A PROTOTYPE ANALYSIS SYSTEM FOR SPECIAL REMOTE VIEWING TASKS

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For Release 2000/08/08 : S rE789R002200570001-5 Final Report-Task 6.0.3 October 1989 Covering the Period 1 October 1988 to 30 September 1989 A PROTOTYPE ANALYSIS SYSTEM FOR SPECIAL REMOTE VIEWING TASKS (U) By: Wanda L. W. Luke Thane J. Frivold Edwin C. May Virginia V. Trask Prepared for: Contracting Officer's Technical Representative SRI Project 1291 WARNING NOTICE RESTRICTED DISSEMINATION TO THOSE WITH VERIFIED ACCESS TO THE PROJECT NOT RELEASABLE TO FOREIGN NATIONALS SECRET 333 Ravenswood Ave. ? Menlo Park, CA 94025 $$ led For Rele~asei2~~~~i /U8TvA91 ~W6?b~~l89ROe~22d0- 0'001-5 REMOTE VIEWING TASKS (U) Final Report-Task 6.0.3 October 1989 Covering the Period 1 October 1988 to 30 September 1989 A PROTOTYPE ANALYSIS SYSTEM FOR SPECIAL By: Wanda L. W. Luke Thane J. Frivold Edwin C. May Virginia V. Trask Prepared for: Contracting Officer's Technical Representative SRI Project 1291 For Release 2000/08/08: P&'f 789R002200570001-5 WARNING NOTICE RESTRICTED DISSEMINATION TO THOSE WITH VERIFIED ACCESS TO THE PROJECT Approved by: Copy 2 of 5 Copies MURRAY J. BARON, Director This document consists of 27 pages Geosclence and Engineering Center SRI/GF-0321 CLASSIFIED BY: HO, USAMRDC (SGRD-ZA) DECLASSIFY ON: OADR NOT RELEASABLE TO FOREIGN NATIONALS SECRET 333 Ravenswood Ave. ? Menlo Park, CAII 94025 d For Release 170~~~~08 TA&~9~628089R~U~2200570001-5 Approved For Release 2000/08/08 : CI OK 0789R002200570001-5 ABSTRACT (U) (S/NF) We have developed a prototype analysis system for remote viewings conducted against targets of intelligence interest. The system uses individual viewers' performance histories in conjunction with current data to prioritize a set of possible intelligence interpretations of the site. SECRET Approved For Release 2000/08/08 : CI R P96-00789R002200570001-5 Approved For Release 2000/08/08UN1 L &$ J1OtE R002200570001-5 (U) TABLE OF CONTENTS ABSTRACT .................................................................ii LIST OF TABLES ........................................................... iv LIST OF FIGURES ........................................................... iv I INTRODUCTION (U) ...............................................1 II METHOD OF APPROACH (U) ...................................... 2 A. (U) Fuzzy Set Formalism ....................................... 2 B. (U) Prototype Analysis System ................................... 5 C. (U) Partial Application of Analysis System to Existing Target Pool ..... 7 D. (U) General Conclusions ......................................12 REFERENCES .............................................................. 13 APPENDIX A ................................. ............................14 APPENDIX B ................................. ............................15 UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 (U) LIST OF TABLES 1. (U) Numerical Listing of Targets ........................................... 8 2. (U) Technology Cluster .................................................. 11 3. (U) Principal Elements Contained in the Technology Template .................. 11 (U) LIST OF TABLES 1. (S/NF) Cluster Diagram for Simulated Operational Targets ..................... 10 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Approved For Release 2000/08/08 : CI.p0789R002200570001-5 I INTRODUCTION (U) (U) Since 1973, when the investigations of the human information-accessing capability called remote viewing (RV) first began at SRI International,'* evaluating the quality of the information obtained has been a continuing challenge. In order to develop valid evaluation procedures, two basic questions must be addressed: (1) What constitutes the target? (2) What constitutes the response? (S/NF) If the RV task is research-oriented, the targets are known, and therefore can be precisely defined. In intelligence-oriented tasks, however, the targets are generally unknown and their descriptions are problematical. In both task domains, RV responses tend to consist of sketches and written phrases. A method to encode unambiguously this type of "natural language" is one of the unsolved problems in computer science, and there has been little progress to date. Thus, a complete definition of an RV response is also problematical. (S/NF) An intelligence-oriented RV task poses further problems. High-quality RV does not always provide useful intelligence. For example, the RV may provide additional support for information that has been verified from other sources, but provide no new information. In some cases, however, an overall low-quality RV may provide key elements that positively