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Abigail S. Gertner

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3 papers

1 author row

AAAI Conference 1998 Conference Paper

Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model

Abigail S. Gertner

One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding of both what solution path the student is pursuing, and the student’s current level of domain knowledge. Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests. Andes’ student model uses a Bayesian network that computes a probabilistic assessment of three kinds of information: (1) the student’s general knowledge about physics, (2) the student’s specific knowledge about the current problem, and (3) the abstract plans that the student may be pursuing to solve the problem. Using this model, Andes provides feedback and hints tailored to the student’s knowledge and goals.

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AIIM Journal 1997 Journal Article

On-line assurance in the initial definitive management of multiple trauma: evaluating system potential

Abigail S. Gertner
Bonnie L. Webber
John R. Clarke
Cathering Z. Hayward
Thomas A. Santora
David K. Wagner

The TraumAID system has been designed to provide on-line decision support throughout the initial definitive management of injured patients. Here we describe its retrospective evaluation and the use we subsequently made of judges comments on the validation data to evaluate TraumaTIQ, a new critiquing interface for TraumAID, investigating the question of whether, with timely recording of information, a system could produce commentary in line with that of human experts. Our results show that (1) comparable commentary can be produced, and (2) validation studies, which take great time and effort to conduct, can produce useful data beyond their original design goals.

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AAAI Conference 1996 Conference Paper

A Bias towards Relevance: Recognizing Plans where Goal Minimization Fails

Abigail S. Gertner

Domains such as multiple trauma management, in which there are multiple interacting goals that change over time, are ones in which plan recognition’ s standard inductive bias towards a single explanatory goal is inappropriate. In this paper we define and argue for an alternative bias based on identifying contextually “relevant” goals. We support this claim by showing how a complementary planning system in TraumAID 2. 0, a decision-support system for the management of multiple trauma, allows us to define a four-level scale of relevance and therefore, of measurable deviations from relevance. This in turn allows definition of a bias towards relevance in the incremental recognition of physician plans by Traum- AID’ s critiquing interface, TraumaTIQ.

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