=Paper=
{{Paper
|id=Vol-2558/short7
|storemode=property
|title=Anticipatory Intelligence Analysis with Cogent: Current Status and Future Directions
|pdfUrl=https://ceur-ws.org/Vol-2558/short7.pdf
|volume=Vol-2558
|authors=Gheorghe Tecuci,Dorin Marcu,Mihai Boicu,Chirag Uttamsingh
|dblpUrl=https://dblp.org/rec/conf/aaaifs/TecuciMBU19
}}
==Anticipatory Intelligence Analysis with Cogent: Current Status and Future Directions==
https://ceur-ws.org/Vol-2558/short7.pdf
Anticipatory Intelligence Analysis with Cogent:
Current Status and Future Directions
Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, Chirag Uttamsingh
Learning Agents Center, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
{ tecuci; dmarcu; mboicu; cuttamsi }@gmu.edu
Abstract IC and DOD organizations. While praising their theoretical
Anticipatory intelligence analysis is the complex task of framework and evidentiary knowledge, the analysts desired
drawing defensible and persuasive conclusions about future a simplified interface and interaction, which has led to the
events or states based on current information. This paper development of Cogent (Tecuci et al., 2015; 2018).
presents a systematic approach to anticipatory intelligence
In this paper we discuss how Cogent supports an analyst
analysis with Cogent, a software cognitive assistant that
enables a synergistic integration of the analyst’s imagination in performing anticipatory analysis. We start with a brief
and expertise with the computer’s knowledge and critical account of the complexity of this task. Then we introduce a
reasoning. It shows how current, as well as envisioned systematic approach to anticipatory analysis which is
capabilities of Cogent, help alleviate many of the grounded in the science of evidence (Schum, 2009).
complexities of this task.
Following that we present two examples of anticipatory
analysis with Cogent, and discuss how it assists the analysts
Introduction in coping with their complexity. Finally we discuss future
developments of Cogent.
Anticipatory intelligence analysis is the complex task of
drawing defensible and persuasive conclusions about future
events or states based on current information of all kinds that Complexity of Anticipatory Analysis
come from a variety of different sources. It addresses new
and emerging trends, changing conditions, and Evidence-based Reasoning
underappreciated developments (ODNI, 2019).
The evidence upon which the anticipation of possible future
The prevailing approach to anticipatory intelligence
states or events eventually rests has five major
analysis and intelligence analysis in general is the holistic
characteristics that make these anticipations necessarily
approach where the analysts, after reviewing large amounts
probabilistic in nature (Tecuci et al., 2016a, pp.159-167).
of information and performing the reasoning in their heads,
The evidence is always incomplete no matter how much we
reach a conclusion (Marrin, 2011). A complementary
have. It is commonly inconclusive in the sense that it is
approach uses structured analytic techniques, such as those
consistent with more than one future state or event. Further,
described by Heuer and Pherson (2011) that guide the
the evidence is frequently ambiguous and, in most
hypothesis generation and testing process performed by the
situations, dissonant, some of it favoring one future state or
analysts. Some of these methods and more advanced ones
event while other evidence favoring others. Finally, the
based on Bayesian probabilistic inference networks are
evidence comes from sources having different levels of
implemented in analytical tools, such as Netica (2019).
credibility. Arguments to test the hypothesized future states
We have developed a sequence of cognitive assistants
or events are necessary in order to establish and defend the
based on Wigmorean networks (Wigmore, 1937). The first
three major credentials of evidence: its relevance, its
of these systems, Disciple-LTA, integrates capabilities for
credibility, and its inferential force or weight. These
analytic assistance, learning, and tutoring (Tecuci et al.,
arguments rest upon both imaginative and critical reasoning.
2008). TIACRITIS and its subsequent version, Disciple-CD
(Tecuci et al, 2016a), were developed primarily for teaching
intelligence analysis and were experimentally used in many
Assessing the Credibility of Evidence
______________________________________________________________________ Evidence credibility assessments form the very foundation
Copyright © 2020 for this paper by its authors. Use permitted under for all arguments we make from evidence to possible future
Creative Commons License Attribution 4.0 International (CC BY 4.0). states or events. The different types of tangible, testimonial,
and mixed evidence have many credibility indicators and all unimportant in light of what we learn today. The
would need to be assessed based on ancillary evidence in consequence is that the complex process of discovery or
order to have high confidence in the accuracy of our investigation in anticipatory analysis is a ceaseless activity.
anticipations (Tecuci et al., 2016a, pp. 118-133).
Limits of Individual Probability Views Anticipatory Intelligence Analysis
While anticipatory intelligence analysis is probabilistic in in the Framework of the Scientific Method
nature, none of the non-enumerative probability views Following the framework of the scientific method, we
known to us (Subjective Bayesian, Belief Functions, model anticipatory analysis as ceaseless discovery of
Baconian, and Fuzzy) can optimally cope with all the five evidence, anticipations, and arguments, in a non-stationary
characteristics of evidence mentioned above (Schum, world, involving collaborative computational processes of
2001a; Tecuci et al., 2016a, pp.173-208). For example, the evidence in search of anticipations, anticipations in search
conventional Subjective Bayesian view cannot cope well of evidence, and evidentiary testing of anticipations, as
with ambiguities or imprecision in evidence. On the other represented in Figure 1 (Tecuci et al., 2016a).
hand, the Fuzzy view can naturally cope with such First, through abductive (imaginative) reasoning that
imprecisions. But neither the Bayesian view nor the Fuzzy shows that something is possibly true (Schum, 2001b), we
view can account for the incompleteness of the coverage of generate alternative future events or states that may explain
evidence. The only view that can account for this is the an intelligence alert. If, instead of an intelligence alert, the
Baconian view where the probability of a future state or starting point is an intelligence question, the alternative
event depends on how complete the evidence is, or how anticipations are the possible answers to this question. Next,
many questions recognized as being relevant remain through deductive reasoning that shows that something is
unanswered by the evidence we have. This is in contrast necessarily true, we use the hypothesized future
with the Bayesian, Belief Functions, and Fuzzy views that states/events to generate new lines of inquiry and discover
all rest on how strong is the evidence we have about the new evidence. After that, through inductive reasoning that
considered future state or event. While on the Bayesian shows that something is probably true (Schum, 2001a), we
probability scale “0” means disproof, on the Baconian scale, test each anticipation by developing an argumentation that
“0” simply means lack of proof. A future state/event shows how the discovered evidence favors or disfavors it.
currently having “0” Baconian probability can be revised As shown at the bottom of Figure 1, these are
upward in value as soon as we have some evidence for it. collaborative processes that support each other in recursive
calls. For example, the discovery of new evidence may lead
Time Constraints to the modification of the existing hypotheses of future
A major objective of anticipatory intelligence analysis is to events/states or the generation of new ones that, in turn, lead
help insure that the policies and decisions reached by the to the search and discovery of new evidence. Also,
governmental and military leaders, at all levels, are well inconclusive testing of the considered anticipations requires
informed. In many cases analyses are required to answer the discovery of additional evidence. The next sections
questions that are of immediate interest and that do not allow illustrates this process using Cogent in two analyses.
analysts time for extensive research and deliberation on Alternative Future Events or States Probabilities of Anticipations
available evidence regarding the questions being asked.
Non-Stationary World
As outlined above, anticipatory intelligence analysis has
many inherent difficulties, but none seem more difficult
than the fact that analysts must assess future states or events Alert or Question New Evidence
in a non-stationary world that keeps changing as analysts are Evidence in search Anticipations in Evidentiary testing
of anticipations search of evidence of anticipations
trying to understand it. As a result, we have continuing
Abduction Deduction Induction
streams of new information, some items of which are A or Q possibly H H necessarily E E probably H
relevant evidence regarding our anticipations. An
Figure 1: A framework for anticipatory analysis.
explanation for some pattern of past events analysts have
previously regarded as correct may now seem incorrect in
light of new evidence just discovered today. A future event Alert-Driven Anticipatory Analysis
regarded as highly likely today may be overtaken by events
we will learn about tomorrow. In fact, the very questions we The following example of anticipatory analysis with Cogent
asked yesterday may need to be revised or may even seem shows how evidence about a missing cesium-137 canister
leads to anticipating that a dirty bomb will be set off in the cesium-137 canister was stolen. Then it is possible that it
Washington, D.C., area (Tecuci et al., 1016a). Note that this was stolen by a terrorist organization, or by a competitor of
scenario and all the entities involved are fictitious. XYZ, or by an employee. Upper level hypotheses concern
Mavis, a counterterrorism analyst, reads in today’s possible future events, such as, the terrorist organization will
Washington Post that a canister containing cesium-137 is use the cesium-137 canister to build a dirty bomb, the dirty
missing from the warehouse of the XYZ Company in bomb will be set off in the Washington, D.C., area, or it will
Maryland (see E* at the bottom of Figure 2). The question be set off in the New York area.
is: What hypothesis would explain this observation? The analyst and Cogent need to assess each of these
Through abductive (imaginative) reasoning, Mavis infers competing hypotheses, and determine which of them are
that a dirty bomb will be set off in the Washington, D.C., likely. Starting from bottom-up, each hypothesis is put to
area (see H5 at the top of Figure 2). However, no matter how work to guide the collection of additional evidence:
imaginative or important this future event is, no one will Assuming that the cesium-137 canister is indeed missing,
take it seriously unless Mavis and her cognitive assistant, what other things should be observable? What are the
Cogent, are able to justify it. So they develop the chain of necessary conditions for an object to be missing from a
abductive inferences shown in the left side of Figure 2: warehouse? It was in the warehouse, it is no longer there,
We have evidence that the cesium-137 canister is missing. and no one has checked it out.
Therefore it is possible that it is indeed missing. It is possible As a result, the analyst contacts Ralph, the supervisor of
that it was stolen. It is possible that it was stolen by a the warehouse, who reports that the cesium-137 canister is
terrorist organization. It is possible that the terrorist registered as being in the warehouse, that no one at the XYZ
organization will use the cesium-137 canister to build a Company had checked it out, but it is not located anywhere
dirty bomb. It is possible that the dirty bomb will be set off in the hazardous materials locker. He also indicates that the
in the Washington, D.C., area. lock on the hazardous materials locker appears to have been
But these are not the only hypotheses that may explain the forced. Ralph’s testimony provides several items of relevant
evidence. Just because there is evidence that the cesium-137 evidence, and the question is: What is the probability that
canister is missing does not mean that it is indeed missing. the cesium-137 canister is missing, based on this evidence?
At issue here is the credibility of the source of this To answer this question, Mavis and Cogent build the
information. Thus an alternative hypothesis is that the Wigmorean probabilistic inference network from Figure 3
cesium-137 canister is not missing. But let us assume that it (Wigmore, 1937; Tecuci et al., 2018). It integrates logic and
is missing. Then it is possible that it was stolen, but it is also Baconian probabilities with Fuzzy qualifiers, and uses the
possible that it was misplaced, or maybe it was used in a min/max probability combination rules common to the
project at the XYZ Company. Now let us assume that the Baconian and Fuzzy views of probability (Cohen, 1977;
1989; Zadeh, 1983). That is, the probability of a conjunction
H5: A dirty H’5: A dirty of hypotheses is the minimum of their probabilities, and the
bomb will be bomb will probability of a disjunction of hypotheses is the maximum
Where? set off in the be set off in
the New
of their probabilities.
Washington,
D.C., area York area First Mavis and Cogent have to assess the probabilities of
the bottom hypotheses in Figure 3, based on the
Why? H4: build dirty H’4: build corresponding relevant evidence. Then these probabilities
bomb unshielded are composed to produce the probability of the top
radiation
sources hypothesis. The probability of a hypothesis, like the one
H3: stolen H”3: stolen
from the bottom left of Figure 3, shown also at the top of
H’3: stolen by
Who? by terrorist by employee Figure 4, is assessed based on the three credentials of
competitor
organization evidence: credibility, relevance, and inferential force
(Tecuci et al., 2016a, pp. 62-73). They are assessed by using
H”2: used the ordered symbolic probability scale from the upper right
How? H2: stolen H’2:misplaced in project
of Figure 3. As in the Baconian system, “lacking support”
for a hypothesis means that we currently have no basis to
H’1: not consider that the hypothesis might be true. However, we
What? H1: missing missing may later find evidence to infer that the hypothesis is, for
instance, “likely.” Figure 3 shows a favoring argument for
E*: Article on the top hypothesis and therefore it appears under the left
cesium-137
canister missing (green) square. Disfavoring arguments (if any) appear under
the right (pink) square.
Figure 2: Multi-step abduction and competing explanations. The credibility of the evidence item E2 in Figure 4 is
Figure 3: Wigmorean probabilistic inference network.
assessed as very likely because its source, Ralph, has access the cesium-137 canister was stolen with a U-Haul truck.
to the reported information and has a reputation for honesty. Having concluded that the cesium-137 canister is
The relevance of E2 is assessed as almost certain because missing, Mavis and Cogent have now to establish whether it
the records of the XYZ Company are almost certainly was stolen with a truck, it was misplaced, or it was used in
correct. Consistent with both the Baconian and the Fuzzy a project at the XYZ Company. Each of these hypotheses is
min/max probability combination rules, the inferential force put to work to guide the collection of evidence for assessing
of E2 on the hypothesis H is determined as the minimum it: If the cesium-137 canister was stolen with a truck, what
between the credibility of E2 (very likely) and the relevance other things should be observable?
of E2 (almost certain). Thus, the inferential force of E2 on Based on the current evidence, Mavis imagines the
H is very likely. Obviously, an irrelevant item of evidence following scenario on how the cesium-137 canister might
will have no inferential force, and will not convince us that have been stolen: The truck entered the company, the
the hypothesis is true. An item of evidence that is not canister was stolen from the locker, the canister was loaded
credible will have no inferential force either. Only an item into the truck, and the truck left with the canister.
of evidence that is both relevant and credible supports the Such scenarios have enormous heuristic value in
truthfulness of a hypothesis. advancing the investigation because they consist of mixtures
Because in the argumentation from Figure 4 there is only of what is taken to be factual and what is conjectural.
one item of favoring evidence, E2, its inferential force on Conjecture is necessary in order to fill in natural gaps left by
the hypothesis is also the probability of the hypothesis. In the absence of existing evidence. Each such conjecture,
general, however, the probability of the hypothesis would be however, opens up new avenues of investigation, and the
the result of the balance of probabilities between the
combined inferential force of the favoring evidence items very likely Probability of H
and the combined inferential force of the disfavoring items. H: The canister was in the warehouse
As shown at the top of Figure 3, it is very likely that the very likely Inferential force: Probability of H based
cesium-137 canister is missing, this being the minimum only on E2
between the probabilities of the three sub-hypotheses and almost certain Relevance: Probability of H2 assuming
the relevance of their conjunctive argument. that E2 is true
Some of the newly discovered evidence may trigger new
very likely Credibility: Probability that E2 is true
hypotheses or the refinement of the current hypotheses. For
E2 Canister registered: Ralph, who has
example, during her initial investigation, Mavis discovered
a reputation for honesty, reports that
a video segment from a security camera at the warehouse the cesium-137 canister is registered as
showing a person loading a container into a U-Haul truck, being in the warehouse.
leading her to refine the “stolen” hypothesis to indicate that
Figure 4: Evidence credentials.
discovery of additional evidence, if the scenario turns out to Jihad Bis Sayf will set off a dirty
bomb in the Washington, DC, area.
be true. This scenario, for instance, leads Mavis to check the
records of the security guard and they show that a panel
&
truck bearing Maryland license plate number MDC-578 was
in the XYZ parking area on the day before the discovery that
reasons desire capability
the cesium-137 canister was missing.
Fusing all the discovered evidence, Mavis and Cogent …
& &
conclude that it is very likely that the cesium-137 canister
was stolen with the MDC-578 truck. After further Jihad Bis Sayf is A dirty bomb set ability to ability to set off
investigation, they also conclude that the two competing a terrorist off in Washington, obtain a its dirty bomb in
organization D.C., the capital of dirty bomb the Washington,
hypotheses, “misplaced” and “used in a project,” lack opposed to the the United States, D.C., area.
evidentiary support. United States. would have a very
high impact. …
Continuing the investigation with the rental company …
owning the truck, it is discovered that Omar al-Massari … Jihad Bis Sayf will
Jihad Bis Sayf has a build a dirty bomb.
rented the MDC-578 truck giving his alias, Omer Riley, and presence in the
a false address, and that the truck is now contaminated Washington, D.C., area.
&
because cesium-137 is radioactive. These lead to the …
conclusion that Omar used the truck to steal the cesium-137
has has has expertise has secure
canister. It is further discovered that Omar al-Massari has
radioactive explosive to build a place to build
ties with terrorist organizations, and that he has given the material material dirty bomb a dirty bomb
cesium-137 canister to Saeed al-Nami, alias Kenny
… … …
Derwish, who is a member of the terrorist organization Jihad &
Bis Sayf. These discoveries lead to the specializations of the
hypotheses from Figure 2 as shown in Figure 5. cesium-137 canister The cesium-137
Figure 6 shows the analysis of the top anticipatory was stolen by Jihad canister contains
hypothesis “Jihad Bis Sayf will set off a dirty bomb in the Bis Sayf, a terrorist enough cesium-137 to
organization. build a dirty bomb.
Washington, DC, area.” It shows that Jihad Bis Sayf has
reasons, desire, and capability to set off the dirty bomb. It … …
has reasons because it is a terrorist organization opposed to Figure 6: Anticipatory analysis.
the United States, it has a presence in the Washington, DC,
area, and a dirty bomb in this area would have a very high Through such spiral hybrid reasoning, where abductions,
impact. Furthermore, Jihad Bis Sayf has both the ability to deductions, and inductions feed on each other in recursive
build the bomb and to set it off. In particular, it has the calls, Mavis and Cogent continuously generate and update
radioactive material from the stolen cesium-137 canister, intermediate alternative hypotheses, use these hypotheses to
and further investigation has determined that it has both the guide the collection of relevant evidence, and use the
necessary explosive material (Saeed al-Nami has stolen 2 evidence to test these hypotheses, until the probability of the
pounds of RDX explosive) and expertise to build the bomb top-level hypotheses are assessed, ultimately anticipating
(Saeed al-Nami has expertise in explosives and has received that Jihad Bis Sayf will likely set off a dirty bomb in the
training in the building of dirty bombs). Washington, D.C., area.
Note, however, that performing this analysis is not as
H5: Jihad Bis Sayf will set off a dirty simple as one may infer from this presentation. It is the
Where? bomb in the Washington, D.C., area methodology from Figure 1 and Cogent that guide the
Why? H4: Jihad Bis Sayf will build a dirty bomb analyst and simplify it. Many things can and will indeed go
wrong. But Cogent provides the means to deal with them.
H3: cesium-137 canister stolen by Based on evidence, you come up with some hypotheses, but
Who?
Jihad Bis Sayf, a terrorist organization then you cannot find more evidence to support any of them.
So you need to come up with other hypotheses, and you
H2: cesium-137 canister stolen by
How? should always consider alternative hypotheses. The
Omar al-Massari with MDC-578 truck
deduction-based decomposition approach guides you on
What? H1: cesium-137 canister missing how to look for evidence, but your knowledge and
imagination also play a crucial role. As illustrated here,
E*: Article on cesium-137 canister missing Mavis imagined a scenario where the cesium-137 canister
was stolen with a truck. But let us now assume that she did
Figure 5: Evidence-based hypothesis specialization. not find supporting evidence for this scenario. Therefore,
Mavis has to imagine other scenarios. Maybe the cesium (likely). They combine into an overall desire of likely.
canister was stolen by someone working at the XYZ Finally, almost certainly the United States have the required
Company, or maybe it was stolen by Ralph, the capabilities that consist of required homegrown scientific
administrator of the warehouse. The important thing is that knowledge, technical knowledge, economic resources, and
each such scenario opens up a new line of investigation. natural resources.
Question-Driven Anticipatory Analysis Current Cognitive Assistance
The previous section illustrated a situation where the Consider again the process described in Figure 1. With the
anticipatory analysis was driven by an intelligence alert. current version of Cogent, the analyst has to imagine the
This section illustrates a situation where the analysis is possible future states or events. However, Cogent helps with
driven by the intelligence question: Who will be the world developing argumentations that lay out the underlying
leader in wind power within the next decade? analytical framework for every anticipation, including the
The top level of the corresponding anticipatory analysis connection between the evidence and various intermediate
is shown in Figure 7. It is likely that the United States will hypotheses in the analysis, the evaluation of the credibility
be the world leader in wind power within the next decade of evidence and its strength in supporting a hypothesis, and
because almost certainly they have reasons, likely they have the role of assumptions in addressing missing information.
the desire, and almost certainly they have the necessary Anticipatory analysis may be affected by the analyst’s
capability. A reason is that significant production of wind biases. Cogent can detect several of them, such as the
power will reduce the current need of the United States to confirmation bias (building an argumentation and/or only
consume huge quantities of oil that represent a danger to the searching for evidence that confirms the analyst’s beliefs
environment. The desire of the United States, which is a while dismissing or ignoring evidence to the contrary), the
representative democracy, is determined by the desire of the satisficing bias (choosing the first hypothesis that appears
people (almost certain), the desire of the major political good enough, rather than carefully identifying all possible
parties (very likely), and the desire of the energy industries hypotheses and determining which one is the most
consistent with the evidence), and potential absence of
evidence bias (failure to consider the degree of
completeness of the available evidence). Many other biases
are avoided because explicit argumentations are developed
that employ an intuitive system of symbolic probabilities.
Additionally, Cogent facilitates the analysis of what-if
scenarios, where the analyst may make various assumptions
and Cogent automatically determines their influence on the
analytic conclusion. It also automatically updates the
computed probabilities based on new or revised evidence.
Once the analysis is finalized, Cogent generates a
structured report that the analyst then transforms into a more
understandable and persuasive report that includes
argumentation fragments and evidence, can be shared with
other analysts, subjected to critical analysis, and
correspondingly improved.
The hierarchical structure of the Wigmorean
argumentation enables the analyst and Cogent to perform
the analysis at different levels of abstraction. Moreover, the
analyst may drill down on selected sub-hypotheses as much
as allowed by the available time and evidence. Consider, for
example, the dirty bomb anticipated event from Figure 6. In
time-limiting situations the analyst may assume that Jihad
Bis Sayf has the reasons and desire to set off the dirty bomb,
and focus the investigation on its capability.
Note that an anticipated future state made at a given
moment in time may change afterwards because many of its
indicators are dynamic, such as those that determine the
Figure 7: Another example of anticipatory analysis.
desire of the United States in the wind power evidence
scenario (see Figure 7). Therefore evidence of
these indicators needs to be continuously credibility of image
monitored and updated. In the current version of
tangible testimonial missing authoritative
Cogent, this monitoring has to be done by the evidence evidence evidence record
analyst who also needs to insert the new authenticity reliability
evidence in the analysis. After that Cogent
accuracy
automatically updates the analysis. However, as
demonstrative real unequivocal equivocal
discussed in the next section, continuous tangible tangible testimonial testimonial
monitoring and updating of evidence can also be evidence evidence evidence evidence
automated. credibility of human source
Cogent has a knowledge base that includes an unequivocal testimonial testimonial probabilistically
ontology of evidence and rules for assessing its evidence based upon evidence based equivocal testimonial
direct observation on opinion evidence
credibility. Figure 8 shows a fragment of this
accuracy
ontology (Schum et al., 2009). For each type of competence unequivocal testimonial
veracity completely equivocal
evidence from this ontology, Cogent has a evidence obtained at testimonial evidence
second hand
procedure for assessing its credibility. For
example, as illustrated in the left hand side of Figure 8: Evidence ontology and credibility patterns.