influence an analyst's interpretation. (S/NF) Another characteristic of current laboratory analysis techniques is that they do not provide an a priori assessment of the RV quality. While this is not a problem in the laboratory, intelligence applications require such evaluation. An RV analyst cannot provide intelligence usefulness ratings from the RV alone; rather, the analyst must provide a priori probabilities that individual RV-response elements (or concepts) are present at the target site. It remains the responsibility of an intelligence analyst to determine whether such data are ultimately useful. (S/NF) Analysis of laboratory RV has been a major part of the ongoing Cognitive Sciences Program.2-7 For FY 1989, we focused on the development of a prototype analysis system that would provide the needed a priori assessments for intelligence tasking.t * (U) References are at the end of this report. t (U) This report constitutes the deliverable for Statement of Work item 6.0.3. Approved For Release 2000/08/08 : CF$l rAT00789R002200570001-5 Approved For Release 2000/08/08: CIq:. ffg _f 789R002200570001-5 II METHOD OF APPROACH (U) (S/NF) The analysis of remote viewing (RV) data in an operational environment differs considerably from laboratory analysis. Most often, analysts have incomplete or no information about the target site and are required to provide a priori assessments of data gathered from RV sessions. In this section we outline a prototype analysis system for operational RV that uses concepts from fuzzy set theory, historical archival data, and "templates" of typical operational targets. In addition, we apply this prototype system to an existing target pool as an illustration of the power of the technique. A. (U) Fuzzy Set Formalism (S/NF) A more complete description of the full fuzzy set formalism can be found in our literature.6'7 For the purpose of this report, we have summarized that formalism in general terms that are not specific to either laboratory experiments or intelligence tasking. 1. (U) Construction of Target and Response Fuzzy Sets (U) A formal definition of a target and its associated RV response (i.e., the data obtained from an RV session) is necessary to any analysis system. To use the fuzzy set method, a universal set of elements is constructed on which target and response descriptions are based. These elements should contain descriptive aspects of the target material and incorporate items that typify responses from the intended viewers. This universal set should also be extendible (i.e., allow for additional items that may arise in the responses). (U) In general, the task of an RV analyst is to assign a membership value (?) between 0 and 1 to each element in the universal set. The numerical value for each element in a response is assigned by the degree to which the analyst is convinced that the given element is present in that response. Membership values for target elements are assigned on the basis of the degree to which the elements contribute to the target description. (S/NF) In the laboratory, the targets are known, so that defining a universal set of elements is comparatively straightforward.6.7 In intelligence tasks, however, defining a single universal set of elements that is appropriate for all operations is difficult. Because the usual intelligence task is so highly mission-dependent, defining a single universal set of elements that is customized to that mission becomes easier. 2 AW Approved For Release 2000/08/08: ClISWR T00789R002200570001-5 Approved For Release 2000/08/08 89R002200570001-5 (S/NF) The intelligence analyst, as opposed to an RV analyst, should construct such a list for each mission. While there may be considerable similarities between element lists for different missions, undoubtedly the lists will require specialization. In Section II-C below, we show the construction of one element list and how it can be applied to a set of 65 simulated operational targets. 2. (U) Analysis of Complete Responses (S/NF) Once an appropriate universal set of elements has been created, and fuzzy sets that define the target and the response have been specified, the comparison between them is straightforward. We have defined accuracy as the percent of the target material that is described correctly by a response. Likewise, we have defined reliability (of the viewer) as the percent of the response that is correct.6 Although in the laboratory it is required to provide a posterior probability estimates of the target-response match, in an operational setting, this may be less important. All that is usually necessary is to describe the accuracy and reliability for complete responses, and for individual target