Thomas A. Russ

Building and (re)using an ontology of air campaign planning

This article presents a case study in building and (re)using an ontology in a specific application domain-air campaign planning. The article describes the common ontology built to serve as a basis for knowledge sharing and discusses several issues raised in its construction.

Simplifying construction of complex workflows for non-expert users of the Southern California Earthquake Center Community Modeling Environment

Workflow systems often present the user with rich interfaces that express all the capabilities and complexities of the application programs and the computing environments that they support. However, non-expert users are better served with simple interfaces that abstract away system complexities and still enable them to construct and execute complex workflows. To explore this idea, we have created a set of tools and interfaces that simplify the construction of workflows. Implemented as part of the Community Modeling Environment developed by the Southern California Earthquake Center, these tools, are integrated into a comprehensive workflow system that supports both domain experts as well as non expert users.

Electric Elves: Agent Technology for Supporting Human Organizations

The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, to monitor the status of such activities, to gather information relevant to the organization, to keep everyone in the organization informed, etc. Teams of software agents can aid humans in accomplishing these tasks, facilitating the organization’s coherent functioning and rapid response to crises, while reducing the burden on humans. Based on this vision, this paper reports on Electric Elves, a system that has been operational, 24/7, at our research institute since June 1, 2000. Tied to individual user workstations, fax machines, voice, mobile devices such as cell phones and palm pilots, Electric Elves has assisted us in routine tasks, such as rescheduling meetings, selecting presenters for research meetings, tracking people’s locations, organizing lunch meetings, etc. We discuss the underlying AI technologies that led to the success of Electric Elves, including technologies devoted to agenthuman interactions, agent coordination, accessing multiple heterogeneous information sources, dynamic assignment of organizational tasks, and deriving information about organization members. We also report the results of deploying Electric Elves in our own research organization.

Using hindsight in medical decision making

As the clinical picture of a patient evolves over time, more information becomes available. Certain procedure require time to perform, causing delay between the time when the tests are ordered and when the results are available. Furthermore, as the patients condition changes over time, serial measurements can be made. The availability of more data allows a more accurate assessment of the patient. Uncertainties, guesses or errors that were made early in the clinical course of patient care can also be identified and resolved when more information is available. Reasoning with a stream of data that changes over time presents a challenge to the designers of expert systems. The use of hindsight in expert system requires that appropriate attention be paid to the temporal relations of the data and that care is exercised in revising decision. I present a data-dependency system, the Temporal Control Structure (TCS), designed to support reasoning with data changing over time and show how it can be used to implement reasoning by hindsight.

Automatically Constructing Semantic Web Services from Online Sources

The work on integrating sources and services in the Semantic Web assumes that the data is either already represented in RDF or OWL or is available through a Semantic Web Service. In practice, there is a tremendous amount of data on the Web that is not available through the Semantic Web. In this paper we present an approach to automatically discover and create new Semantic Web Services. The idea behind this approach is to start with a set of known sources and the corresponding semantic descriptions and then discover similar sources, extract the source data, build semantic descriptions of the sources, and then turn them into Semantic Web Services. We implemented an end-to-end solution to this problem in a system called Deimos and evaluated the system across five different domains. The results demonstrate that the system can automatically discover, learn semantic descriptions, and build Semantic Web Services with only example sources and their descriptions as input.

Evaluating expert-authored rules for military reasoning

Eliciting complex logical rules directly from logic-naive subject matter experts (SMEs) is a challenging knowledge capture task. We describe a large-scale experiment to evaluate tools designed to produce SME-authored rule bases. We assess the quality of the rule bases with respect to the: 1) performance on the addressed functional task (military course of action (COA) critiquing); and 2) intrinsic knowledge representation quality. In the course of this assessment, we note both strengths and weaknesses in the state of the art, and accordingly suggest some foci for future development in this important technology area.

Knowledge Synthesis with Maps of Neural Connectivity

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called “Knowledge Engineering from Experimental Design” (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

Exploiting Data Semantics to Discover, Extract, and Model Web Sources

We describe Deimos, a system that automatically discovers and models new sources of information.The system exploits four core technologies developed by our group that makes an end-to-end solution to this problem possible. First, given an example source, Deimos finds other similar sources online. Second, it invokes and extracts data from these sources. Third, given the syntactic structure of a source, Deimos maps its inputs and outputs to semantic types. Finally, it infers the sources semantic definition, i.e., the function that maps the inputs to the outputs. Deimos is able to successfully automate these steps by exploiting a combination of background knowledge and data semantics. We describe the challenges in integrating separate components into a unified approach to discovering, extracting and modeling new online sources. We provide an end-to-end validation of the system in two information domains to show that it can successfully discover and model new data sources in those domains.

Case-based reasoning for procedure learning by instruction

To control intelligent tools that perform a variety of complex procedures, users need to be able to both modify existing procedure descriptions and communicate new procedures. In one approach, the user describes fragments of a procedure with text, and the tool searches the space of potential procedures for a match. This approach sometimes provides too little guidance for users, yet providing templates for guidance can require an expensive knowledge engineering effort in each new domain. We investigate the use of case-based reasoning to help guide the user, treating previously-defined procedures in the domain as cases. We describe domainindependent methods to find similar procedures while the user creates or modifies a procedure, to suggest potential steps to copy and to manage mapping the variables from the existing procedure into the procedure being edited. In some cases, the mapping tool suggests auxiliary steps to copy along with the desired steps, following an approach similar to derivational analogy. We evaluate the potential of this approach with an implemented tool, CB-Tailor, in a travel domain containing a number of procedures that may be added by the user. Our experiences suggest that the tool can provide useful guidance in a realistic set of situations.

Coordinating advice and actual treatment

The author focuses on identifying the current treatment, allowing time for therapy to take effect, and coordinating the advice with the actual execution of treatment plans. These problems were encountered during the implementation of an expert system for treatment of diabetic ketoacidosis. The programming system, called the temporal control structure (TCS), is a temporal data dependency manager that maintains a temporal database and automatically updates decisions that are based on information that changes. Data can be stored either as point events or as intervals. When an expert system is programmed, the data dependencies of all of the decisions are declared. By tracing the dependency structure, the TCS can assure the complete propagation of information as it arrives and changes. The programmer is relieved of the burden of explicitly calling for the recalculation of affected decisions. The use of the TCS allows the flexible implementation of reasoning systems that manipulate data that arrives during the course of the consultation.<<ETX>>