The present paper considers the effects of introducing inaccuracies in a learner’s environ-ment in Gold’s learning model of identification in the limit. Three kinds of inaccuracies are considered: presence of spurious data is modeled as learning from a noisy environment, miss-ing data is modeled as learning from incomplete environment, and the presence of a mixture of both spurious and missing data is modeled as learning from imperfect environment. Two learning domains are considered, namely, identification of programs from graphs of computable functions and identification of grammars from positive data about recursively enumerable languages. Many hierarchies and tradeoffs resulting from the interplay between the number of errors allowed in the final hypotheses, the number of inaccuracies in the data, the types of inaccuracies, and the type of success criteria are derived. An interesting result is that in the context of function learning, incomplete data is strictly worse for learning than noisy data. 1

, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. 1

Children’s trust in previously inaccurate informants who were well- or poorly- informed: When past errors can be excused.

removing them influence broadcasting time? The aim of this paper is to answer this question, hence we assume that density is unknown and nodes perceive their position with some unknown error margin ɛ. It turns out that this combination of missing and inaccurate information substantially changes the problem

We investigate the problem of routing connections with QoS requirements across one or more networks, when the information available for making routing decisions is inaccurate and expressed in some probabilistic manner. This uncertainty about the actual state of a node or network arises naturally

of SRPT scheduling, it is not widely applied. One important reason for the spo-radic application of SRPT scheduling is that accurate job size information is often unavailable. Our previ-ous work addressed the performance and fairness issues of SRPT scheduling when job size information is inaccu-rate. We

utility-based allocation mechanisms and policies must explicitly consider the fidelity of elicited job value information from users. 1.

of SRPT scheduling, it is not widely applied. One important reason for the sporadic application of SRPT scheduling is that accurate job size information is often unavailable. Our previous work addressed the performance and fairness issues of SRPT scheduling when job size information is inaccurate. We

This paper investigates the problem of routing flows with Quality-of-Service (QoS) requirements through one or more networks, when the information available for making such routing decisions is inaccurate. Inaccuracy in the information used in computing QoS routes, e.g., network state such as link

This paper addresses the optimal recovery of functions from Hilbert spaces of functions on the unit disc. The estimation, or recovery, is performed from inaccurate information given by integration along radial paths. For a holomorphic func-tion expressed as a series, three distinct situations