Mixed-initiative control of intelligent systems

Mixed-initiative user interfaces provide a means by which a human operator and an intelligent system may collectively share the task of deciding what to do next. Such interfaces are important to the effective utilization of real-time expert systems as assistants in the execution of critical tasks. Presented here is the Incremental Inference algorithm, a symbolic reasoning mechanism based on propositional logic and suited to the construction of mixed-initiative interfaces. The algorithm is similar in some respects to the Truth Maintenance System, but replaces the notion of 'justifications' with a notion of recency, allowing newer values to override older values yet permitting various interested parties to refresh these values as they become older and thus more vulnerable to change. A simple example is given of the use of the Incremental Inference algorithm plus an overview of the integration of this mechanism within the SPECTRUM expert system for geological interpretation of imaging spectrometer data

Moebius Language Reference, Version 1.2

Moebius is a representation and interface language based on a subset of English. It is designed for use as a means of encoding information and as a means of conveying information between software components and other software components, between software components and humans, and between data repositories and their users -- human or machine. This report describes the structure and use of the Moebius language and presents three applications of the language to date

STAR (Simple Tool for Automated Reasoning): Tutorial guide and reference manual

STAR is an interactive, interpreted programming language for the development and operation of Artificial Intelligence application systems. The language is intended for use primarily in the development of software application systems which rely on a combination of symbolic processing, central to the vast majority of AI algorithms, with routines and data structures defined in compiled languages such as C, FORTRAN and PASCAL. References to routines and data structures defined in compiled languages are intermixed with symbolic structures in STAR, resulting in a hybrid operating environment in which symbolic and non-symbolic processing and organization of data may interact to a high degree within the execution of particular application systems. The STAR language was developed in the course of a project involving AI techniques in the interpretation of imaging spectrometer data and is derived in part from a previous language called CLIP. The interpreter for STAR is implemented as a program defined in the language C and has been made available for distribution in source code form through NASA's Computer Software Management and Information Center (COSMIC). Contained within this report are the STAR Tutorial Guide, which introduces the language in a step-by-step manner, and the STAR Reference Manual, which provides a detailed summary of the features of STAR

A Suite of Techniques for Describing Activity in Terms of Events

This report presents a set of software techniques that support the tasks of event recognition, summarization of event sequences, explanation of recognized events, explanation of non-recognized events, prediction of event completions, and question answering by leveraging language-encoded human knowledge of what typically happens during various types of events. The techniques operate on sequences of timestamped, three-dimensional positions and contacts for humans, body parts, and objects, provided by a Microsoft Kinect sensor plus associated software. Appendices describe 64 activity sequences used for development and testing of the techniques and 102 event models created as part of the effort

Causal Reconstruction

Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. T his task is difficult because written d escriptions often do not specify exactly how r eferenced events fit together. This article (1) ch aracterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions,'' or collections of changes expressible in everyday language, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity

A Ransomware Case for Use in the Classroom

Given the global growth in ransomware attacks, employees need to understand the risks of ransomware and how to protect against it. This paper presents a teaching case based on an actual ransomware attack on a hospital that undergraduate or graduate course can use to teach students. The case introduces students to Wildcat Hospital, a fictitious 450-bed acute-care facility in a suburban location in the Northeastern United States. A ransomware attack hit Wildcat Hospital as the workday began. Malware infected the hospital\u27s computers and demanded one bitcoin, a virtual currency that affords anonymity, as ransom to restore functionality of the information systems. The chief executive officer and the chief information officer led the organizational response to the attack. We include links to two videos, a demo of a Locky ransomware attack in action, and a National Broadcasting Company (NBC) TV network news report about a similar ransomware incident at another hospital (Hollywood Presbyterian Medical Center in California) to engage students