In search of meta-knowledge

Development of an Intelligent Information System (IIS) involves application of numerous artificial intelligence (AI) paradigms and advanced technologies. The National Aeronautics and Space Administration (NASA) is interested in an IIS that can automatically collect, classify, store and retrieve data, as well as develop, manipulate and restructure knowledge regarding the data and its application (Campbell et al., 1987, p.3). This interest stems in part from a NASA initiative in support of the interagency Global Change Research program. NASA's space data problems are so large and varied that scientific researchers will find it almost impossible to access the most suitable information from a software system if meta-information (metadata and meta-knowledge) is not embedded in that system. Even if more, faster, larger hardware is used, new innovative software systems will be required to organize, link, maintain, and properly archive the Earth Observing System (EOS) data that is to be stored and distributed by the EOS Data and Information System (EOSDIS) (Dozier, 1990). Although efforts are being made to specify the metadata that will be used in EOSDIS, meta-knowledge specification issues are not clear. With the expectation that EOSDIS might evolve into an IIS, this paper presents certain ideas on the concept of meta-knowledge and demonstrates how meta-knowledge might be represented in a pixel classification problem

Intelligent interaction in diagnostic expert systems

AbstractAdvisory systems help to improve quality in manufacturing. Such systems, however, both human and computerized, are less than perfect and frequently not welcome. Sharp separation between working and learning modes is the main reason for the apparent hostility of advisory systems. Intelligent interaction deploys computerized advisory capabilities by merging working and learning modes. We have developed a knowledge-based interactive graphic interface to a circuit pack diagnostic expert system. The graphic interface integrates both the domain knowledge (i.e. circuit pack) and the troubleshooting knowledge (i.e. diagnostic trees). Our interface dynamically changes the amount of detail presented to the user as well as the input choices that the user is allowed to make. These changes are made using knowledge-based models of the user and of the circuit pack troubleshooting domain. The resulting system, McR, instead of guiding the user by querying for input, monitors users actions, analyzes them and offers help when needed. McR is able both to advise “how-to-do-it” by reifying shallow knowledge from the deep knowledge, and to explain intelligently “how-does-it-work” by abstracting deep knowledge from the hallow knowledge, McR is used in conjunction with the STAREX expert sytem which is installed at AT&T factory

Specializing Interpreters using Offline Partial Deduction

We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages