Integrated Framework for Decision Support in Planning

OSU LAIR in collaboration with Richard Kaste and

Mike Barnes (ARL) andPatricia McDermott (MAAD)

Foundations, Technology, Applications.

• Desiderata

– Open architecture

– Should be able to work with variety of external software, such as simulation programs, and support domain-specific add-ons.

– Base part should be domain-independent

– But should commit to an ontology that is general yet provides power.

Exploit advances in computing to go beyond “three distinct COAs.” Generate and simulate lots of COAs. Support understanding as well as selection.

Review of Past Work

Development of Seeker-Filter-Viewer Technology for COA generation, filtering and multi-criterial selectionSet of alternatives Pareto

FilterViewe

rViewer: Tradeoffs can be explored interactively.

Relations between COA specifications

and performance can be visualized. Seeker: Explored Evolutionary Algorithms and composition from component libraries for COA generation. Large number of COAs --Simulator evaluates COAs on multiple-criteria

Filter: Selects Pareto-optimal COAs. Efficient, scales well.

Decision Space Understanding: From Simulation to Insights

• The Viewer as a discovery/hypothesis verification aid to generate insights

• A MOUT example

• Specific Building named Obj + buildings B1 to B4.

• Question: Does taking any of the Bi have any effect on taking Obj?

• In a third of the cases where Obj was not taken B1 was also not taken

• In a third of the cases where Obj was taken, B1 was not taken.

• Heuristic conclusion: Taking or not taking B1 has about the same effect on taking Obj.

How does the occurrence of event E1 affect the outcome?

Network Disruption Planning: An EBO Application

A plan consists of a set of nodes and links to disrupt, which differ in value and costs to disrupt.

Used Evolutionary Programming with Pareto Filter as the “angel of death for each generation” to generate about 300 COAs.

Current Focus1. Helping Planner Generate Robust COAs

2. Decision Support for Sensor Allocation Planning

How to compare COAs under these uncertainties?* Choosing based on most likely is not the answer* Need to explore COAs for robustnessWhat is a robust COA? Our

definition:

Alternatives have approximately similar expected outcomes, but the one whose outcomes are less sensitive to simulation assumptions and uncertainties is the more robust one.

Issue is not just avoiding worst-case scenarios

The commander might be looking for high-risk/high payoff options too.

How can a DSS Help?A DSS should support:

Vary model assumptions & simulate over statistical contingencies of each model.

Robust COAs

Peacekeeping and counter insurgency operations in “Erdistan.”

Experiments in Two Domains

Exploring robustness of an EBO COA for Network Disruption Planning

• Three political/demographical groups exist, with different properties (size, force, desire for democracy, level of contentment with the country etc.).

• The country has properties of its own (democracy, secularity, economy etc.), which depend on the groups' properties and influence them in turn.

• A discrete time simulation: Events and parameters in the current time click influence the ones in the next time click.

• Peacekeeping COAs:– Allocate resources to: Attack units, Propaganda units,

Education units, Economic activities.

• A sets of COAs were subjected to robustness analysis by varying assumptions about Erdistan and how groups behave, to identify which assumptions were crucial, and which COAs were relatively more resistant to the assumptions.

• Disrupt 29-node enemy network• Nodes 2, 3 and 4 are important centers that need to be

isolated, nodes differ in defensive strengths • A COA that targets nodes 12, 14, 16, 23, 27 and 29

chosen• Two criteria of interest: network fragmentation,

bandwidth bet. Centers.• Robustness analysis suggested that the COA was quite

sensitive to the assumptions about the defensiveness parameters for nodes 16 and 19.

• This might call for additional intel resources to obtain more reliable estimates of these parameters.

Decision support for Sensor Allocation Planning (With Barnes and McDermott)

• Example Scenario: There are 11 sensor assets in Alpha Company’s sector and 15 additional sensors in the surrounding sectors. The Battalion commander designates the likely enemy infiltration point as NAI 4. Alpha Company requests robotic sensor coverage in NAI 4 for the next 8 hours. You have 90 minutes to emplace the assets. What is the best configuration?

? ?A 2/505TOC

? 2/505FAS

2 A/2/505

1 C/2/505

3 A/2/505

NAI 1

NAI 4

Alpha company’s sector

2 C/1/505

2 A/1/505

2 B/1/505

2 A/3/505

2 C/3/505

NAI 2

NAI 3UG-1A

U-2A

UG-1C

UG-1B

UG-2C

UG-2B

UG-3C

UG-3B

UG-3AUG-2D

Mule1Mule2Mule3

Mule4

Mule5

ARV1

ARV3

ARV2

ARV4

UG-3D? ?A 2/505TOC

? ?? ?A 2/505TOC

? 2/505FAS? 2/505? 2/505FAS

2 A/2/5052 A/2/505

1 C/2/505

3 A/2/5053 A/2/505

NAI 1

NAI 4

Alpha company’s sector

2 C/1/5052 C/1/505

2 A/1/5052 A/1/505

2 B/1/5052 B/1/505

2 A/3/5052 A/3/505

2 C/3/5052 C/3/505

NAI 2

NAI 3UG-1A

U-2A

UG-1C

UG-1B

UG-2C

UG-2B

UG-3C

UG-3B

UG-3AUG-2D

Mule1Mule2Mule3

Mule4

Mule5

ARV1

ARV3

ARV2

ARV4

UG-3D