PROLOG META-INTERPRETERS FOR RULE-BASED INFERENCE UNDER UNCERTAINTY

Uncertain facts and inexact rules can be represented and processed in standard Prolog through meta-interpretation. This requires the specification of appropriate parsers and belief calculi. We present a meta-interpreter that takes a rule-based belief calculus as an external variable. The certainty-factors calculus and a heuristic Bayesian belief-update model are then implemented as stand-alone Prolog predicates. These, in turn, are bound to the meta-interpreter environment through second-order programming. The resulting system is a powerful experimental tool which enables inquiry into the impact of various designs of belief calculi on the external validity of expert systems. The paper also demonstrates the (well-known) role of Prolog meta-interpreters in building expert system shells.Information Systems Working Papers Serie

META-INTERPRETERS FOR RULE-BASED REASONING UNDER UNCERTAINTY

One of the key challenges in designing expert systems is a credible representation of uncertainty and partial belief. During the past decade, a number of rule-based belief languages were proposed and implemented in applied systems. Due to their quasi-probabilistic nature, the external validity of these languages is an open question. This paper discusses the theory of belief revision in expert systems through a canonical belief calculus model which is invariant across different languages. A meta-interpreter for non-categorical reasoning is then presented. The purposes of this logic model is twofold: first, it provides a clear and concise conceptualization of belief representation and propagation in rule-based systems. Second, it serves as a working shell which can be instantiated with different belief calculi. This enables experiments to investigate the net impact of alternative belief languages on the external validity of a fixed expert system.Information Systems Working Papers Serie

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

HUDDL for description and archive of hydrographic binary data

Many of the attempts to introduce a universal hydrographic binary data format have failed or have been only partially successful. In essence, this is because such formats either have to simplify the data to such an extent that they only support the lowest common subset of all the formats covered, or they attempt to be a superset of all formats and quickly become cumbersome. Neither choice works well in practice. This paper presents a different approach: a standardized description of (past, present, and future) data formats using the Hydrographic Universal Data Description Language (HUDDL), a descriptive language implemented using the Extensible Markup Language (XML). That is, XML is used to provide a structural and physical description of a data format, rather than the content of a particular file. Done correctly, this opens the possibility of automatically generating both multi-language data parsers and documentation for format specification based on their HUDDL descriptions, as well as providing easy version control of them. This solution also provides a powerful approach for archiving a structural description of data along with the data, so that binary data will be easy to access in the future. Intending to provide a relatively low-effort solution to index the wide range of existing formats, we suggest the creation of a catalogue of format descriptions, each of them capturing the logical and physical specifications for a given data format (with its subsequent upgrades). A C/C++ parser code generator is used as an example prototype of one of the possible advantages of the adoption of such a hydrographic data format catalogue