A Final Determination of the Complexity of Current Formulations of Model-Based Diagnosis (Or Maybe Not Final?)

There are three parts to this paper. First, I present what I hope is a conclusive, worst-case, complexity analysis of two well-known formulations of the Minimal Diagnosis problem — those of [Reiter 87] and [Reggia et al., 85]. I then show that Reiter\u27s conflict-sets solution to the problem decomposes the single exponential problem into two problems, each exponential, that need be solved sequentially. From a worst case perspective, this only amounts to a factor of two, in which case I see no reason to prefer it over a simple generate-and-test approach. This is only emphasized with the results of the third part of the paper. Here I argue for a different perspective on algorithms, that of expected, rather than worst-case performance. From that point of view, a sequence of two exponential algorithms has lesser probability to finish early than a single such algorithm. I show that the straightforward generate-and-test approach may in fact be somewhat attractive as it has high probability to conclude in a polynomial time, given a random problem instance

Search Through Systematic Set Enumeration

In many problem domains, solutions take the form of unordered sets. We present the Set-Enumerations (SE)-tree - a vehicle for representing sets and/or enumerating them in a best-first fashion. We demonstrate its usefulness as the basis for a unifying search-based framework for domains where minimal (maximal) elements of a power set are targeted, where minimal (maximal) partial instantiations of a set of variables are sought, or where a composite decision is not dependent on the order in which its primitive component-decisions are taken. Particular instantiations of SE-tree-based algorithms for some AI problem domains are used to demonstrate the general features of the approach. These algorithms are compared theoretically and empirically with current algorithms

Automatic cataloguing and characterization of Earth science data using SE-trees

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC

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

Flexible Support for Trauma Management Through Goal-Directed Reasoning and Planning

We describe a system, TraumAID, which has been designed to provide decision support throughout the initial definitive management of severely injured patients (i.e., after their initial evaluation, resuscitation, and stabilization). Over the course of initial definitive management, TraumAID recommends appropriate procedures to be carried out, based on currently available evidence and on the complexity and urgency of the situation. TraumAID\u27s ability to deal flexibly with complex and often urgent situations comes from its ability to reason separately about the management goals that should be achieved and about the means that are situationally appropriate for achieving them. In this paper, we describe TraumAID\u27s approach to trauma management in more detail, showing in particular how it enables TraumAID to adapt its reasoning and recommendations to the urgency with which a patient\u27s condition must be addressed

Progressive Horizon Planning

In an earlier paper [Rymon et a1 89], we showed how domain localities and regularities can be used to reduce the complexity of finding a trauma management plan that satisfies a set of diagnostic and therapeutic goals. Here, we present another planning idea - Progressive Horizon - useful for optimizing such plans in domains where planning can be regarded as an incremental process, continuously interleaved with situation - goals analysis and plan execution. In such domains, planned action cannot be delayed until all essential information is available: A plan must include actions intended to gather information as well as ones intended to change the state of the world. Interleaving planning with reasoning and execution, a progressive horizon planner constructs a plan that answers all currently known needs but has only its first few actions optimized (those within its planning horizon). As the executor cames out actions and reports back to the system, the current goals and the plan are updated based on actual performance and newly discovered goals and information. The new plan is then optimized within a newly set horizon. In this paper, we describe those features of a domain that are salient for the use of a progressive horizon planning paradigm. Since we believe that the paradigm may be useful in other domains, we abstract from the exact techniques used by our program to discuss the merits of the general approach

TraumAID: AI Support in the Management of Multiple Trauma

This paper outlines the particular demands that multiple trauma makes on systems designed to provide appropriate decision support, and the ways that these demands are currently being met in our system, TraumAID. The demands follow from: (1) the nature of trauma and the procedures used in its diagnosis, (2) the need to adjust diagnostic and therapeutic procedures to available resource levels, (3) the role of anatomy in trauma and the need for anatomical reasoning, (4) the role of non-specialists in managing trauma, and (5) the competing demands of multiple injuries and the consequent need for planning. We believe that these demands are not unique to multiple trauma, so that the paper may be of general interest to expert system research and development

TraumAID: Reasoning and Planning in the Initial Definitive Management of Multiple Injuries

The TraumAID system has been designed to provide computerized decision support to optimize the initial definitive management of acutely injured patients after resuscitation and stabilization. The currently deployed system, TraumAID 1.0, addresses penetrating injuries to the abdomen and to the chest. Our experience with TraumAID 1.0 has demonstrated some major deficiencies in rule-based reasoners that are faced with problems of both diagnosis and treatment. To address these deficiencies, we have redesigned the system (TraumAID 2.0), factoring it into two modules: (1) a rule-based reasoner embodying the knowledge and logical machinery needed to link clinical evidence to diagnostic and therapeutic goals, and (2) a planner embodying the global knowledge and logical machinery needed to create a plan that addresses combinations of goals. After describing TraumAID 2.0, we discuss an extension of the TraumAID interface (critique mode interaction) that may improve its acceptability in a clinical setting. We close with a brief discussion of management support in resource-limited environments, which is an important issue in the time-critical context of multiple trauma

Diagnostic Reasoning and Planning in Exploratory-Corrective Domains

DIAGNOSTIC REASONING AND PLANNING IN EXPLORATORY-CORRECTIVE DOMAINS Ron Rymon Bonnie L. Webber (Supervisor) I have developed a methodology for knowledge representation and reasoning for agents working in exploratory-corrective domains. Working within the field of Artificial Intelligence in Medicine, I used the specific problem of diagnosis-and-repair in multiple trauma management as both motivation and testbed for my work. A reasoning architecture is proposed in which specialized diagnostic reasoning and planning components are integrated in a cycle of reasoning and action/perception: 1. A Goal-Directed Diagnostic (GDD) reasoner which is predicated on the view that diagnosis is only worthwhile to the extent that it can affect repair decisions and that goals can be used to focus on such. Rather than focusing on a diagnosis object as the primary purpose of the diagnostic process, the GDD reasoner is tasked primarily with generating goals for the planner and with reasoning about whether t..