u: 

- = r~t.k4>,2~3-, 

.T _-I _--I 

. . -" &, i 2- . 

, .. _ .. . . - < _ ." * .: '*I ; ..I 

. . I _- * U- I 

. .( 

"Con tents 

warhead Design 

Creativity Mahine.,, .,,.-,.,., I 

Director's Corner .,,.,..,,,, 

a+s. . M ... " - - : m+~3-~.iw- : " _ -r- . -- .. _- -. -. i 

. - 

- . . . +-..-r 

. . < _ I' 

News .----------.:..L-: to'. A - , .; ,..,. : 

r. _C -. - ,. - . -. - - &.- ;- ; A? - , i. '.. 

Calendar of EventiL .... ZL,: ....,.,, I i - ..- , . , - - - 

.*r' 

-?:- - .v:- . -Z I, s j , ,, "* \. " 

. " 7- 

, 4. 2 F- - - i - C 

f : . = -: ; 8 . .. = " 8 - - " <* *C . r 1~t lsa DOD Infomalion - - I . Idcenkr5~onsored by he r - : k: . nse Technkal Information ,i$ + " - + 

and!RemLd bg,,iT: .3; ; + , .: 

Research Instilute. .-_r. - : : 

: The w&heacl ueSign. -.:-.y. 

- I .) .- 1 

". - : By stephen 1: flaler --" : L .-. " -,- 

. lrnaginafion Engines,: * i \. -_ . <- ' 4 *.L ,lC .' . %. 

l.0 lntrod;ction ,' : . . . . ., 1% , - . .- _ rq 

A rtificial-ne~al:networkS {AhlN}.were victimized by_s6mehlvery bad press 

in he ir(id,l98Os, as s~me-~~erty~zealous reseaicRers promised more 

than they could pbssiblydeliver, Since thaaitiirrej these unique computational 

devices, inspired by fhe,very benchmarcof mteil@it'nce, fhe human brain, are 

significantly more advanced retheir modelhg +4pbBities,%-Gdap0rn~~nd ease 

of use. : - - :_'.?*-", *_- , - *- - , " " % 

_I_ _- - 4 .. ' 

-T-*. .. . 

Perhaps the most significant develobt&t in &brunch 6f~t h6s l&rrlhhkdiscovery 

of a powerful neural network paradigm that enables ANNs to perform beyond 

their customary role as pattern detectors, allowing them tonow function as novel 

pattern generators that "invent" seminal concepts and plans of action. The machine 

intelligence enabled by this neural net paradigm shift, called a "Creativity Machine" 

(Thaler, .1995A, 1996A, 1 m7B; Yay 1,995B; Holmes, 1 9968; Brown, 1 997A), 

allows computers to butomaticallygkr%at6.new and useful ideas via the pattern- 

. based comput~ng schemer uiilized by the,brain. In contrast to the older schools of 

' artificial intelligence, such as expert siisfems, where sundry rules and atoms of 

, knowledge must be laboriously asternbled and entered into a computational 

. system, such Creativity Machines capiblize upon the extraordinary ability of ANNs 

to simply "watch" a conceptual;~pace and implicitly absorb the fundamental 

entities ~nd heuristics therein. %her; than take the extremely inefficient and ' 

~ compu~ationalI~4~stIY~~hit a$~ssn.approach used by genetic algorihrns to . 2 

discover the,rnosr7dbq c~n~e@,dthG Ereativify Machine (CM) takes the approach . I I 

'used by the human~&itytud $ot blind e~olution. Intelligently, rak than randomly, 

'this advo~~ed neural sysfern arrives at highly optimizedmncepts, including specialhi + . ,,tailored and odaptf~e~.ordnance designs. , < : 5 . / ., 7 . . < 

-; 1.1 The Current Neural lileiwork Paradigm - The Muliilaybr Perceptron- ; , * 

ANNs may be very succinctly described as computer codes that autdnbmoesly . 'write themselves as they are exposed to representative input and.owtput.ptterns. 

7v Ouring this process of autonomous code writing, a system of computersimulated .* /.. I ?? , . ' " + > ,..- -. . 

. -- 

+ , 

on-off switches, called cornputotional neurons, effectively wi~~tkiemselves.tcsgether. :* ' 

('using .what I speak of tonguein-cheek as a "mathematical spankmg,". ,- - .. 7 

.= ._ 2- - 

In practice, .such spankings are achieved through a sew of $orfial-&%&ential 

equations that constitute a process called backpropugafian [~&eJhhrt; 19861. ; . 

Using this methodology, a series of lnput patt&ns-are-;fed- througlj~~essi~ej 

. . layers of neurons and their interconnecting weigk~~, 6t first prbaucing.b. ?kries of % 

initially erroneous output paiferns. Successively, th network's errors; represented , , I the difference between actual and-desired outputiTpro+gate from butpot to . : 

~t end of the ANN, via the back-propagation equations, iteraiively cbrreqting , . > 

L '.' i 1 

. , I - . ,: - . .- k~nfinued-o~ 2). -' 

. . . J . Tv I ;- I. - . ,/ " 

I I. ' _' C - i .- 1. -21 - r . - ~ 

"2). _ " -4 '- .t 1 
(Continued from pge I) 

the numerical values of weights connecting the neurons. After a sufficient 

number of backpropagation cycles, the connection weights, tantamount 

to the expansion coefficients in a traditional statistical fit, produce a 

model that connects the known input-output pairs. When such ANNs 

consist of three or more layers of such computational neurons they are 

called multilayer perceptrons (MLP), and may be used to fit arbitrarily 

complex and often nonlinear functional relationships in very high 

dimensional spaces. The predictive accuracy of such models is contingent 

upon several factors, including the network architecture (i.e., the numbers 

of neurons in each of the successive layers, the functional form taken by 

each neuron, usually of sigmoid form, and a variety of parameters 

intrinsic to the back-propagation algorithm). The reliability of the MLP 

model fit also shares many of the limitations of traditional statistical 

modeling techniques wherein the quality and uniform sampling of data 

patterns are critical. 

Close examination of a trained MLP reveals that the system of 

computational neurons, along with their connection weights, have 

assumed the form of "logic circuits," both discrete and fuzzy, that embody 

the many rules or heuristics embodying the inputoutput transformation. 

After all, the network is developing an accurate model, so it is compelled 

to "grow" an effective internal logic that is tantamount to a theory. 

Therefore, with sufficient dissection of the trained MLP, we uncover the 

critical neural circuitry that combines the various features of the input 

patterns to faithfully produce the individual features of the output patterns. 

In Figure 1, we show the weight architecture of o multilayer perceptron 

that accurotely maps the design parameters of a potential warhead to 

its fragmentation field. It has been trained by supplying a variety of 

known warhead design specifications (i.e., length, width, casing 

thickness, explosive type and weight, geometry, striation pattern, etc.), 

and the associated fragmentation patterns (i.e., Zdata tables that have 

been generated from arena testing). Training input patterns consist of 

these design specifications, and polar sampling window A8, centered 

at 8. Associated training output patterns consist of the initial fragment 

velocity at the polar angle, major fragment weight groups involved, 

and the numerical count of fragments within these respective weight 

groups. Once trained, we may enter as network inputs some set of 

design parameters for the warhead, a polar sampling width A8 and 

then progressively ramp the value of 8 to generate the fragmentation 

field of some real or hypothetical warhead design. 

In other words, such an MLP trained on arena test data may serve as a 

Zdata table generator for new candidate warhead designs, or if 

embedded within a warhead, intelligently readapt the warhead inbound 

toward the target. Currently, using the available arena test data 

assembled from the Joint Munitions Effectiveness Manual (JMEM), rootv weight groups weight group counts & M" board 

figure I. A multilayer ercepiron (top) has trained upon multiple examples of warhead design specifications and corresponding arena test 

data (i.e., hgrnent ve P ocities and fragment distributions). Applying some warhead design specification to the inpuk of this network, we may 

set the polar angle inputs, 0, and the angular collection window, ~0, to calculate he resulting initial fragment velocib weight groups, and 

weight group counts, within that polar sampling window. (Note: in arena fesk, the fragment distribution capbred by wall board spanning A0 

is assessed through manual fragment counts.) [Continued on page 3) 
WSlWC NEWSLmER. 1st Quarter. 2002 

(Continued from page 2) 

meansquare training error is estimated at approximately 5%, reflecting To build an effective generator of new warhead designs we employ a 

the underlying experimental error in counting fragments following an special kind of neural network called an "imagination engine." To build 

arena test. Further, such Zdata generators have achieved 8% root such an inventive net for this particular problem, we train an MLP to 

meansquare error in predicting the fragmentation field for warhead praducean inputoutput mapping whose output patterns represent known 

designs that have been isolated from the original training examples, to warhead designsl. Important to note is that as a result of such training, 

provide a test set to gauge the network's overall predictive power. the net has automatically absorbed all of the implicit constraint 

equations (i.e., heuristics) that dictate how one design parameter, When contrasted with the very complex and costly first principles such as explosive weight, varies with all of the other design parameters. calculations performed using hydrodynamic and chemical Typically, these constraint relations are captured within the final layer of thermodynamic theories, we see the MLP as a very practical and weights of the MLP economical alternative. In effect, the MLP is autonomously devising its 

own warhead fraamentation theorv! The effect that enables such a pretrained MLP to generate a stream of 

1.2 The Future Neural Network Paradigm - 

The Creativity Machine 

Although very powerful, the MLPapproach to warhead design is limited, 

especially when we begin to consider how best to find a warhead 

design that will generate the needed beam spray. It would be very 

convenient, for instance, to use the MLP in reverse, applying some 

sought fragmentation field at the network's outputs and then observing 

the required warhead design to achieve that fragmentation pattern at 

the in~uts. However. MLPs. bv definition. are not bidirectional and , I 

only bredict through forward propagat/on. Unfortunately such an 

approach would fail, even if we were to train a new MLP to map 

fragmentation patterns to design specifications for the following reason: 

the mapping between design specification and beam spray is 

intrinsicallv manvtoone. That is, there are ~otentiallv multi~le warhead 

designs {hat ;ay achieve the same 'Fragmeniationl field. In a 

computational system that allows only one output pattern for any given 

input pattern, the design specification appearing at the outputs of the 

proposed net would effectively represent an average of several design 

solutions, that in itself may be an incorrect one. 

