For the past few decades, Rome Air Development Center (RADC) has been conducting research in Artificial Intelligence (AI). When the recent advances in hardware technology made many AI techniques practical, the Intelligence and Reconnaissance Directorate of RADC initiated an applications program entitled Knowledge Based Intelligence Systems (KBIS). The goal of the program is the development of a generic Intelligent Analyst System, an open machine with the framework for intelligence analysis, natural language processing, and man-machine interface techniques, needing only the specific problem domain knowledge to be operationally useful. The development of KBIS is described

Mccown, Michael G.

English

NASA Technical Reports Server

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COOPERATIVE ANALYSIS EXPERT SITUATION 
ASSESSMENT RESEARCH 
Michael G. McCown, 1 Lt, USAF 
Rome Air Development Center 
For the past few decades, Rome Air Development 
Center has been conducting research in the area 
termed Artificial Intelligence. When the recent 
advances in hardware technology made many Artificial 
Intelligence techniques practical, the Intelligence and 
Reconnaissance Directorate of RADC initiated an 
applications program entitled Knowledge Based 
Intelligence Systems (KBIS). The goal of the program 
is the development of a generic Intelligent Analyst 
System, an open machine with the framework for 
intelligence analysis, natural language processing, 
and man-machine interface techniques, only needing 
the specific problem domain knowledge to be 
operationally useful.1 
On the path towards such an architecture, RADC 
first implemented a number of domain specific expert 
systems. The evolutionary design of these individual 
expert systems has rapidly become more modular and 
distributed in itself (Figure 1). The next step of the 
generic design requires that the modular and 
distributed natures of these systems be carried to their 
necessary conclusion, that of loosely coupled, 
distributed, cooperating knowledge based system 
architecture. The effort to explore this step is entitled 
Cooperative Analysis Expert Situation Assessment 
Research (CAESAR). 
architecture used in the KBlS program, it is necessary 
to understand a few fundamentals of intelligence 
analysis and the intelligence community. Much day to 
day intelligence analysis is done by assessing the 
status of indicators. Indicators are sets of actions that 
an enemy would be expected to take in preparation for 
an aggressive act. An indicator list is a list of activities 
that an enemy might be expected to engage in if they 
intended to initiate hostilities. The activities are 
IogicaVplausible moves or acts based on: a) 
Operational procedures, b) Observed activities during 
past conflicts and crises, and c) Results of intelligence 
assessments of enemy strategic offensive military 
doctrine. 
The monitoring of indicators pertaining to strategic 
warning is an extremely time-critical process since this 
warning impacts the degree of readiness of our 
In order to understand the evolution of the current 
strategic offensive and defensive forces. Given the 
increasing threat from a number of sources, the task of 
providing an accurate and timely assessment of 
enemy intent is limited by the availability of qualified 
intelligence analysts and their ability to process 
increasingly large amounts of information in a crisis. 
While some tools exist to assist the intelligence 
analysis function (the World Wide Indicator 
Management System, for example), further research is 
needed to increase the accuracy and timeliness of 
strategic warning. 
Today's automated support for the intelligence 
analyst is limited primarily to Intelligence Data 
Handling Systems, which often times can be as difficult 
to use as the paper pushing methods they replace. 
Yet automated data handling support is dictated by the 
massive volumes of traffic being passed through all 
Indications and Warning Centers. In addition to the 
data flow and management problem, the analysis and 
fusion of that data is becoming increasingly complex 
and subtle as foreign technology and doctrine 
improves. Today's intelligence analyst requires 
phenomenal memory, computer skills, and analytical 
capability in order to be effective. To relieve some of 
the burden, automated support must take the role of an 
informed colleague to the analyst, extending his 
memory, performing data retrievals, and aiding him in 
analysis. In addition, this automated system must 
require little to no time and capability to learn and to 
use. The KBlS program is designed to help alleviate 
this problem. 
