A SHORT INTRODUCTION TO EXPERT SYSTEMS

Information Systems Working Papers Serie

A SHORT INTRODUCTION TO EXPERT SYSTEMS

Information Systems Working Papers Serie

Logical Reduction of Metarules

International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times

Refunctionalization at Work

We present the left inverse of Reynolds's defunctionalization and we show its relevance to programming and to programming languages. We present two methods to put a program that is almost in defunctionalized form into one that is actually in defunctionalized form, and we illustrate them with a recognizer for Dyck words and with Dijkstra's shunting-yard algorithm

Privacy-Preserving Health Data Collection for Preschool Children

With the development of network technology, more and more data are transmitted over the network and privacy issues have become a research focus. In this paper, we study the privacy in health data collection of preschool children and present a new identity-based encryption protocol for privacy protection. The background of the protocol is as follows. A physical examination for preschool children is needed every year out of consideration for the children's health. After the examination, data are transmitted through the Internet to the education authorities for analysis. In the process of data collection, it is unnecessary for the education authorities to know the identities of the children. Based on this, we designed a privacy-preserving protocol, which delinks the children’s identities from the examination data. Thus, the privacy of the children is preserved during data collection. We present the protocol in detail and prove the correctness of the protocol