Onevery impractical approach to finding the required warhead design 

would be to apply appropriately scaled random numbers, representing 

potential warhead design parameters, to the inputs of the network shown 

in Figure 1, until closely producing the necessary fragmentation field. 

The overwhelming problem with such a stochastic approach is that the 

vast majority of these potential designs, represented by random patterns, 

would be unphysical or nonsensical. For instance, shell casings may 

not be large enough to accommodate the prescribed weight of explosive, 

or the structural portion of the warhead may be far too heavy compared 

to its yield. These and other myriad constraints must be readily captured 

and exercised when feeding a stream of potential warhead concepts 

to an MLP that maps design specifications to fragmentation field. 

new, potential warhead desigis is one that I publisied (Thaler, 1995A) 

in a variety of scientific publications in the early to mid 00s. This extremely 

important neural network phenomenon is described as follows: If we 

administer transient disturbances (i.e., small algebraic variations) to the 

connection weights that precede the final weight layers of the trained 

MLP, the network tends to rapidly output a sequence of plausible output 

patterns, in this case representing potential warhead designs. The 

observation that each of these output patterns represents a physically 

realistic design owes itself to the fact that each disturbance dealt to a 

connection weight exercises the absorbed and implicit constraint 

equations. In effect, the network is "dreaming" new warhead designs. 

The underlying driver behind these emerging concepts is what I call 

"virtual input effect." (The network is hallucinating outputs, since inputs, 

tantamount to external sensory stimuli, are not being applied to the 

network.) 

The most direct approach to building such imagination engines is through 

the use of what is called on autwssociative nebork. In training such a 

network, known warhead designs, are shown to it, both as the input 

and output patterns. Following training, when any known warhead 

design specification is applied to the inputs of this net, it is "deconstructed" 

through the preliminary weight layer(s), and then "reconstructed" at the 

MLP outputs, using the various constraint rules contained within the 

output weight layer. As in the general case of MLPs, the application of 

mild disturbances to the preliminary layer of connection weights in the 

auto-associative MLP produces plausible warhead designs at the net's 

output layer. 

To make this effect more relevant, imagine that through years of study, 

warhead designers have established analytically represented equations 

of a general warhead, explicitly representing how any design parameter 

depends upon all others: 

casing diameter . = F1 (casing length, explosive h/pe, explosive weight, . . ., fusing scheme) (1 ) 

casing length = F2 (casing diameter, explosive type, explosive weight, . . ., fusing scheme) 

explosive type = F3 (casing length, casing diameter, explosive weight, . . ., fusing scheme) 

fusing scheme = FN (casing length, casing diameter, explosive weight, . . ., casing striation) 

Note that for now, I am noncommittal about the nature of this network's input patterns. 

[Continued on page 4) 

3 
[Continued from page 3) 

Then, implementing this model on a digital computer, random variations 

can be administered to any arbitrary design parameter, as the computer 

algorithm automatically adjusts all others according to these master 

warhead equations (1 1. The truly amazing fact is that the imagination 

engine rapidly absorbs such equations, from raw warhead deign data, 

in a matter of seconds or minutes, and then effectively generates myriad 

design variations through the parallel exercise of all these constraint 

equations through the perturbation of the network's connection weights. 

In addition to experimenting with different values of design parameters, 

any small numerical perturbations delivered to the imagination engine's 

connection weights effectively softens or breaks the implicit rules 

absorbed therein. To this end, Imagination Engines, Inc. (IEl) has 

developed techniques wherein perturbations transiently "hop" among 

the connection weights of the imagination engine, rapidly experimenting 

with rule softening. One simple parameter, the magnitude of the mean 

synaptic perturbation (expressed as the fractional perturbation to each 

connection weight) is adjusted according to how drastically we intend 

to bend the usual design rules. The new warhead concepts are then 

relayed to an MLP of the type shown in Figure 1, that maps each of 

these could-l?e designs to the resulting predicted fragmentation field. 

The governing algorithm hen accumulates a wealth of potential design 

specifications that will provide a desired beam spray pattern. 

We belatedht note that whereas anv MLP could have been used to 

produce output patterns representing known warhead designs, the auto 

associative net is most exdient. for the followina reason: We need 

not seek out associated data that would be usedvto create a hiteroassociative mapping, wherein a warhead inventory code, for instance 

is mapped to design specification. In contrast, all that we require are 

numerous examples of known warhead design specifications. 

Furthermore, the autwssociative version of the imagination engine is 

extremely useful for studying variations on the prototypical designs offered 

through the warhead training exemplars. 

To achieve such design variations, we may apply to the imagination 

engine's inputs, some known warhead design specification, while 

simultaneously administering small, transient perturbations to connection 

weights in the preliminary weight layer. .The result is that the stream of 

output patterns, new potential warheads, represent small design 

modifications to individual features of the warhead, as the other design 

features change in a selfconsistent way to generally preserve the 

absorbed design heuristics. 