elements of interest. These quantities for the jth sessions are n i Wk (Rj n Tj)k 1 (1) WkRj,k n i Wk (Rj n Tj) k 1 aj - n (2) 1: WkTj,k where the sum over k is called the sigma count in fuzzy set terminology, and is defined as the sum of the membership values (?) for the elements of the response, the target, or their intersection, and n is the number of possible elements as defined by the element list. A fuzzy intersection is defined as the minimum of the intersecting fuzzy set membership values. In this version of the definitions, we have allowed for the possibility of weighting the membership values, Wk, to provide mission-defined relevances. (U) For the above calculation to be meaningful, the membership values for the targets must be similar in kind to those for the responses. For most mission-dependent specifications, this is generally not the case. The target membership values represent the degree to which a particular element is characteristic of the target, and the response membership values represent the degree to which the analyst is convinced that the given element is represented in the response. Approved For Release 2000/08/08: GI/ RM70789R002200570001-5 Approved For Release 2000/08/Oaj q ij 9R002200570001-5 (U) Until RV abilities can encompass the recognition of elements as well as their degree of target characterization, we are required to modify the target fuzzy set. An analyst must decide upon a threshold above which an element is considered to be completely characteristic of the target site. In fuzzy set theory, this is called an a-cut: a technique to apply a threshold to the ? values such that if the original value exceeds it, reassign the value to 1, otherwise set it to 0. In this way, the analyst's subjectivity can be encoded in the response fuzzy set, and Equations 1 and 2 remain valid. 3. (U) Analysis of an Individual Element (U) Equations 1 and 2 can be simplified to provide an accuracy and reliability on an individual, element basis instead for a complete response. For example, let N be the number of sessions against different targets that exist in a current archive for a specified viewer. Let e be an element in question (e.g., airport). Then the empirical probability that element a is in the target, given that the viewer said it was, is given by R(c)-Nr, (3) where Nc is the number of times that the individual was correct, and Nr is the number of times that element a was mentioned in the response. R(e) is also the reliability of the viewer for that specified element. (U) To compute what chance guessing would be, we must know the occurrence rate of element e in the N sessions. Let No be the actual number of times element a was contained in the N targets. Then the chance-guessing empirical probability is given by Ro(c)=No. Ra(e) can also be considered as the guessing reliability (i.e., the reliability that would be observed if the viewer guessed a during every session). The more R(e) > Ro(e), the more reliable the individual is for the specified element. (U) The empirical probability that the viewer said element e, given that it was in the target, is given by A(e) is also the accuracy of the viewer for that specified element. (U) As a numerical example, suppose a single viewer participated in N = 25 sessions. Let e = "airport." Further suppose that No = 5 of the targets actually contained an airport. Approved For Release 2000/08/Oh4`f~Df~ F0~7'~R002200570001-5 Approved For Release 2000/08/08 : CI9EFEQQA-O T0789R002200570001-5 (U) Then, R0(airport) = 0.20 is the chance probability (i.e., guessing airport during every session would only by 20 percent reliable). Assume that the viewer mentioned airport Nr = 6 times and was correct Nc = 4 times. Then this viewer's reliability for airports is computed as R(airport) = 0.67 > Ro(airport) = 0.20. The viewer's accuracy for airports is computed as A(airport) = Nc/N0 = 0.80. Thus in this example, we can conclude that this viewer is reasonably accomplished at remote viewing an airport. B. (U) Prototype Analysis System (S/NF) We assume that an intelligence analyst has constructed a mission-dependent universal set of elements. We further assume that there are a number of competing interpretations of the target site in question. 