An Abstracted ‘Not So Expert’ System

G.M. McGrath (Australia)

Keywords

Expert systems; data abstraction; betting.

Abstract

ED ‘NOT SO EXPERT’ SYSTEM G. Michael McGrath Centre for Tourism and Services Research Victoria University Melbourne, Australia michael.mcgrath@vu.edu.au ABSTRACT After falling out of favour during the 1990s, advisory expert systems and their underlying artificial intelligence (AI) technologies are now being used increasingly in organizational decision making activities – particularly, data mining. In this paper, an expert system designed for greyhound race tipping specifically (and investment advice more generally) is described. Our system is distinguished from others in two ways: i) the use of an abstracted schema allows ready extension to other investment domains; and ii) emphasis is placed on reducing complexity where possible – hence, the ‘not so expert’ system. Early indications are that the forecasts produced by our system compare favourably with those of both human experts and previous, relevant decision support systems (DSS).

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