2.0 'he Warhead Design Creativity Machine 

A simple Warhead Design Creativity Machine that has already been 

tested2, is shown in Figure 2. There, we show the internally perturbed 

autwssociative imagination engine whose inputs are momentarily set 

to established warhead designs. The synaptic perturbations drive the 

network to produce plausible variations on these prototypical warheads. 

These potential warhead designs are then relayed to the lower critic 

network that now calculates an associated fragmentation pattern. The 

overall search process terminates when the combined networks discover, 

to within some redetermined root-meansauare error, the souaht " 

fragmentation pattern. The associated de;ign specification now 

represents the sought warhead that propels the desired number of 

fragments into the required polar angles. 

I I I 

I I v weight groups weigM group counts 

Figure 2. A Simple Warhead Design Creativity Machine. Starbursfs within the imagination engine produce a series of plausible wartread 

designs, while he lower, critic net, calculates the associated Fragmentation paitem. This process continues until he sought fragmentation field is 

generated. e may hen be varied, at set do, to explore he full angular dependence of he Fragmentation field. 

*contract F086309K-0090 PO0002 with AFRL/MNF, Eglin Air Force Base, FL (Continued on page 51 
WmAC NEWSEITER, 1st Quartet. 2002 

(Continued from page 41 

We note that in this initial effort, with only a limited sampling of warhead 

design specifications, it was only possible to consider a subset of possible 

design parameters that included length, diameter, case thickness, weight, 

explosive iype, explosive weight, and the nature of fusing (nose or tail 

initiation). Rather than solve for the design specification required to 

give the full fragment distribution, we solved for a design specification 

projecting the required fragmentation distribution into some polar angle. 

Then, we were able to explore the full polar dependence of a promising 

design, by maintaining the candidate design specification at the 

imagination engine's inputs, and then ramping the value 8 from 0 to 

1 80 degrees. This process is shown in Figure 3, depicting the system's 

user interface, wherein we modify the characteristics of an exemplary 

fragment distribution and then allow the Creativiiy Machine to solve 

for the required design specification. 

3.0 Rule hdon from fhe Wattmud Design Creativity Machine 

Since the critic network in Figure 2 has formed a highly accurate mapping 

between warhead design specifications and resulting fragment field, 

the necessary "logic circuits" have developed within the critic net that 

reflect the underlying physics of warhead detonation. Using IEl's 

proprietary weight pruning schemes, we may strip away all but the 

relevant connections to visually display these important relationships. In 

Figure 4, for example, is a selectively skeletonized critic network that 

reveals an important connection trace between the Boolean choice of 

tail initiation (yes or no] with the resulting fragment spread. From this 

trace we may readily discern that by choosing tail initiation, we enhance 

the lower fragment weight groups, while opting out of tail fusing (i.e., 

nose initiation) higher weight groups are augmented. 

In a similar manner, we may skeletonize similar connection traces and 

then selectively interrogate them to discern highly valuable 

interrelationships that may of immense use to weapons designers. More 

advanced rule extraction techniques that have been pioneered by IEI, 

are capable of converting a neural to a semantic network, wherein 

logical rules governing the relationship between individual design 

features and the resulting fragmentation characteristics are automatically 

depicted through diagrams incorporating natural language (English]. 

Figure 3. User Inkrhce for Warhead Design Creativiv Machine. Dato point indicated by vertical arrow is modified to alter the Frogmenfation 

paftern. The unden'yng neural nefworks thdn solve For a warhead design specifkation delivering fhis frogmentation field. 

Thereafter, the system may calculate comparative plots showing the variation of fragmentation pattern as the various design parameters are 

varied (right). 

/Continued on page 6) 

5 
{Continued from page 5) 

tail inltiafm 

wl group 4 

wt gmup 5 

wt gmup 6 

wt gnup 10 -7 

tar1 initiation =false 

wt group 11 / 

Figure 4. Rule mdion horn the Underlying Neural Networks. Here the critic nekork of the Warhead Design Geativiy Machine has pruned 

itself of unnecessary connedion weights to reveal how the presence or absence of tail inifidon toggles the distribution of weight groups. The 

upper link contains an excitatory connectron (deep purple), while the lower link (pale purple) is inhibitory Therefore the upper link labeled "tail 

initiation = true" tums on the weight groups 4, 5,6, and 7, while the lower trace marked "tail initiation = false" turns on weight groups 9, 10, 

and I I. The color legend shows the relative shngths of the weights involved in these connection traces. 

4.0 Planned Improvements in he Warhead Design Creativity 

Machine 

The foremost problem faced in training the Warhead Design Creativity 

Machine has been a shortage of available arena test data. In this first 

proofaf-principle experiment, only seven distinct and unclassified 

warhead designs were considered, combining JMEM derived design 

specifications with the results of corresponding arena tests carried out 

at the AFRLMunitions Directorate at Eglin AFB. The primary limitation of 

the overall system currently is the lower critic network pictured in Figure 

2, that has demonstrated an approximate 8% root-meansquare 

prediction error for warhead designs swapped out of the original training 

set and then submitted to the network to sample generalization capacity. 