Figure 8, the credibility of an item of
demonstrative tangible evidence (e.g., a map, a sound time, Cogent will learn domain analysis rules from the
recording, or a satellite image) depends on its authenticity, contributions of the Analyst, through the employment of the
its accuracy, and the reliability of the instrument the Disciple-EBR multistrategy learning approach, which
produced it. The credibility of a human source depends on integrates learning from examples, learning from
the source’s competence, veracity, and accuracy. These explanations, and learning by analogy and experimentation,
indicators depend on lower level indicators. For example, in a mixed-initiative interaction with the expert. Successive
the competence depends on the source’s access and versions of this learning approach are presented in (Tecuci
expertise, while the accuracy depends on the source’s 1998; Tecuci et al. 2002; 2005; 2008; 2016b). Cogent will
objectivity and observational sensitivity. also facilitate the development of analyses in collaboration
As discussed in the next section, future research will with other analysts, each contributing sub-arguments based
address the problem of learning general analysis rules from on their expertise, reviewing and commenting on each-
an expert analyst, which will speed-up and improve the other’s contributions, and sharing previously learned
development of new analyses. Note, for example, that the domain analysis rules, sources, and evidence.
analyses from the previous sections, although very different, (2) Automatic Analysis Updating, shown in the bottom
both make use of the following reasoning pattern: An actor part of Figure 9, where Cogent (through its collaborative
will perform a certain action or achieve a certain state if it autonomous agents) continuously monitors the Multi-INT
has reasons, desire, and capability. The rules learned from Environment and updates the performed analysis based on
the analysis in Figure 7 will enable Cogent to automatically the newly discovered evidence. These agents are copies of
generate analyses of future states such as: China will be the the corresponding modules of the Learning and Reasoning
world leader in solar energy within the next decade. Anticipations Learning to Probabilities
discover and
Mixed-Initiative monitor evidence
Mixed-Initiative and Automatic Learning and
Reasoning Assistant Learning to Learning to
generate test
Anticipatory Analysis Alert or Question hypotheses Collection Requests/Evidence hypotheses
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intelligence analysis, this paper illustrated how they can be 853-860. Edmonton, Alberta: AAAI Press.
alleviated through the use of the Cogent cognitive assistant Tecuci, G., Boicu, M., Boicu, C., Marcu, D., Stanescu, B.,
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anticipatory analyses based on imperfect information in a
Assistance. Journal of Intelligence Community Research and
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imagination and expertise with the computer’s knowledge Tecuci, G., Marcu, D., Boicu, M., Schum, D. A. 2015. Cogent:
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Acknowledgements Report FS-15-02, AAAI Press, Palo Alto, CA.
David Schum has significantly influenced this research that Tecuci, G., Schum, D. A., Marcu, D., Boicu, M. 2016a.
was supported in part by NSF under grant number 1611742, Intelligence Analysis as Discovery of Evidence, Hypotheses, and
Arguments: Connecting the Dots, Cambridge University Press.
by AFRL under contract number FA8750-17-C-0002, by
IARPA under contract number 2017-16112300009, and by Tecuci, G., Marcu, D., Boicu, M., Schum, D. A. 2016b. Knowledge
Engineering: Building Cognitive Assistants for Evidence-based
George Mason University. The views and conclusions Reasoning, Cambridge University Press.
contained herein are those of the authors and should not be
Tecuci, G., Kaiser, L., Marcu, D., Uttamsingh, C., Boicu, M. 2018.
interpreted as necessarily representing the official policies, Evidence-based Reasoning in Intelligence Analysis: Structured
either expressed or implied, of any organization of the U.S. Methodology and System, Computing in Science and Engineering,
Government. The U.S. Government is authorized to 20(6) 9-21, November/December.
reproduce and distribute reprints for governmental purposes Tecuci, G., Meckl, S., Marcu, D., Boicu, M. 2019. Instructable
notwithstanding any copyright annotation therein. Cognitive Agents for Autonomous Evidence-Based Reasoning.
Seventh Annual Conference on Advances in Cognitive Systems,
August 2-5, MIT, Cambridge, MA.
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