John F. Gilmore

A Survey Of Diagnostic Expert Systems

Diagnostic expert systems is an area of growing interest in the application of expert system technology. Diagnosis is a step-by-step process that attempts to ascertain the internal characteristics of a physical system. The first generation of knowledge-based systems for hardware diagnosis are based primarily on the largely empirically derived knowledge of human experts. A number of diagnostic expert systems dealing with electronic and computer hardware have been developed over the last several years. The majority of these systems have utilized a variety of diagnostic techniques and concepts to solve a specific hardware application. This paper surveys four of the most successful diagnostic expert systems in the areas of computers and electronic hardware and analyzes each in a comparative manner. Several additional systems are summarized to provide the reader with a relatively extensive overview of existing research and development activities in the area of diagnostic expert systems.

Knowledge-based approach toward developing an autonomous helicopter system

The advent of advanced computer architectures for parallel and symbolic processing has evolved technology to the point at which prototype autonomous vehicles are being developed. Control of such devices requires communication between knowledge-based subsystems in charge of the vision, planning, and control aspects necessary to make autonomous systems func-tional in a real-world environment. The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in dynamic situations, autonomous vehicles must be capable of interpreting terrain on the basis of predetermined mission goals. This paper describes an autonomous airborne-vehicle simulation currently being developed at the Georgia Tech Research Institute. The Autonomous Helicopter System (AHS) is a multimission system consisting of three distinct sections: vision, planning, and control. The vision section provides the local and global scene analyses that are symbolically represented and passed to the planning section as the initial route-planning constraints. The planning section generates a task-dependent path for the vehicle to traverse that assures maximum mission system successes as well as survivability. The control section validates the path and either executes the given route or feeds back to previous sections in order to resolve conflicts.

Knowledge-based target recognition system evolution

Target recognition systems have undergone a variety of changes over the past 20 years. Initial systems exploited signal processing techniques to detect ground-based targets based on one-dimensional signals. Limitations of these systems eventually led to the development of automated target recognizers (ATRs) that processed two-dimensional digital images to detect, classify, and identify targets. Though their performance exceeded that of signal processing systems, ATRs exhibited several deficiencies to which artificial intelligence (Al) offered numerous potential solutions. This paper reviews the evolution of target recognition systems with primary focus on Al applications. Deficiencies of Al approaches to target recognition are presented and complemented by a discussion of a blackboard-based ATR system currently being developed at Georgia Tech.

A Model Driven System for Contextual Scene Analysis

Existing strategies for the identification of objects in a scene are based upon classical pattern recognition approaches. The basic concept involved centers around the extraction of a set of statistical features for each object detected in a scene, followed by the application of a classifier which attempts to derive the decision boundaries that separate these objects into classes. As statistical features are quite sensitive to noise, this approach has led to problems due to the inability of classifiers to identify accurate feature set separation in less than ideal conditions. A global approach utilizing the contextual information in a scene currently discarded offers the most promise in overcoming the short-comings of current object classification methods.

Intelligent control in traffic management

The goal of an advanced traffic management system (ATMS) is to efficiently manage existing transportation resources in response to dynamic traffic conditions. The utility of an ATMS will greatly depend upon its ability to adaptively respond to traffic patterns and permutations. The application of knowledge-based systems and neural networks provides an ATMS with the technology required to control traffic in an intelligent manner. The volume of traffic combined with the number of streets and intersections an operator control station must monitor clearly dictates the need for computer support. Integrating these technologies with existing transportation methodologies produces a semi-autonomous system capable of reducing operator workloads while maintaining high levels of safety. This paper describes an intelligent traffic management control system called TERMINUS developed to adaptively respond to real-time traffic management problems.<<ETX>>

Implementation Of A Generic Blackboard Architecture

The development of general purpose expert system tools has led to a decrease in the development time of application oriented expert systems. Recently, the need for communicating expert systems has spurred interest in the blackboard architecture. Until blackboard systems are relatively easy to implement; however, their use will be restricted to knowledge engineers willing to write their system from the ground up. This paper describes the development of a blackboard architecture in the Generic Expert System Tool (GEST) developed by the Artificial Intelligence Branch of the Georgia Tech Research Institute. GEST has been developed as a general purpose tool applicable to a wide variety of application domains. Recently, GEST has been enhanced by incorporating a blackboard architecture which allows several GEST expert systems to cooperate with one another. This paper outlines GESTs _software architecture, including its knowledge representation schemes, control sturctures, and blackboard.

The Autonomous Helicopter System

This paper describes an autonomous airborne vehicle being developed at the Georgia Tech Engineering Experiment Station. The Autonomous Helicopter System (AHS) is a multi-mission system consisting of three distinct sections: vision, planning and control. Vision provides the local and global scene analysis which is symbolically represented and passed to planning as the initial route planning constraints. Planning generates a task dependent path for the vehicle to traverse which assures maximum mission system success as well as safety. Control validates the path and either executes the given route or feeds back to previous sections in order to resolve conflicts.

Applications Of Artificial Intelligence

Intelligence evolves out of matter, so said the Sankhya philosophers of ancient India. The discipline of artificial intelligence (Al), which was established some 30 years ago, has confirmed the validity of the above assertion. Recently, a number of AI applications have been successfully demonstrated, generating a great deal of excitement and interest in scientific and technical circles. In this special issue of Optical Engineering a representative set of applications that incorporate Al principles is presented.

Knowledge-Based Tactical Terrain Analysis

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples.

Unmanned guided vehicle system

Unmanned guided vehicles (UGV) require the ability to visually understand the objects contained within their operating environments in order to locally guide vehicles along a globally determined route. Several large scale programs have been funded over the past decade that have created multimillion dollar prototype vehicles incapable of functioning outside of their initial test track environment. This paper describes the Unmanned Guided Vehicle System (UGVS) developed for the US Army Missile Command for operation in natural terrain. The goal of UGVS is to develop a real-time system adaptive to a range of terrain environments (e.g. roads, open fields, wooded clearings, forest areas) and seasonal conditions (e.g., fall, winter, summer, spring). UGVS consists of two primary processing activities. First, the UGVS vision system is tasked with determining the location of gravel roads in video imagery, detecting obstacles in the vehicles path, identifying distant road spurs, and assigning a classification confidence to each image component. Second, the guidance and navigation system computes the global route the vehicle should pursue, utilizes image classification results to determine obstructions in the local vehicle path, computes navigation commands to drive the vehicle around hazardous obstacles, correlates visual road spur cues with global route digital maps, and provides the navigation commands to move the vehicle forward. Results of UGVS working in a variety terrain environments are presented to reinforce system concepts.