Because of this current limitation, the accuracy of the Creativity Machine 

may be lacking, especially as the imagination engine generates designs 

far removed from those used as training exemplars. For this reason, trial 

runs of this CM were run at low perturbation levels to produce warhead 

designs that were only slight variational themes on those seen by the 

imagination engine through training. 

Based upon post Creativity Machine projects there is no doubt that as 

additional training exemplars are supplied, the ability of this architecture 

to produce reliable new designs will vastly improve. Likewise, as 

observed in past CM efforts, there will inevitably be a bootstrapping 

phase wherein new recommended designs are actually built and tested. 

Iteratively, through cumulative mistakes, Creativity Machines, iust as 

people, become progressively more reliable in producing new, reliable, 

and useful concepts. 

5.0 Automation of the Warhead Design Creativiiy Machine 

Recent advances at IEl have led to a totally new type of MLP known as 

a "Self-Training Artificial Neural Network Objects" or "STANNO" Faler, 

1996C, 1998). To make a very long story short, these neural network 

class templates contain their own integrated training algorithm. The 

very useful result is that multiple, self-training networks may be instantiated 

and combined into cascade structures, such as the Creativity Machine 

shown in Figure 2. Each of these STANNOs may then train in situ, 

without the necessity of removing and individually training each upon 

its associated exemplar database. Since most of the subtleties ordinarily 

dealt with by neural network practitioners are handled automatically 

within the STANNO, the overall Creativity Machine may be trained by 

those with little or no expertise in the area. This autonomy is extremely 

valuable to those working with classified, need-to-know data. 

Appropriately cleared personnel, without extensive knowledge of neural 

networks, may now train and exercise Creativity Machines through 

simple menu selections and mouse clicks. 

The autonomy of the STANNO has also contributed to the development 

of large neural networks that may be implemented as client-server 

applications. Thus, some centralized neural network may be 

collaboratively trained by multiple users around the country via secure 

TCP/IP connections. In this manner multiple services could be supplying 

arena test data to a master neural network that is able to absorb the 

implicit rules relating warhead design and their resulting potencies. 

Through the client applications, users could independently query this 

central network for Zdata tables of hypothetical warhead designs. 

Similarly, the component networks of a warhead design Creativity 

Machine could be trained and interrogated in a similar clientserver 

scheme. 

{Continued on poge 7) 
(Continued from page 6) 

6.0 Planned Refinements to the Warhead Design Creat~ty 

Machine 

The next obvious step in improving the Warhead Design Creativity 

Machine is to enlarge the critic network to calculate the full fragmentation 

field (i.e., 0 < 8 < 180) at once. To accomplish this task, much more 

arena test data will be required than was utilized in the protoype 

system, perhaps resorting to physics-based supercomputer simulations 

of arena tests. Once completed though, Zdata files generated for 

hypothetical weapons designs may be output to existing ray trace 

programs such as MNA, to calculate blast effects upon various types 

OF target structures (i.e., buildings, bunkers, etc.] 

Additional critic modules may be added that monitor for other kinds of 

effects, including collateral damage to personnel, structures, and 

materials. Furthermore, additional Creativity Machine levels may be 

added that generate new potential chemical explosives that may be 

incorporated into the candidate warhead designs. Such an ancillary 

explosive materials Creativity Machines could be especially critical as 

new potential explosive materials, unaccompanied by thermodynamic 

data, are synthesized. Considering that extant codes such as JAGUAR 

rely upon chemical free energies of formation, this explosives discovery 

system would offer an immediate alternative to this highly successful 

code, perhaps absorbing that algorithm, and then refining itself through 

exposure to the behavior of new explosive materials and their highly 

irreversible thermodynamics. 

7.0 The Future of Creativity Machines 

Implicit in this discussion is the message that the Creativity Machine 

paradigm is a perfectly general methodology that may be applied to 

any conceptual spce or area of human endeavor. Since these systems 

are totally autonomous in their formation, and because no explicit 

heuristics need be gleaned from their respective conceptual spaces, 

they may be functional and producing new concepts and courses of 

action in very little time. For this reason, Creativity Machines, serving 

as viable alternatives to and improvements over genetic algorithms, are 

currently providing solutions within in a variety of problem areas including 

materials discovery, personal hygiene products, synthetic music, robotics, 

communication satellite scheduling, as well as weapons design. 

Therefore, the sky is the limit as far as future applications of this very 

fundamental neural network paradigm, in both the civilian and military 

sectors as it offer's Al's "best bet" (Bushnell, 2001) to a fully creative 

brand of artificial intelligence. . 

8.0 References 

Rummelhart, D. E. (1 986), Parallel Distributed Processing: Explorations 

in the Microstructure of Cognition, Volume 1 : Foundations, MIT Press, 

Cambridge, MA, 328-330. 

Thaler, S. L. (1 995A) "Virtual Input Phenomena" Within the Death of a 

Simple Pattern kiator, Neural Nehworks, 8(1), 55-65. 