1. (U) Target Templates (S/NF) The first step in our prototype analysis system is to define templates (i.e., general descriptions of classes of target types) of all competing target interpretations from the universal set of elements. For example, a class of target types could be a generic biological warfare (BW) facility. Exactly what the templates should represent is entirely dependent upon what kind of information is sought. Both the underlying universal set of elements and the templates must be constructed to be rich enough to allow for the encoding of all the information of intelligence interest. That is, if neither the set of elements nor the templates can meaningfully represent information about, say BW development sites, then it will be unreasonable to consider asking, "Does development of BW agents take place at the site?" Furthermore, a certain amount of atomization is necessary because such division into small units provides the potential for interactions within the universal set of elements. If the profile of a BW facility consists of a single element, the template would be useless unless the response directly stated that particular element; rather, the profile should be constructed from groups of elemental features (e.g., biological, offensive, weapon, decontamination). (S/NF) There are two different ways to generate target templates. The most straightforward technique is also likely to be the most unreliable, because it relies on the analyst's judgment of a single target type. With this method, the analyst, who is familiar with the intelligence problem at hand, simply generates membership values for elements from the universal set of elements based upon his or her general knowledge. Given the time and resources, the best way to generate template membership values is to encode known targets that are closely related (e.g, a number of known BW sites). Each template ? is the average value across targets, and thus is more reliable. If it is known that some targets are more S Approved For Release 2000/08/08: CISWRET00789R002200570001-5 Approved For Release 2000/08/08 : CIM0789R002200570001-5 (S/NF) "characteristic" of the target type than others, then a weighted average should be computed. In symbols, (4) where the sums are over the available targets that constitute the template, wk are the target weights, and the ?1,k are the assigned membership values for target k. (S/NF) A critical feature of an analysis system for intelligence RV data is that along with the current RV data to be evaluated, the individual viewer's past performance on an element-by-element basis must also be included. For example, if a viewer has been relatively unsuccessful at recognizing BW facilities, then a BW reference in the current data should not contribute much in the overall analysis. (S/NF) As ground truth becomes available for each session, a performance database should be updated for each viewer to reflect the new information This database should be a fuzzy set whose membership values for each element are the reliabilities computed from Equation 3. 3. (U) Optimized Probability List (S/NF) The goal of any intelligence RV analysis system is to provide an a priori prioritized and weighted list of target possibilities that results from a single remote viewing that is sensitive to the performance history of the viewer. Assuming that a template exists for each of the possible intelligence interpretations, an analyst should adhere to the following protocol: (1) Analyze the RV data by assigning a membership value (?) for each element in the universal set of elements. Each ? represents the degree to which the analyst is convinced that the particular element is included in the response. For example, suppose that the viewer said, "I perceive a BW facility." Then ?(BW facility) = 1. Alternatively, suppose the viewer said, "I perceive glassware and smell organic chemicals." In this case, ?(BW facility) might be assigned 0.6. (2) Construct a crisp set, Rc, as an a-cut of the original response set. By adopting a threshold of 0.5, for example, then the resulting crisp set contains only those elements that the analyst deems most likely as being present in the response. (3) Construct an effective response set, Re, as Re = Rc U Ra, where Ra is the reliability set drawn from the archival database. For example, suppose the original Approved For Release 2000/08/08 : CIkkL7P9-6-00789R002200570001-5 Approved For Release 2000/08/08 : CI$EP0789R002200570001-5 (S/NF) (5) assignment from the raw RV data was ?(BW facility) = 0.6. Then after the a-cut with a threshold set at 0.5, ?(BW facility) = 1.0. Suppose, however, that the viewer has been performing well on BW facilities and the archival database shows that Ra(BW facility) = 0.8. Thus, Re(BW facility) = 0.8. (4) Using this effective response set, compute an accuracy and reliability in accordance with Equations 1 and 2. Then compute a figure-of-merit, MM, for the jth competing interpretations as Mj=aj X rj . Of course, the accuracy and reliability use the effective response set from step 3 above. Order the Ms from largest to smallest value. Since the figures-of-merit range in value from 0 to 1, they can be interpreted as relative probability values for each of the alternative target possibilities. By following such a protocol, an analyst can produce a list of target alternatives that is sensitive to the current remote viewing yet takes into consideration to the individual viewer's archival record. C. (U) Partial Application of Analysis System to Existing Target Pool (U) We have used an existing target pool (developed under a separate program) as a test bed for the analysis system described above. 1. (U) Criteria for Inclusion in the Target Pool (S/NF) Targets in this pool have the following characteristics: ? Each target is within an hour and a half automobile drive of SRI International. ? Each target simulates an operational site of intelligence interest. ? Each target fits generally within one of five functional categories: Production, Recreation, Scientific, Storage, and Transportation. ? Each target meets a consensus agreement of experienced RV monitors and analysts about inclusion in the pool. (U) The pool consists of 65 targets. Initially, they were divided into 13 groups of five targets each, where each group contained one target from each of five functional categories. By carefully organizing the targets in this way, the maximum possible functional difference of the targets within each group was ensured. Table 1 shows a numerical listing of these targets. Approved For Release 2000/08/08 : AW- CP9'6T00789R002200570001-5 Approved For Release 2000/08/08 : C 0789R002200570001-5 (U) Numerical Listing of Targets 1. Transformer Station 23. Space Capsule 45. Pump Station 2. Ballpark 24. Coastal Battery 46. Ice Plant 3. Satellite Dish 25. Bay Area Rapid Transit 47. Caves/Cliffs 4. Weapons Storage 26. Salt Refinery 48. Bevatron 5. Naval Fleet 27. Candlestick Park 49. Barn 6. Gravel Quarry 28. Solar Observatory 50. Golden Gate Bridge 7. Swimming Pool 29. Food Terminal 51. Modern Windmills 8. Observatory 30. Pedestrian Overpass 52. Baylands Nature Preserve 9. Prison 31. Electrical Plant 53. Gas Plant 10. Shipping and Receiving 32. White Plaza 54. Auto Wreckers 11. Greenhouse 33. Space Shuttle 55. Fishing Fleet 12. Picnic Area 34. Coastal Battery 56. Radio Towers 13. Satellite Dishes 35. Train Terminal 57. Vineyard 14. Paint Warehouse 36. Sawmill 58. Pharmaceutical Laboratory 15. Naval Air Station 37. Pond 59. Toxic Waste Storage 16. Sugar Refinery 38. Wind Tunnel 60. Airport 17. Playground 39. Grain Terminal 61. Car Wash 18. Aquarium 40. Submarine 62. Old Windmill 19. Drum Yard 41. Cogeneration Plant 63. Nuclear Accelerator 20. Aircraft 42. Park 64. Reservoir 21. Sewage Treatment Plant 43. Linear Accelerator 65. Train Station 22. Hoover Tower 44. Dump 2. (U) Fuzzy Set Element List (S/NF) In FY 1989, we developed a prototype analysis system for analyzing targets and responses in operational remote viewings. A list of elements, based on target function (i.e., the mission specification), is arranged in levels from relatively abstract (information poor) to the relatively complex (information rich). Having levels of elements is advantageous in that each can be weighted separately in the analysis. (U) This universal set of elements (included as Appendix A) represents primary elements in the existing target pool of 65 targets. The set was derived exclusively from this known target pool. In an actual RV session, however, a viewer does not have access to the element list, and thus is not constrained to respond within its confines. An accurate RV analysis must include any additional data that may be provided in the response; therefore, additional space has been provided on the analysis sheets (see Appendix A) to include elements that are part of the response but not initially included as part of the universal set. Approved For Release 2000/08/08 : C IMMMI 9R002200570001-5 Approved For Release 2000/08/08 : 789R002200570001-5 (S/NF) The target-dependent elements emphasize the site's function, and use terms that are potentially universal across targets. We identified six element levels ranging from relatively information rich to relatively information poor: affiliation, function, attributes, modifiers, objects, and general/abstract. Because operational RV presupposes a certain level of ability on the part of the viewer, there are relatively few general/abstract elements included in our prototype analysis system. A description of some of the elements shown in Appendix A and a guide to their use are presented in Appendix B. 3. (U) Target Similarities (U) In order to generate a demonstration target-type template using Equation 4, we first organized the 65 targets into clusters of similar types. (U) We begin by