We describe enhancements underway to our probabilistic argument mapping framework called FUSION. Exploratory modeling in the domain of intelligence analysis has highlighted requirements for additional knowledge representation and reasoning capabilities, particularly regarding argument map nodes that are specified as propositional logic functions of other nodes. We also describe more flexible specifications for link strengths and node prior probabilities. We expect these enhancements to find general applicability across problem domains.
Downstream+!
maps. Exploratory modeling in the domain of intelligence
analysis—where argument mapping [
Forthcoming sections first present an early model motivating some of these capabilities, then describe our technical approach to each. Appendices describe key elements of our proposed intelligence domain-specific variant of FUSION called CRAFT. 2
Figure 1 is a screenshot of a FUSION model addressing the
CIA’s Iraq retaliation scenario [
O Opposite Node colors in Figure 1 capture whether Saddam’s disposition (or attitude) regarding a given statement is favorable (blue) or unfavorable (red). FUSION computes these dispositions from a single directive—to propagate the favorability of SaddamWins upstream (only), respecting link polarities. One node (gray, bottom left) is not touched by this propagation. Four nodes (purple, top right) have ambiguous status with respect to Saddam’s disposition. Belief bars augment spatial tick marks with colors chosen to reflect the degree of concern a given statement would pose given a node’s disposition. Lower belief (redder bar) poses less concern regarding a statement viewed unfavorably, more regarding one viewed favorably, higher belief (bluer bar) the reverse. Red/blue contrast thus draws attention to a statement that should be of concern to Saddam.
FUSION models can include argument map link types per Table 1.
In a FUSION model, every argument map statement is a Hypothesis. For the last two link types in Table 1, the downstream statement also is a Logic statement.
In developing the model in Figure 1, we identified the
following representation and reasoning shortcomings for
which we are now implementing responsive capabilities.
•! Beliefs computed for the scenario’s three outcomes do not
sum to 1.0. We can correct this by recasting
IraqRetaliatesWithTerror as an exclusive-or (summary or
constraint) Logic statement (see section 3) over the
scenario’s outcomes, rather than as an absolutely
supported Hypothesis, while also addressing the next
issue.
•! In current FUSION, a given node cannot be both a Logic
statement and an indicator. Our FUSION spec-to-BN
conversion software [
1 Per argument map convention, “downstream” is left, “upstream” right in the left-flowing argument map of Figure 1. •! TerrorAttacksFail (likewise TerrorAttacksSucceed) should be allowed to be true only when TerrorAttacks also is true. We are correcting this by adding the capability to assert Logic constraints, such as the one described for this model in section 3 to be used instead of the naïve OppositeOf link now connecting TerrorAttacksFail and TerrorAttacksSucceed.
We are working to make FUSION support any standard propositional logic expression using unary, binary, or higher arity operators2. When a Logic statement has a hard true finding3, we refer to it as a Logic constraint, otherwise as a summarizing Logic statement.
Figure 1’s model would be better if TerrorAttacksFail were allowed to be true only if TerrorAttacks also were true. We know that an attempted action can succeed or fail only if it occurs. By explicitly modeling (as Hypotheses) both these potential action results and adding a Logic constraint4, we can force zero probability for every excluded truth value combination, improving the model (in tradecraft terms, correcting a “logic flaw”). See Figure 2.
Figure 2’s graphics are from the COTS Netica BN Application. Because all nodes in the generated BN underlying a FUSION model are binary, we also could render the full BN using more perspicuous single belief bars.
FUSION implements a target belief spec either (depending on purpose) using a BN node like Figure 2’s ConstraintTBC or (equivalently) via a likelihood finding on the subject BN node. The FUSION GUI does not ordinarily expose an auxiliary node like ConstraintTBC to an analyst.
This example is for illustration. We can implement this particular BN pattern without target beliefs. We also could implement absolute-strength IndicatedBy links as simple implication Logic constraints. However, this would not naturally accommodate one of these links’ key properties— the ability to specify degree of belief in the link’s upstream node when the downstream node is true—relevant because we can infer nothing about P given P ! Q and knowing Q to be true. It also demands two target belief specs that tend to compete. We are working to identify more Logic constraint patterns that can be implemented without target beliefs and to generalize specification of belief degree for any underdetermined entries in a summarizing Logic statement’s CPT.
2 See, e.g., https://en.wikipedia.org/wiki/Truth_table.
3 A likelihood finding could be used to implement a soft constraint.
4 This constraint can be rendered (abbreviating statement names) as (or/(and/occur/(xor/succeed/fail))/(and/(not/Occurs)/(nor/ Succeeds/ Fails))) or more compactly via an if-then-else logic function (notated ite) as (ite/ Occurs/ (xor/ Succeeds/ Fails)/ (nor/ Succeeds/ Fails))—if an attack occurs, it either succeeds or fails, else it neither succeeds nor fails.
true 75.0 false 25.0
true 25.0 false 75.0
true 25.0 false 75.0
true )100 false )))0
true 50.0 false 50.0
true 25.0 false 75.0
true 25.0 false 75.0
true 50.0 false 50.0
true )100 false )))0
We can set a target belief for any FUSION node whose computed belief under a baseline situation (say, before the application of a set of evidence items or assumptions) deviates from a modeler’s expectations or requirements. We have formerly done this to address unacceptably small probabilities for far-upstream nodes—an issue we hope to see less of with new flexible link strengths (section 5). A target belief also can serve an exogenous variable that should be informed by real-world data statistics.
As with BN belief inference, there is no closed-form
solution for target belief satisfaction, which requires a
gradient descent optimization over individual BN inference
invocations, measuring in each step nodes’ differences
between observed beliefs and targets. Our implementation
[
In Haystax’ primary risk assessment FUSION application
(called CARBON) including hundreds of BN nodes and
dozens of target beliefs, processing takes just a few seconds.
Our results compare favorably with those of related
published algorithms. Per the recent survey by Mrad et al
[
A given Bayesian network requires the specification of
many individual, point probability values, but intelligence
analysts generally work with probability ranges, as
institutionalized in Intelligence Community Directive (ICD)
203 [
While several of FUSION’s parameters are probabilityvalued, the most prominent way probabilities enter a FUSION-generated BN is via under-the-hood encoding of onthe-dashboard-specified indication strengths. FUSION now encodes strengths using fixed odds ratios per Figure 4. More flexible specifications may be more appropriate for many problem types.
Robust statistical sampling of belief distributions could, in large models, exceed GUI near-real-time thresholds for to-user feedback. Another attractive option, given reasonably tight belief distributions, is to develop only bounds for statement beliefs, exploiting FUSION’s perstatement actor disposition framework (described at Figure 1). Develop a statement’s favorable bound (i.e., the lower bound for an unfavorable statement, upper bound for a favorable one) by considering just other favorable bounds in an unambiguous-statement-only subgraph, and address limited combinatorics over disconnected subgraphs. Belief bars resulting would thus include just two tick marks, rather than a richer distribution shape.
We are working to make FUSION, already a powerful framework for SMEs to author Bayesian network-based models, more complete and versatile for probabilistic argument map applications—in intelligence analysis and in other domains, generally. Allowing summary Logic statements to serve as probabilistic indicators of hypotheses will afford uniform expressive power across a model’s downstream and upstream levels. Supporting arbitrary Logic formulas as constraints as well as summary statements will increase expressiveness and conciseness. Supporting more flexible belief and strength specifications to capture finer probabilistic distinctions will enable more precise modeling by individual SMEs and SME teams or crowds—values for numeric model parameters might be aggregated using crowdsourcing methods.
Our proposed CRAFT variant designed for intelligence analysis will express all of FUSION’s capabilities in analystfriendly vocabulary (appendix A) and develop mechanisms and methods explicitly supporting the modeling of source credibility per Intelligence Community standards (appendix B). A
intelligence analysis alternative—e.g., a possible future (or present, or past) situation. A statement whose probability of truth depends on upstream-neighbor links.
A statement that is in principle knowable from evidence available in the problem domain. A statement reported by some source. Absolutely influences a like-content EvidenceHypothesis that is sourceindependent and accommodates any number of SupportedBy or RefutedBy links from EvidenceReports corresponding to different reports.
A statement characterizing the credibility of an EvidenceReport. Per modeler discretion, either a single node or a conjunctive Logic statement summarizing relevant credibility factors.
A statement posited by an analyst to fill a gap in available information. Plays a modeling role similar to EvidenceReport, when evidence is unavailable.
Validity/ (of Assumption)/
Logic/
assessed legitimacy of a posited Assumption.
A propositional logic expression over other statement nodes, either summarizing or constraining them.
CRAFT will apply to a HUMINT EvidenceReport
statement Schum’s framework [
Below we outline how CRAFT will map (in italics)
specific analytical tradecraft standards (in bold) regarding
items of evidence per ICD 203 [
Factors from ICD 203 D.6.e.1:
1.!Accuracy (use Credibility) and completeness (explicitly
model omitted possibilities as Assumptions or other
appropriate statements)
2.!Possible denial (use Opportunity) and deception
(elaborate a deceptive course of action as an Outcome or
other Hypothesis—see SaddamMaintainsDiplomacy in
Figure 1, e.g.)
3.!Age and continued currency of information (use
temporal relevance)
4.!Technical elements of collection (apply true/false
positive/negative sensor models, confusion matrices—
ancillary to Schum’s framework—see also [
More factors from ICD 206 [
From ICD 206 appendix D.5: 13.! Potential strengths and limitations of available information (see 1) 14.! Notable inconsistencies in reporting (impugn CredibilC ity, per factor as warranted) 15.! Important information gaps (see 1) 16.! Other factors deemed relevant (model as appropriate)
HUMINT is testimonial evidence. Hughes [