Yam, P. (19958) As They Lay Dying ... Near the End, Artificial Neural 

Networks Become Creative, Scientific American, May 1995. 

Thaler, S. L. ( 1 996A) Neural Networks That Create and Discover, PC 

Al, May/June 1996. 

Holmes, R. (1 9968) The Creativity Machine, New Scientist, 20 January 

1 996. 

Thaler, S. 1. (1 9969 SelkTraining artificial Neural Networks, PC Al, 

Nov/Dec 1996 

Brown, A. (1 W7A) Computers that create: No hallucination, Aerospace 

America, January 1997 

Thaler, S. L. (1 9978) US05659666 - Device for the autonomous 

generation of useful information, issued 08/19/1997 ond divisional 

patents 

Thaler, S. 1. (1 998) US05845271 - Non-Algorithmically implemented 

artificial neural networks and components thereof, issued 1 2/01 / 1998 

and divisional patents 

Bushnell, D. M. (2001). Future Strategic Issues/Future Warfare [Circa 

20251, NASA Langley Research Center. 

About the Author: Dr. Stephen 7haler has been the President & CEO of Imagination Engines, Inc. since 1995. He is also the Principal Scientist 

for Sytex, Inc. Previously he was a Production Chemist for Mallinckrodt Nuclear I1 973-74) and a Principal Technical Specialist at McDonnell 

Douglas 1 98 1 - 1 995). He has a B.A. from Westminster College, a Ph. D. in physics from the University of MissouriColumbia, and is a graduate 

of McDonnell Douglas Voluntary Improvement Program in Artificial Intelligence. Dr. Thaler is an experf in advanced neural networks and holds 

a number of patents in this area. He can be contacted at /3 14) 576 16 17, sthaler@imaainationen4ines.com or through the /El web site at 

h#p://www. imaginationengines.com. 

Smart Precision Weapons Training Seminar 

LOCATION: Huntsville, Alabama, 2002 

5-7 March 23-25 Apn'l 18-20 June 20-22 August 

[Seminar starts at 8:00 AM Tuesday and ends at 12:OO PM Thursday) 

The Weapon Systems Technology Information Analysis Center (WSTIAC) developed this 2% day Smart Weapons Training Seminar to provide a 

comprehensive understanding of smart weapons and related technologies. This seminar is aimed at providing general knowledge about smart 

weapons technology and a source of current information on selected U.S. and foreign smart weapons, to include system description, concept of 

employment, performance characteristics, effectiveness and program status. 

For additional information, contact: Ms. Kelly Hopkins, Seminar Administrator, 

at (256) 382-4747, fax: (256) 382-4702 or by e-mail: khopkins@ iitri.org 
by Dr. Wes Kitchens 

H omeland defense has become a high priority for our 

Nation as a result of the September 1 1 th terrorist attacks. 

Many new initiatives have been started in direct response 

to these terrorist attacks and the White House Office of Homeland 

Security has been established with former Governor Tom Ridge 

(PA) as Director. The Office of Homeland Security is responsible 

for conducting cross-agency coordination, identifying how much 

money each agency is spending on homeland defense, 

quantifying the gaps that exist in current programs and identifying 

what needs to be done to fill them. Many of the technologies 

we have developed for weapons systems can and will be 

employed to support our homeland defense efforts. Three 

technologies that immediately come to mind are: (1) sensing 

and identifying chemical and biological agents; (2) detecting 

and neutralizing explosives and unexploded ordnance; and (3) 

air and missile defense. Air and missile defense is thought by 

many to be a critical component of any robust homeland defense 

strategy and it will undoubtedty receive increased attention. The 

United States recently notified Russia that we were going to 

withdraw from the 39ear old Anti-Ballistic Missile (ABM) Treaty. 

President Bush explained why we were doing this during his 

remarks in the Rose Garden on 13 December 2001 : "1 have 

concluded the ABM treaty hinders our government's ability to 

develop ways to protect our people from future terrorist or rogue 

state missile attacks. We know that the terrorists, and some of 

those who support them, seek the ability to deliver death and 

destruction to our doorstep via missile. And we must have the 

freedom and the flexibility to develop effective defenses against 

those attacks." 

No matter what you may think about the prospects for achieving 

a robust missile defense system, it is clear from President Bush's 

remarks that this administration plans to pursue such a system. 

As most of you already realize, the United States currently has 

no capability to shoot down a ballistic missile launched against 

an American city. This poses a critical risk to the United States 

and we need to apply an appropriate level of resources to 

reduce our vulnerability to this threat. We are attempting to 

develop a missile defense system to defend the forces and 

territories of the United States, our allies and friends against 

ballistic missile threats from all ranges, with the goal of achieving 

a layered defense that will provide multiple engagement 

opportunities along the total flight path of a threat missile. The 

Missile Defense Agency, MDA (formerly BMDO) has successfully 

demonstrated that a long-range intercontinental ballistic missile 

target vehicle launched from Vandenberg AFB, CA can be 

intercepted over the Pacific Ocean by a prototype hit-tokill 

missile launched from 4800 miles away in the Marshall Islands. 