defining the similarity between target j and target k (Sj,k) to be a normalized fuzzy set intersection between the two target sets; N (w1(Tj n Tk)il 2 S1,k N N N WiTj.i) X (N WjTk.i t=i li-1 l J (5) By inspection, we see that Sj,k is also the figure-of-merit between target j and target k. (U) For N targets there are N(N-1)/2 unique values (2080 for N=65) of Sj,k. The value j and k that correspond to the largest value of Si,k represent the two targets that are most functionally similar. Suppose another target m is chosen and Sm,j and Sm,k are computed. If both of these values are larger than Sm,p (for all p not equal to j or k) then target m is assessed to be most similar to the pair j,k. The process of grouping targets based on these similarities is called cluster analysis. (U) Figure 1 shows the six clusters found from the cluster analysis of the 65 targets. * The numbers shown refer to the targets listed in Table 1, and the clusters are in close agreement with the original five categories used to select the targets. The point, however, is that a numerical algorithm is capable of dividing a set of targets into functional categories. (U) In order to make the graphic output more meaningful, we used 1 - Sj,k in the analysis. Approved For Release 2000/08/08 : C - -00789R002200570001-5 Approved For Release 2000/08/08 : 0789R002200570001-5 Recreation Cluster 1 42 27 - 16 7 - -------------- 55 Cluster 2 Transportation 37 --------------------------------- 40 - Weapons k J4 4 1 Cluster 4 Technology -------49~ 45 62 6 0.4 1 - Si,k Figure 1. (S/NF) Cluster Diagram for Simulated Operational Targets LtS 63 -~ 58 44 Cluster 5 59 I- Storage 39 14 Cluster 6 Production/Distribution 3~ -o 39 H UNCLASSIFIED (U) We used the technology cluster (i.e., number 4 in Figure 1) to apply Equation 4 to construct a technology target template. Table 2 shows the targets in this cluster, where the horizontal lines indicate the subclustering within the technology group shown in Figure 1. Approved For Release 2000/08/08 :-00789R002200570001-5 Approved For Release 2000/08/08VFJeEAIgr R002200570001-5 (U) Technology Cluster Target Name 56. Radio Towers 1. Transformer Station 51. Modern Windmills 31. Electrical Plant 41. Cogeneration Plant 3. Satellite Dish 13. Satellite Dishes 8. Observatory 28. Solar Observatory 58. Pharmaceutical Laboratory 63. Nuclear Accelerator 43. Linear Accelerator 48. Bevatron (U) Table 3 shows those elements that met or exceeded average membership values of 0.4 using Equation 4. (U) Principal Elements Contained in the Technology Template Levels Number Name Affiliation 1 Commercial/Private Function 14 Research/Experimentation Attribute 24 Energy Modifier 47 Electricity/Radio Objects 88 High Technology Electronics 99 Restricted Access 120 Wires/Cables Abstract 122 Activity-Passive 130 Ambiance-Indoor 131 Ambiance-Manmade 137 Ambiance-Outdoor 149 Size-Medium Approved For Release 2000/08/0Y'L@-:(',R;a9R002200570001-5 Approved For Release 2000/08/08 : CIASPt' 0789R002200570001-5 (U) As a self-consistency check, we included the technology template in the total target pool and recalculated the clusters. As expected, the technology template was included within the subgroup of targets 3 and 13, and well within the technology cluster as a whole. (S/NF) The goal of this effort was to develop an analysis system that would prove effective in providing a priori assessments of intelligence remote viewing tasks. If the proper mission-dependent universal set of elements can be identified, then, using a viewer-dependent reliability archive, data from a single remote viewing can be used to prioritize a set of alternative target templates so as to chose the most likely one for the mission. Approved For Release 2000/08/08 : C`RC7100789R002200570001-5 Approved For Release 2000/08/08 : 0789R002200570001-5 REFERENCES (U) 1. Puthoff, H.E., and Targ, R., "A Perceptual Channel for Information Transfer Over Kilometer Distances: Historical Perspective and Recent Research," Proceedings of the IEEE, Vol. 64, No. 3, March 1976, UNCLASSIFIED. 2. Targ, R., Puthoff, H.E., and May, E.C., 1977 Proceedings of the International Conference of Cybernetics and Society, pp. 519-529, 1977, UNCLASSIFIED. 3. May, E.C., "A Remote Viewing Evaluation Protocol (U)," Final Report (revised), SRI Project 4028, SRI International, Menlo Park, California, July 1983, SECRET. 4. May, E.C., Humphrey, B.S., and Mathews, C., "A Figure of Merit Analysis for Free-Response Material," Proceedings of the 28th Annual Convention of the Parapsychological Association, pp. 343-354, Tufts University, Medford, Massachusetts, August 1985, UNCLASSIFIED. 