Recent efforts have concentrated on the automation 
of indicator assessment, particularly for space situation 
assessment.2 These have included sophisticated 
data base managers as well as single domain expert 
systems designed to maintain indicator lists. However, 
as the Intelligence Community is currently structured, 
different centers have control over different data 
sources, and each exploit these data sources in 
slightly different ways. This necessitates the sharing of 
relevant information between centers and creates two 
important problems. It is difficult to tell exactly what 
information may be relevant so it is often necessary to 
overcompensate by sending too much information, 
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and it is this overcompensation which causes such a 
data flow problem. 
It is the hypothesis of the CAESAR extension of the 
KBlS program that increases in accuracy and 
timeliness of assessments can be made by developing 
a cooperative framework for a number of single 
domain expert systems, and this may allow for a 
possible reduction in data flow over communication 
lines. The architecture will make use of a common 
information exchange language that will enable the 
domain expert systems to request and receive 
information from other KBS's on a shared 
communication network, such as AUTODIN. As a 
result, this system will provide the intelligence analyst 
with an expanded yet focused interpretation of 
strategic indicators developed via cross-domain 
synergism, as well as relieve the data distribution and 
flow problems which currently exist. 
As can be seen in Figure 1, the architecture has 
levels of components: the message processing, data 
base, and active agent. This is the architecture 
currently under implementation in the KBlS program. 
The data base level is separated from the other two 
primarily so as to allow integration of advances in the 
other two areas with existing data bases. This is 
crucial, as current knowledge based systems have 
been shown to be most successful when dealing with 
a deep, narrowly scoped domain. As such, they need 
to be incrementally added to an existing system in 
order to be operationally practical. In addition, the 
intelligence analysis process itself is often broken 
down in this same manner into subanalysis tasks so as 
to be handled by individual analysts, and the 
assessments from these subtasks are combined at a 
higher hierarchical level. This real world process then 
serves as our model for the cooperative expert system 
design. 
The active agent concept is an important one to the 
architecture. Active agent components are so called as 
it should not matter to the other two components 
(message processing and data base) whether the 
communications from that level are from a human 
being or a knowledge based (expert) system. Since 
both theoretically will request and pass similar 
information, that facet should be transparent to the 
architecture. This concept allows for multiple types of 
man-machine interfaces, from existing (dumb terminal) 
types to future sophisticated (smart terminal, intelligent 
MMI) types. Also, this allows for multiple sub-domain 
expert systems working on the same problem in a 
blackboard manner, shown to be a powerful method 
for problem solving. The key here is that these active 
agents are themselves independent, and cooperation 
takes place only at the level of using the same 
communication language.3 This communication 
language within the architecture has a highly formatted 
syntax, but is free as to content of fields. 
The message processing level is a complex and 
critical aspect to this architecture. Since a large 
number of messages are currently passed between 
ir,relligence centers in natural language, the message 
processing level requires a sophisticated natural 
12 iguage processing capability in order to make the 
other two levels effective. It is primarily at this level that 
changes will need to be made in order to have a viable 
distributed network architecture. However, changes at 
this level are strongly linked to the methods used at the 
active agent level. It is the hope of this work that the 
internal communication language currently embodied 
in the Supervisor module which handles the control 
and information transfer between the knowledge 
based systems, man-machine interfaces, and DBMS 
can serve as the baseline for the more general case. 
The general case, however, is quite broad. From raw 
data, to multiple levels of fusion and assessment, to 
hypothesis and warning generation, the types of 
information passed vary widely. In addition to specific 
information requests, broadcasting of desired goals 
and achieved states, and passing of reasoning chains, 
the general case must also include control of 
information transfer, including network protocols, multi- 
level security, communication gateways (network to 
network links), message routing, and routing header 
formats. 
Since the capacity of this internal communication 
language is crucial to the practical success of this 
architecture, it bears futher discussion. While 
numerous attempts have been made in the past to 
rigidly format messages so as to allow for automated 
data processing ease (JINTACCS, for example), these 
attempts always have met with limited success. 
Limited, in the sense that all message formats allow for 
at least one free text area to explain and present 
information which doesn't fit well in the rigid fields of 
the message type. 