The impressive test results (3 successful intercepts out df 5 tries) 

confirm that it's possible to hit a bullet with a bullet, at least in 

the case of a relatively benign threat that doesn't employ countermeasures. General Kadish and the MDA are congratulated on 

these recent successes and the foundation they provide for 

our future missile defense system. While it's true that the 

administration's missile defense plans remains controversial in 

some circles, our missile defense efforts will clearly grow in 

importance as we focus more attention and resources on meeting 

our homeland defense requirements. 

This issue of the WSTMC Newsletter features a technical paper 

entitled "The Warhead Design Creativity Machine," by Dr. 

Stephen L. Thaler, Imagination Engines, Inc., Dr. Thaler's article 

discusses how advanced neural network technology can be 

applied to "predict" the fragmentation pattern of a warhead. 

One can imagine a situation at some time in the future where 

algorithms such as Dr. Thaler's are embedded in guidance 

integrated fuze "nanochips" and used to selectively fuze an inbound smart munition to optimize its fragmentation pattern 

depending on the target type and the munition/target encounter 

dynamics. 

We welcome your feedback about the WSTMC Newsletter and 

solicit your suggestions for future topics you would like to see 

included. You can reach me by email at wkitchens8iifri.org or 

by phone at (703) 933-33 17. 
WSTlAC NMLETTER. 1st Quarter, 2002 

DoD Studl~ Medlcal Impact of 

Depleted Uranlum In me Balkans 

The Department of Defense released an information paper, "Depleted Uranium 

Environmental and Medical Surveillance in the Balkans," which summarizes medical 

and environmental assessments performed in the Balkans area by a number of 

countries. On he whole, these assessments have not found any connections 

between depleted uranium exposure in the Balkans and negative health effects. 

Most of the work assessed was done independently, by organizations outside the 

Defense Department. The information paper examines assessments performed by 

the United Kingdom Royal Socieiy, the World Heolth Organization, the United 

Nations Environmental Programme and others. 

Investigators supporting the special assistant for Gulf War illnesses, medical 

readiness and military deployments compiled and analyzed those reports and 

referenced them in the new information paper. Most of the cited references are 

available in public libraries or on the Internet. 

The special assistant's support staff is the same organization that previously 

investigated depleted uranium use in the Gulf War. Both tanks and aircraft 

employed depleted uranium ammunition in the Gulf War. However, all the 

depleted uranium used in the Balkans was in the form of 30millimeter rounds 

fired from Air Force A-10 aircraft. About 10,000 rounds were fired in Bosnia 

and approximately 3 1,000 rounds were fired in Kosovo. That adds up to neorly 

13 tons of depleted uranium, much less than the 320 tons of depleted uranium 

used during the Gulf War. 

Concerns about possible health effects of depleted uranium in Europe were first 

raised by newspaper reports. Italian media reports initially tried to link an apparent 

rise in the incidence of leukemia in Balkon veterans to exmure to de~leted 

uranium. After an extensive scientific study, the Italian governhent concludA that 

the incidence of leukemia was not as high in Balkan veterans as it was in the 

general population. Many other countries started medical screening programs 

for their Bosnia veterans. So for, none has reported elevated uranium levels in 

their soldiers' urine, or any negative health consequences they attribute to depleted 

uranium exposure. 

Because depleted uranium is a heavy metal, it can be potentialh/ harmful under 

certain circumstances. For that reason, the NATO nations have instituted training 

in the safety precautions to use in an area where depleted uranium was used 

militarily. Much of this training is based on the training programs created by the 

U.S. Army. 

Information papers are reports of what the Defense Department knows today 

about military procedures and equipment. This information paper is intended to 

provide a basic understanding of depleted uranium use in the Balkans. Although 

not an investigative report, the report will be updated if additional information 

becomes available. This report is posted on the DeployrnentLlNK Web site at ' htfp://deploymentlink.osd.miI/dv-baIkans/index.html 

1 NEWS RELEASE from the United States Department of Defense No. 552-01 

! Oaober 301 2M)1. 
Who Ya Gonna Call? Airborne Laser Changes Affiliation 

When bndlinemmunications link are on he friband itie job is too massshe for KlRTlAND AIR FORCE BASE, N.M. - In a brief ceremony here on Friday 

local cellular communications, the Navnl Research tabomton/ (NRL) in Wash (Oct. 1 2) the Airborne Laser (ABL) formally ended its nearly decadelong 

ington, D.C., stand mdytoanswerheall. NRI has cutfitted Humwith he affiliation with he Space and Missile System Center (SMC), transitioning 

nagement functions to the Aeronautical Systems Center 

eties but was moved forward this summer 

SMC to its Space Command. Since ABL 

e program, the transition to ASC was 

Restoration), the Hum commun 

Originally designed to support several U.S. Navy and Marine rce Materiel Command, also 

operations, the modular communications components installed mbers of the Kirtland 

Humvees are entirely selfcontained and can be configured to fit the nsfer to ASC slots but all 

of a particular civilian or military situation. nnel will remain in NewMexico. On November 1, ABL transferred 