5. Humphrey, B.S., May, E.C., Trask, V.V., and Thomson, M. J., "Remote Viewing Evaluation Techniques (U)," Final Report, SRI Project 1291, SRI International, Menlo Park, California, December 1986, SECRET. 6. Humphrey, B.S., May, E.C., Utts, J.M., Frivold, T.J., Luke, W.L., and Trask, V.V., "Fuzzy Set Applications in Remote Viewing Analysis," Final Report-Objective A, Task 3, SRI Project 1291, SRI International, Menlo Park, California, December 1987, UNCLASSIFIED. 7. May, E.C., Humphrey, B.S., Frivold, T J., and Utts, J M., "Applications of Fuzzy Sets to Remote Viewing Analysis (U)," Final Report-Objective F, Task 1, SRI Project 1291, SRI International, Menlo Park, California, December 1988, SECRET. Approved For Release 2000/08/08 : 89R002200570001-5 Approved For Release 2000/08/084N(A$IIW%RO02200570001-5 Appendix A UNIVERSAL SET OF ELEMENTS FOR ANALYSIS OF FUNCTION (U) (This Appendix is completely UNCLASSIFIED) UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 .r. E c ` ai 0 C c E m W ?C L O E co ' Om < cm c 0 a 0 to co c) v m > m R 3 U) M m ro 3 ( M 0 m 3 N M L- a) OL CL .0 0 CO w i` m U > c L1. O LL z 0 Q E m } a N N & 0) V & 0 6 R CC r cc C cc C, to (0 0 r- OD C) 0 D z 0 LLI E 1p U U) C s d (0 b w n' 0 w 00 . Go O 4 0) m N U - Cl C 0 C-4 N w c) N } w N cr- C G 0 U. U) Approved For Release 2000/08/08 : CIA-RDP96-00789 - Or m U m c~'n n a m U p ? U o i m 0 U p CL 0 CO (f) 0) F- E c O Q m m to o (0 co 0 ^ Cl in oooaoooaoooo U V U U. m 0 m c CO (A C l({ 'V C U U C e0 'C U (! (7 2 2 z' a `a m for to 1) IL) I) 10 U) ~ to N N (0 (0 j j j I I j I I I I I I V ~ o ao l' (0 N $ 0 V = 0 Rt U U C X b V O 4 d m m? U U w Ui M 00) ? v v v co v Approved For Release 2000/08/08: CIA-RDP96-00 - L m v v 00 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 E c 0 A 0 ID ? 0. 1 cc U > El El C] C] El El El El El El EIEI El aooooo0 Approved For Release 2000/08/08 : CIA-RDP96-00789RO02200570001-5 ElUooooo UEIEIEffIEIIJ o V o N dl } m N co I cm m 0 cc (%0 M= CD 0 Cl 0 0 0 0 co 0) p) Q) T T T T T T 00000000 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 5 cc 0 LL /\z V P 0 2 U) 9 CL w 0 m `0o ,oo O O T to Le) T T o 0 0 7 E s 22 Q U 2 0 :9 m 0 CI) COO D) co c) eF qt 1w T T T T T T T T T 000000000000 0 0 a 0 :0. 0 Si ? o 2 j E a Q Q Q ? Q E?? I - N m 1w l) CO ^ cD 0) O T N N N N N N N N N N co C) c?) aooaooaooooa Annroved For Release 2000/08/08 CIA-RDP96-007898002200570001-5 r CD C) O r N M u1 t0 ti c0 C) n n h O CO CO CO CO CO CO co CO r r r r r r T r T r T r E1UJEllh1J11UE1L1DDU tl') (0 N. CO C) 0 - CO) N ef' U) CO CO CO CO CO CO N. P. N. N. P. N. ti T T r r - r r T T r T T T ooooaooooaoaa 0 N CO U) co N U) U) N T T T T T U) ^ co C) r O N Cl) U) U) U) CO t0 t0 t0 T r r r r r T or a ease Approved For Release 2000/08/OUABSMD39R002200570001-5 Appendix B ANALYSTS' GUIDE TO THE UNIVERSAL SET OF ELEMENTS FOR FUNCTION (U) (This Appendix is completely UNCLASSIFIED) Approved For Release 2000/08/0> % ATP?t c , 9R002200570001-5 Approved For Release 2000/08/OiJ .P F8WdEQ9R002200570001-5 AN ANALYST'S GUIDE TO THE UNIVERSAL SET OF ELEMENTS (U) (U) This appendix is intended to assist an analyst in using the universal set of elements shown in Appendix A. We developed six levels of elements ranging from relatively abstract (information poor) to the relatively complex (information rich). B. (U) Element Levels and Their Use (U) The task of the analyst is to assign a membership value between 0 and 1 to each individual element. For targets, a numerical value will be assigned on the basis of the presence or absence of each element in terms of functional importance. For responses, the numerical value will be assigned on the basis of the degree to which the analyst is convinced that the element is contained in the response. (U) All subsequent commentary is referenced by the element numbers in Appendix A. Although each level may contain a number of elements, only those individual elements that may need explanation are listed below. 1. (U) Element Level-Affiliation (U) "Affiliation" represents an advanced level of remote viewing functioning. Although we infrequently observe this advanced functioning, the data are valuable, and, therefore, are included. Elements in this level can be assigned membership values by asking the question, "Who owns the target?" There are only three "affiliation" elements: (1) Commercial/Private. (2) Government: Federal, state, or local governmental ownership (e.g., municipal utilities), but excluding military. (3) Military: military ownership as separate from the above governmental ownership (e.g., a Navy submarine). UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Approved For Release 2000/08/1WNC1~*69]iU?89R002200570001-5 2. (U) Element Level-Function (U) "Function" also represents an advanced level of remote viewing functioning, and it may represent the most important information with regard to overall function. Elements are assigned membership values by asking the question, "What is(are) the primary function(s) of the target?" There are 14 "function" elements, and a few require further explanation: (6) Distribution: the primary function is to receive &ad to transmit something (e.g., an electrical transformer station). (8) Extraction: as in the extraction of minerals from the ground. (11) Reception: the primary function is Qfl]y, to receive (e.g., a satellite tracking station). (13) Refining: the primary function is to refine a raw material into an intermediate or finished product (e.g., a saw mill). (16) Transmission: the primary function is QnJ to transmit (e.g., a radio tower). 3. (U) Element Level-Attributes (U) "Attributes" can be thought of as clarification for the "function" level. Elements are assigned membership values by asking a question similar to, "If the function of the target is production, then what is being produced?" There are 20 "attribute" elements, and the following require further explanation: (18) Animals: animals Q111y. (20) Biology: the study of living things in general. (21) Chemistry: also includes chemicals. (23) Ecology: symbiotic systems in nature, as in ecological zones (e.g., the Bay Lands Nature Preserve). (24) Energy: energy in a broad sense that also includes radio waves. (29) Nature/Natural: general natural objects (e.g., plants ;1IId, animals). (32) Plants: plants 2111X,. (33) Space exploration: general, includes all experimentation done in space. Elements 18 and 32 are given a membership value if the target/response is specifically oriented to one item. Otherwise element 29 should be assigned a value. 4. (U) Element Level-Modifiers (U) "Modifiers" can be thought of as a clarification of the "attributes" level. Elements are assigned membership values by asking a question similar to, "If the function of the target is production, and vehicles are being produced, then what kind of vehicles are they?" There are 36 "modifiers" elements, and only element 66 requires further explanation: UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Approved For Release 2000/08/44 FPt-RVP99-XQ91R002200570001-5 (66) Symbiotic: symbiotic relationships not subsumed under natural or ecology (e.g., a cogeneration plant). 5. (U) Element Level-Objects (U) "Objects" contains specific elements not necessarily related to function. Elements are assigned membership values on the basis of the presence or absence of each object in terms of functional importance. There are 47 "objects" elements, and the following require further explanation: (77) Catwalk: elevated walkway. (79) Coastline: used only as coastline of an ocean. (88) High-Technology Electronics: silicon-based technology. (95) Port/Harbor: port should be marked as in port of departure (e.g., airport, train station, seaport). (116) Water-Bounded: only completely bounded bodies of water (e.g., pool or pond). (117) Water-Canal: manmade. (118) Water-Large Expanse: the San Francisco Bay should be marked as a large expanse. (119) Water-River: also includes stream. 6. (U) Element Level-General/Abstract Items (U) This level contains the most abstract elements. There are 31 elements, and the following require further explanation: (121) Activity-Active: predominant visually active (e.g., an accelerator is very active electromagnetically, but would be considered passive, because there is little visual activity); potential activity is considered as passive. (122) Activity-Passive: predominant visually passive (e.g., a ballpark is passive most of the time). (123) Activity-Flowing (Water, Air, etc.): can be natural (e.g. creek) or manmade. (128) Ambience-Dangerous: perceived and/or physically dangerous. (140) Colorful: to be used only if especially characteristic. (141) Modern: to be used only if especially characteristic. (142) Odd/Surprising: to be used only if especially characteristic. (143) Old: to be used only if especially characteristic. (144) Personnel-Few: 1 to 10 employees mostly full-time. (145) Personnel-Many: 10 to 1000 employees mostly full-time. (146) Personnel-None: no full-time employees, but occasional human attention is allowed. (148) Size-Large (University Campus): represents a "campus" size area. (149) Size-Medium (Building): size of typical single buildings. (150) Size-Small (Human): typically, the size of a human (i.e., 6 feet) (151) Dull: to be used only if especially characteristic of the color. UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5 Next 1 Page(s) In Document Exempt Approved For Release 2000/08/08 : CIA-RDP96-00789R002200570001-5