This effort makes two assumptions as to the 
feasibility of its internal communication language. 
One, that the intelligence domain has a finite number 
of information sources which can be categorized into 
types (IMINT, HUMINT, SIGINT, etc.), and these types 
can be rigidly mapped into a rigid syntax grammar (or 
vector with fixed fields). Two, these vectors can be 
directly converted and matched to the range of 
knowledge representations currently employed in 
knowledge based systems. Each field in a given 
message type is itself of fixed type but free content. 
Should the content of a field not match a particular bit 
of knowledge in a KBS, the KBS can recognize that 
the content is outside its current scope and either 
initiate some learning algorithm based upon the 
known type and field, or flag the input for a knowledge 
base maintenance function. 
163 
The scope of the CAESAR effort is the development 
of an experimental version of a technological 
capability to perform distributed time critical event 
assessment of foreign military activity. The 
experimental version will demonstrate cooperating 
knowledge-based systems technology for indications 
and warning that performs both domain and cross- 
domain indicator analysis through bi-directional 
communications. The effort will require the design and 
implementation of experimental software for handling 
communications between separate knowledge-based 
systems. The effort will also require the development 
of a structured evaluation scheme (to include test 
procedures, evaluation criteria, etc.) to assess the 
system effectiveness of the experimental version. 
Much of the past research in cooperating expert 
systems has dealt with a tightly coupled cooperation, 
While blackboard concepts are still popular, the 
tendency has been to sacrifice true modularity for 
performance in such systems.4 5 In addition, much of the 
current literature claims that in order to be truly effective, 
KBSs must become much more tightly integrated with 
data bases, and many expert system tools are evolving 
in this direction.6 The author supports this evolution, 
and agrees that such architectures enhance 
performance and effectiveness. However, practical 
considerations have forced the evolution of a loosely 
coupled, distributed Intelligence Community, and an 
architecture which models itself on this has the greatest 
chance of simplified integration and success. The 
applicability of such an architecture, though, would 
seem to extend beyond the needs of the Intelligence 
Community to any environment where a loosely coupled 
architecture is advantageous, such as the space station. 
There is a supporting body of research supporting this 
concept as well.' 
I claiming that better performance can be achieved. 
The KBlS program has developed a number of 
successful stand alone systems which tackle real world 
problems, most notably for space situation assessment, 
launch prediction, and space object identification. The 
success of these systems and the lessons learned as to 
individual architectures have provided the baseline for 
the CAESAR effort. As it is necessary for the persons 
using these systems to share information, it is the 
obvious next step in the evolutionary development of an 
overall architecture for these systems. While success 
seems promising based upon the current research, it 
cannot be overly stressed that the real world is always 
more complex than the most ingenuous laboratory 
environment. Until enough individual systems are in 
place in operational settings to make a test of the 
CAESAR architecture valid, the success of the program 
can only be measured against other academic and 
laboratory research. This evaluation will take place in 
the 1990 time frame. 
Final Technical Report, "The Intelligent Analyst 
System", RADC F30602-83-C-0105, June 1985. 
2 Final Technical Report, "Space Foreign Launch 
Assessment", RADC-TR-86-157 (S), September 1986. 
3 Agha, G., ACTORS. A Model of Concurrent 
ComDutation in Distributed Svste ms, The MIT Press, 
1986. 
Nii, H., "Blackboard Systems: The Blackboard Model 
of Problem Solving and the Evolution of Blackboard 
Architectures", The AI Maaazine, Summer 1986. 
5 Hewitt, C. and Lieberman, H., "Design Issues in 
Parallel Architectures for Artificial Intelligence", lEEE 
COMPCON 84, February 1984. 
6 Howard, H., et. al., "Knowledge-Based Database 
Management for Expert System?, SIGART Newletter, 
No. 92, April 1985. 
7 Genesereth, M., et. al, "Cooperation Without 
Communication", AAAl -86 Proceed in%, Vol. 1, 1986. 
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