Learning Performance Models and Tactical Knowledge for Continuous Mission Planning
Navy SBIR 2018.1 - Topic N181-079
ONR - Ms. Lore-Anne Ponirakis - firstname.lastname@example.org
Opens: January 8, 2018 - Closes: February 7, 2018 (8:00 PM ET)
Battlespace, Ground/Sea Vehicles, Information Systems
ACQUISITION PROGRAM: PMS-495,
PEO-LCS, Mine Warfare Environmental Decision Aid Library (MEDAL) Program
The technology within this
topic is restricted under the International Traffic in Arms Regulation (ITAR),
22 CFR Parts 120-130, which controls the export and import of defense-related
material and services, including export of sensitive technical data, or the
Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls
dual use items. Offerors must disclose any proposed use of foreign nationals
(FNs), their country(ies) of origin, the type of visa or work permit possessed,
and the statement of work (SOW) tasks intended for accomplishment by the FN(s)
in accordance with section 5.4.c.(8) of the Announcement. Offerors are advised
foreign nationals proposed to perform on this topic may be restricted due to
the technical data under US Export Control Laws.
OBJECTIVE: Develop machine
learning approaches for automatically acquiring and continually updating asset
performance models and tactical planning knowledge to improve the decision
support by automated mission planning systems in highly dynamic environments,
and to enable their maintainability.
planning tools are commonly used as decision aids for mission planners.
Successful mission planning requires accurate and complete models of the
performance capabilities of the assets, the environment including the behaviors
of other agents in the environment, and mission goals and sub-goals. Current
practice for planning in situations that change is to hand-code the changes in
the models of capability, environment, and goals and then re-plan. This
approach is slow and becomes infeasible in highly dynamic situations,
particularly in tactical mission planning where the tempo of new information
requires rapid changes in the models that may become inconsistent and obsolete
faster than our ability to hand-code new models. This can severely degrade the
quality and effectiveness of automated planning aids to a degree that they may
not be used. This problem is further exacerbated by the introduction of
unmanned assets if there are frequent changes to their sensing and autonomous
capabilities. The Navy needs to develop methods that can rapidly and
continuously plan as new information necessitates updating the models.
PHASE I: To develop and
evaluate the feasibility of the approach, proposers should specify a realistic
application scenario and assets and their performance characteristics. For
example, a scenario of interest is continuous planning for maritime mine
detection and neutralization using unmanned vehicles. Conduct a study of
machine learning approaches that could be used to acquire and update asset
performance models and planning knowledge for automated planning systems.
Assess the feasibility of selected approaches for incrementally and continually
learning performance and planning knowledge in the context of multi-domain,
multi-asset missions. Identify software interface and requirements for
integrating learning algorithms with automated planning aids. Phase I should
include plans for a prototype to be developed during Phase II.
PHASE II: Implement machine
learning algorithms identified in Phase I into a software prototype. Evaluate
the effectiveness of learning over multiple simulated scenarios and systems.
Evaluate and demonstrate the effectiveness using measures such as improvement
in coverage, increased acceptance of planning recommendations and subsequent
increase in mission measures of effectiveness and performance. Work in this
phase may be done at the unclassified level; however, the ability to handle
restricted databases would add flexibility.
PHASE III DUAL USE
APPLICATIONS: Mature and extend the learning algorithms to operate effectively,
be robust, and fault-tolerant to a range of government-provided data and
constraints, in planning systems under their operating conditions. Coordinate
with the program office to fully test and integrate into a potential program of
record. Private sector commercial potential and dual-use applications include
survey and first responder operations.
1. Ozisikyilmaz, Memik,
Choudhary (2008). Machine Learning Models to Predict Performance of Computer
System Design Alternatives. In Proceedings of 37th International Conference on
Parallel Processing. http://cucis.ece.northwestern.edu/projects/DMS/publications/OziMem08B.pdf
2. Garland, Lesh, (2003).
Learning Hierarchical Task Models by Demonstration. Technical Report TR2003-01,
Mitsubishi Electric Research Laboratories. http://www.cs.brandeis.edu/~aeg/papers/garland.tr2002-04.pdf
3. Mohan, Laird (2014).
Learning Goal-Oriented Hierarchical Tasks from Situated Interactive
Instruction. Proceedings of the 27th AAAI Conference on Artificial Intelligence
4. Zhuo, Munoz-Avila, Yang
(2014). Learning Hierarchical Task Network Domains from Partially Observed Plan
Traces. Artificial Intelligence Journal. http://www.cse.lehigh.edu/%7Emunoz/Publications/AIJ14.pdf
Planning; Tactical Mission Planning; Automated Planners; Dynamic Environments;
Machine Learning; Learning Performance Capabilities