speaking, topology control refers to all methods that, starting from a given network topology (V, E), construct a graph (V , E ) that satis es a desired property More speci cally, existing topology control mechanisms can be distinguished according to the following three subclasses Neighbor elimination schemes (NES) maintain the entire set of network nodes while reducing each node s neighbor set; that is, the resulting topology (V , E ) satis es V = V and E E with additional properties like connectivity.

Several examples are known in which the involved probabilities are precisely defined and hence the insufficient reason principle need not be called upon, and nevertheless people on average decide in a way that does not maximize their utility Hence one may argue that this only points out a(nother) deficiency in commonsense reasoning Another open problem that is connected to the above considerations results from the fact that possibility distributions (in the interpretation considered here) essentially model negative information The reason is that in each context it is not important which values are possible (this we know from the domain definitions of the attributes), but which values are impossible, that is, can definitely be excluded As a consequence, a possibility distribution is some kind of upper bound for the probability (cf Sections 241 and 242).

messages are transmitted in the network Queries can be ooding-based where a query is ooded to every node in the network Alternatively, they can be ERS based where a node does not relay a query that it can resolve Currently available query processing systems are summarized below: TinyDB [69]: a query processing system for extracting information from a network of TinyOS sensors TinyDB provides a simple SQL-like interface to specify the data, along with additional parameters such as the rate at which data should be refreshed much as in traditional databases Given a query specifying data interests, TinyDB collects data from nodes in the environment, lters and aggregates them TinyDB does this via power-ef cient in-network processing algorithms.

It would be worthwhile to consider whether this negative information can be complemented by positive information, which could take the form of a necessity distribution Of course, this idea is not new Research on possibility.

Method Signature EJBHome getEJBHome() Class getHomeInterfaceClass() Class getRemoteInterfaceClass() Description Returns the bean s remote home interface. Returns the class for the enterprise bean s remote home interface. Returns the class for the enterprise bean s remote interface.