Collected notes from the Benchmarks and Metrics Workshop

In recent years there has been a proliferation of proposals in the artificial intelligence (AI) literature for integrated agent architectures. Each architecture offers an approach to the general problem of constructing an integrated agent. Unfortunately, the ways in which one architecture might be considered better than another are not always clear. There has been a growing realization that many of the positive and negative aspects of an architecture become apparent only when experimental evaluation is performed and that to progress as a discipline, we must develop rigorous experimental methods. In addition to the intrinsic intellectual interest of experimentation, rigorous performance evaluation of systems is also a crucial practical concern to our research sponsors. DARPA, NASA, and AFOSR (among others) are actively searching for better ways of experimentally evaluating alternative approaches to building intelligent agents. One tool for experimental evaluation involves testing systems on benchmark tasks in order to assess their relative performance. As part of a joint DARPA and NASA funded project, NASA-Ames and Teleos Research are carrying out a research effort to establish a set of benchmark tasks and evaluation metrics by which the performance of agent architectures may be determined. As part of this project, we held a workshop on Benchmarks and Metrics at the NASA Ames Research Center on June 25, 1990. The objective of the workshop was to foster early discussion on this important topic. We did not achieve a consensus, nor did we expect to. Collected here is some of the information that was exchanged at the workshop. Given here is an outline of the workshop, a list of the participants, notes taken on the white-board during open discussions, position papers/notes from some participants, and copies of slides used in the presentations

A NASA high-power space-based laser research and applications program

Applications of high power lasers are discussed which might fulfill the needs of NASA missions, and the technology characteristics of laser research programs are outlined. The status of the NASA programs or lasers, laser receivers, and laser propulsion is discussed, and recommendations are presented for a proposed expanded NASA program in these areas. Program elements that are critical are discussed in detail

Segmenting broadcast news streams using lexical chains

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling

Intelligent Agent Architectures: Reactive Planning Testbed

An Integrated Agent Architecture (IAA) is a framework or paradigm for constructing intelligent agents. Intelligent agents are collections of sensors, computers, and effectors that interact with their environments in real time in goal-directed ways. Because of the complexity involved in designing intelligent agents, it has been found useful to approach the construction of agents with some organizing principle, theory, or paradigm that gives shape to the agent's components and structures their relationships. Given the wide variety of approaches being taken in the field, the question naturally arises: Is there a way to compare and evaluate these approaches? The purpose of the present work is to develop common benchmark tasks and evaluation metrics to which intelligent agents, including complex robotic agents, constructed using various architectural approaches can be subjected

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

COMPARATIVE CASE STUDY: EXPEDITIONARY FIGHTING VEHICLE AND AMPHIBIOUS COMBAT VEHICLE

The Marine Corps Expeditionary Fighting Vehicle (EFV) program cost taxpayers over $3 billion from inception to cancellation. The Amphibious Combat Vehicle (ACV) attempts to replace the Amphibious Assault Vehicle (AAV) and pick up where the EFV left off. A program comparison can be used to learn from previous management mistakes and prevent failures of this magnitude. By analyzing the two amphibious vehicle programs, I assess pertinent successes and failures against the model with available program management tools, including decision science principles. This report compares key junctures in both programs' life cycles and offers recommendations for future amphibious combat vehicle acquisition. The conclusion reveals that unbalanced cost and schedule increases overpowered the EFV performance goal, leading to cancellation. As a result, the ACV shows less performance but at a lower cost in comparison. Through research, acquisition professionals can better understand the importance of oversight, find solutions, and effectively equip themselves to manage major defense weapon systems.ARPMajor, United States Marine CorpsApproved for public release. Distribution is unlimited

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate