Knowledge acquisition techniques for decision analysis using AXOTL and AQUINAS

Jeffrey M. Bradshaw, Stanley P. Covington, Peter J. Russo, John H. Boose
1991 Knowledge Acquistion  
The effective application of current decision tree and influence diagram software requires a relatively high level of sophistication in the theory and practice of decision analysis. Research on intelligent decision systems aims to lower the cost and amount of training required to use these methods through the use of knowledge-based systems; however, application prototypes implemented to date have required time-consuming and tedious handcrafting of knowledge bases. This paper describes the
more » ... pment of DDUCKS, an "open architecture" problem-modeling environment that integrates components from Axotl, a knowledge-based decision analysis workbench, with those of Aquinas, a knowledge acquisition workbench based on personal construct theory. The knowledge base tools in Axotl can be configured with knowledge to provide guidance and help in formulating, evaluating, and refining decision models represented in influence diagrams. Knowledge acquisition tools in DDUCKS will allow the knowledge to be efficiently modeled, more easily maintained, and thoroughly tested. 1. outstripped the capacity of conventional decision aids. Informal, checklist, and rating methods are helpful for simple decisions, but are inadequate for the analysis of tradeoffs involving allocations of critical resources. Spreadsheet, data base, and linear programming models for decision making likewise break down with large amounts of uncertain, incomplete, or conflicting data. Such approaches cannot effectively embody the intuition and flexibility of human decision makers. Knowledge-based systems have been widely hailed as a solution to the problem of modeling expert problem solving knowledge. Unfortunately, typical knowledge-based approaches also have their limitations. Rule-based methods employing heuristic certainty factors have been shown to perform poorly in problems involving large amounts of uncertainty or risk, and the kinds of complex tradeoffs that inevitably emerge in strategic decision making (Horvitz, Breese, and Henrion, 1988) . Furthermore, knowledge-based approaches are not sufficiently flexible for many decisions, since tradeoffs may vary greatly across individual cases (Langlotz, Shortliffe, and Fagan, 1986) . Finally, knowledge-based system development environments do not generally provide facilities for integrating historical data with expert judgment (Spiegelhalter, Franklin, and Bull, 1990) .
doi:10.1016/s1042-8143(05)80004-4 fatcat:vjjipdnp7bh3bplxfkj4nvx74m