Lisa S. Purvis

Creating personalized documents: an optimization approach

The digital networked world is enabling and requiring a new emphasis on personalized document creation. The new, more dynamic digital environment demands tools that can reproduce both the contents and the layout automatically, tailored to personal needs and transformed for the presentation device, and can enable novices to easily create such documents. In order to achieve such automated document assembly and transformation, we have formalized custom document creation as a multiobjective optimization problem, and use a genetic algorithm to assemble and transform compound personalized documents. While we have found that such an automated process for document creation opens new possibilities and new workflows, we have also found several areas where further research would enable the approach to be more broadly and practically applied. This paper reviews the current system and outlines several areas where future research will broaden its current capabilities.

COMPOSER: A case-based reasoning system for engineering design

The frequent use of past experience by human engineers when solving new problems has led to an interest in the use of case based reasoning (CBR) to help automate engineering design. In engineering design it often occurs that many past exp eriences must be combined to solve a new problem, and thus the process of case based adaptation must efficiently and systematically combine information from many sources. We have developed a constraint based methodology for case combination that allows its application across a wide range of problems. We have shown that our approach provides an efficient adaptation methodology that ensures convergence upon a solution if one exists, provides a uniform representation of cases, and is generalizable beyond just one domain. Our technique is implemented in a case based reasoning system called COMPOSER, which ha s been tested in two design domains: assembly sequence design and configuration design.

Towards Improving Case Adaptability with a Genetic Algorithm

Case combination is a difficult problem in Case Based Reasoning, as sub-cases often exhibit conflicts when merged together. In our previous work we formalized case combination by representing each case as a constraint satisfaction problem, and used the minimum conflicts algorithm to systematically synthesize the global solution. However, we also found instances of the problem in which the minimum conflicts algorithm does not perform case combination efficiently. In this paper we describe those situations in which initially retrieved cases are not easily adaptable, and propose a method by which to improve case adaptability with a genetic algorithm. We introduce a fitness function that maintains as much retrieved case information as possible, while also perturbing a sub-solution to allow subsequent case combination to proceed more efficiently.