Ballistic Missile Defense 

Emergency Management Ad ms for all the services. communications assistance in the days following New York 

Trade Center collapse. Just when the Humvees were to set off stphase ballistic missile 

City, FEMA decided additional communications assistance wa ich features a megawattclass 

bvt the exercise proved to the a modified Boeing 747-400 

the Humveebased communi ds for prototype Attack Laser, 

ity in Wichita, Kan., on 

Multipurpose Wheeled 

vehicles produced for ci 

For more information on NRCs H 

you are working media 

ernail haam@ onr.novy. 
WmAC NEWSLETTER, 1st Quarkr. 2002 

9-20 March 2002 29 April-1 May 2002 

Navy Bollistic Missile Defense Conference Fuze Conference 

Crystal City Forum Marriott River Walk 

Arlington VA San Antonio TX 

For additional information: For additional information: 

Email: rmohrmann@ndia.org Email: rmohrmannQndia.org 

http://register.ndia.org/interview/register.ndia?-Brochure-2 1 60 http://register.ndia.org/interview/register.ndia?-Brochure-2560 

1 9-2 1 March 2002 61 0 May 2002 

2002 Joint Undersea Warfare Conference Live Fire Test and Evaluation 

San Diego CA Monterey CA 

For additional information: For additional information: 

Email: kwilliams@ndia.org Email: pedmonson@ndia.org 

http://register.ndia.org/interview/register.ndia?-Brochure-2260 http://register.ndia.org/interview/register.ndia?-Brochure-2 190 

25-28 March 2002 

NorrLethal Defense V 

Hyatt Regency 

Reston VA 

For additional information: 

Email: asaliski@ndia.org 

http://register.ndia.org/interview/register.ndia?-Brochure-2420 

1-5 April 2002 

AeroSense 

Gaylord Palms Resort and Convention Center 

Orlando FL 

For additional information: 

http://spie.org/Conferences/Calls/O2/or/ 

1 5-1 8 April 2002 

Gun and Ammunition Conference and Exhibition 

Panama City FL 

For additional information: 

Email: djenks@ndia.org 

http://register.ndia.org/interview/register.ndia?-Brochure-2590 

17-1 9 April 2002 

Missiles and Rockets Conference and Exhibition 

Panama City FL 

For additional information: 

Email: dienks@ndia.org 

http://register.ndia.org/interview/register.ndia?-Brochure-2630 

2 1-26 April 2002 

High Power Laser Ablation 2002 

Taos NM 

For additional information: 

http://spie.org/Conferences/CalIs/02/hp/ 

11-15~a~ 2002 

IEEE lnternational Conference on Robotics and Automation 

Crystal Gateway Marriott Hotel 

Washington DC 

For additional information: 

http://www.icra-iros.com/icra2002/index.htmI 

27-29 May 2002 

9th Saint Petersburg lnternational Conference on Integrated 

Navigation Systems 

St Petersburg Russia 

For additional information: 

Contact: Dr. John Niemela 732 427 4635 

Email: john.niemela@maill.monmouth.army.mil 

http://www.elektropribor.spb.ru/confs/icins02/index.html 

1 8-20 June 2002 

2002 Warheads and Ballisfics Classified Symposium 

Colorado Springs CO 

For additional information: 

broulstone@ndia.org 

2 1-25 July 2002 

Joint Advanced Weapon Systems Sensors, Simulation and Support 

Symposium (JAWS S3) 

Air Force Academy 

Colorado Springs CO 

For additional information: 

CGriffith@anteon.com 

23-27 September 2002 

20th lnternational Symposium on Ballistics 

Rosen Center Hotel, Orlando FL 

For additional information: 

http://register.ndia.org/interview/ 

regi~ter.ndia?PID=Brochure&SID=~OKOOXOZV8&MlD=22 1 0 
lnformation Request 

Please return this form to.. . 

IIT Research Institute/WSTIAC 

ATTN: Publication Department 

215 Wynn Drive, Suite 101 

Huntsville, At 35805 

or fax.. . 

Add my name to the WSTIAC Newsletter mailing list Correct my address information (see below) 

Please send WSTIAC Technical Area Task Information (government) 17 Please send WSTIAC Subscriber Information 

(industry) 

Please send WSTIAC general information Please send WSTIAC publications list 

Name: 

Organization: 

Address: Telephone: 

Fax: 

Please also add my name to the following mailing lists: 

MTlAC RAC AMmC MSlAC IATAC 

Manufacturing Reliability Advanced Modeling and Information 

Technology Maintainability Materials Simulation Assurance 

Qualify and Processes and Testing 

A WSTIAC 

215 Wynn Drive, Suite 101 

Huntsville, At 35805 

1901 N. Beauregard Street, Suite 400 

Alexandria, VA 223 1 1 

Inside this issue ... 

Warhead Design Creativity Machine 

Director's Corner 

FYI 

Smart Weapons Training Seminar 

News 

Calendar of Events 

Nonprofit Organization 

U.S. Postage Paid 

Huntsville, At 

Permit No. 4