Discourse Deixis and Discourse Processing

Computational approaches to discourse understanding have a two-part goal: (1) identifying those aspects of discourse understanding that require process-based accounts, and (2) characterizing the processes and data structures they involve. To date, in the area of reference, process-based accounts have been developed for reference via anaphoric pronouns and via definite descriptors. In this paper, I propose and argue for a process-based account of deictic reference in text. This account adds precision to common notions of discourse entity, discourse segment and focus and to relationships between the three

Deictic Reference and Discourse Structure

Research on the factors and processes involved in pronoun interpretation has to date concentrated on anaphoric pronouns. Results have supported the now widely-held view that discourse understanding involves the creation of a partial, mental model of the situation described through the discourse. Anaphoric pronouns are taken to refer to elements of that model (often called discourse referents or discourse entities), usually ones that have, at the moment of referring, some special focus status. This paper examines deictic pronouns - in particular, ones that refer to the interpretation of one or more clauses. I argue that referents for these pronouns must come from the interpretations of discourse segments on the right frontier of an evolving structure representing the discourse. Under the assumption that reference is always to an individual, this implies that discourse segment interpretations must also be part of the evolving discourse model. I discuss this in the last section of the paper

Simulation and analysis of complex human tasks

We discuss how the combination of a realistic human figure with a high-level behavioral control interface allow the construction of detailed simulations of humans performing manual tasks from which inferences about human performance requirements can be made. The Jack human modeling environment facilitates the real-time simulation of humans performing sequences of tasks such as walking, lifting, reaching, and grasping in a complex simulated environment. Analysis capabilities include strength, reachability, and visibility; moreover results from these tests can affect an unfolding simulation

Progressive Horizon Planning - Planning Exploratory-Corrective Behavior

Much planning research assumes that the goals for which one plans are known in advance. That is not true of trauma management, which involves both a search for relevant goals and reasoning about how to achieve them. TraumAID is a consultation system for the diagnosis and treatment of multiple trauma. It has been under development jointly at the University of Pennsylvania and the Medical College of Pennsylvania for the past eight years. TraumAID integrates diagnostic reasoning, planning and action. Its reasoner identifies diagnostic and therapeutic goals appropriate to the physician’s knowledge of the patient’s state, while its planner advises on beneficial actions to next perform. The physician’s lack of complete knowledge of the situation and the time limitations of emergency medicine constrain the ability of any planner to identify what would be the best thing to do. Nevertheless, TraumAID’s Progressive Horizon Planner has been designed to create a plan for patient care that is in keeping with the standards of managing trauma

Towards Goal-Directed Diagnosis (Preliminary Report)

Recent research has abstracted diagnosis away from the activity needed to acquire information and to act on diagnosed disorders. In some problem domains, however, such abstraction is counter-productive and does not reflect real-life practice, which integratesdiagnostic and therapeutic activity. Trauma management is a case in point. Here, we discuss a formalization of the integrated approach taken in TraumAID, a system we have developed to serve as an artificial aide to residents and physicians dealing with multiple trauma. Among other things, the active pursuit of information raises the question of what is and what is not worth pursuing. In TraumAID 2.0, we take the view that the process of diagnosis should continue only as long as it is likely to make a difference to future actions. That view is formalized in the goal-directed diagnostic paradigm (GDD). Unlike other diagnostic paradigms, goal-directed diagnosis is first and foremost concerned with setting goals based on its conclusions. It regards the traditional construction of an explanation for the faulty behavior as secondary. In order to explicitly represent goal-directedness, the diagnostic process is viewed as search in a space of attitude-beliefs. From this, we derive a high-level algorithm that produces appropriate requests for action while searching for an explanation. A complete explanation, however, is not the criterion for terminating action. Such a criterion, we argue, is better treated in terms of goal-means tradeoffs. TraumAID\u27s architecture, in so far as it embodies this goal-directed approach, assigns to a complementary planner the resolution of such tradeoffs

Living Up to Expectations: Computing Expert Responses

In cooperative man-machine interaction, it is necessary but not sufficient for a system to respond truthfully and informatively to a user\u27s question. In particular, if the system has reason to believe that its planned response might mislead the user, then it must block that conclusion by modifying its response. This paper focuses on identifying and avoiding potentially misleading responses by acknowledging types of \u27informing behavior\u27 usually expected of an expert. We attempt to give a formal account of several types of assertions that should be included in response to questions concerning the achievement of some goal (in addition to the simple answer), lest the questioner otherwise be misled