M. J. Coombs

The MDR algorithm and its application to the generation of explanations for novel events

Abstract This paper presents an algorithm for reasoning about novel events. Termed Model Generative Reasoning (MGR), we replace deductive reasoning with an abductive procedure based on the generation of alternative, intensional domain descriptions (models) to cover problem assumptions, which are then evaluated against domain facts as alternative explanations for queried events. The algorithm is principally illustrated using a problem from process control.

REASONING WITH GRAPH OPERATIONS

Abstract Problem solving is an analog to scientific method, wherein abduction and deduction operate in a cyclic fashion to generate and refine a series of hypotheses that purport to explain the observed data. Model generative reasoning implements this cycle through a family of operations on representations based on conceptual graphs. Specialize , the operator that implements abduction, generates alternative hypotheses. Fragment removes potential incoherences from hypotheses, while preserving coherence with the observations. This is seen as a form of deduction with the aim of allowing more hypotheses to be generated in the next cycle.

An application of the Birmingham discourse analysis system of the study of computer guidance interactions

University researchers need to use computers in their work yet they are not computer professionals and are not able to progress efficiently without adequate guidance. All British universities make such computer-use guidance available, the most important source of information being the advisory service of the local computer center. However, little is known of the structure and effectiveness of advisory interactions. The analysis of advisory conversations requires some method for representing their structure. This paper reports such a method based on a scheme devised by a research team at the University of Birmingham, UK (Sinclair & Coulthard, 1975) for the study of teaching discourse. Using this system, we are able to define the problem-solving nature of advisory discourse, along with a number of limitations inherent in the way advisers currently conduct interactions. Various coding extensions are suggested, along with proposals for the application of the system to the development of improved advisory strategies.

e-MGR: an architecture for symbolic plasticity

Abstract The e-MGR architecture was designed for symbolic problem solving in task environments where data are noisy and problems are ill-defined. e-MGR is an operator-based system which integrates problem-solving ideas from symbolic artificial intelligence (AI) and adaptive systems research.