Machine Reasoning and Learning
Workshops III and IV Kickoff
Wen Masters
Office of Naval Research
Code 311
Wen.Masters@navy.mil
(703)696-3191
A Desired Future State
ISR Information
(hours-days latency)
Commercial
Information
(minutes-hours-days)
C2 Information
(mins-hours latency)
Information Space
Real Time Fire
Control Information
(fractions of secs
latency)
2
Mission-Focused Autonomy
The control of networks of diverse sensors designed to seek,
understand and shape battlespace in complex, uncertain
environments with the following capabilities:
– Independently understands commander’s intent regarding missions and/or
objectives
– Understands battlespace (events, activities, entities, networks of entities, etc)
based on data it has collected or has access to via other sources
– Assesses information to determine shortfalls and threats in the battlespace relative
to commander’s intent
– Optimally (resources, time, significance) determines/evaluates options for courses
of actions and self-tasks specific components of network sensor(s) to resolve
shortfalls and threats
– Executes tasks as it adapts to changing conditions and is self-aware and teamaware
Vision: Develop autonomous control that intelligently
understands and reasons about its environment relative to its
objectives and independently takes appropriate actions
– Intelligently alerts proper forces or commanders to engage critical threats
3
Machine Reasoning and Learning Overarching Challenges and Goals
Overarching Naval/ Marine Corps Goals
• Reducing manpower while integrating and
interpreting large volumes of data from sensors
of multiple types with HUMINT, other –INT, and
open source data, formulating hypotheses, and
plans to resolve uncertainty, imprecision,
incompleteness, and contradiction to achieve
commander’s intent
• Focusing the available manpower on cognitive
tasking instead of orchestrating the collection
and processing of data
• Increasing the operational tempo and shaping
the battlefield through increased automation
while operating in a dynamic uncertain
environment
•
Dispersing geographically while locally
achieving the desired effect
• Achieving disaggregated functionality while
minimizing resources and providing flexibility
Overarching S&T Challenges
• Automation and robustness of the overall
system that can also interpret the current state
of knowledge in the context of the mission
• Defining the information needs of the mission
and creating courses of action to improve the
state of knowledge
• Computing with quantitative and qualitative
data that are uncertain, imprecise, incomplete,
and contradictory
• Understanding how much error/uncertainty can
be tolerated within the holistic system while
achieving a correct inference/decision
• Defining context and employing context
• Representations for data and knowledge
• Defining clutter and the background
• Establishing fundamental performance limits for
the ability to detect, track and identify objects;
for establishing relationships, activities, and
events
• Aligning data sets with disparate signatures in
space and time
• Developing an information infrastructure that
supports distributed large data sets
Machine Reasoning and Learning –
Questions ONR Thinks About
Workshops I and II
Workshop III
Workshop IV address System Integration
Success for these Workshops
• Identify critical issues and high payoff approaches that will
enable Machine Reasoning and Learning related to
Action/Reaction and Integration
• Community
– Create awareness of the problem and issues
– Formation of an interdisciplinary community that
addresses the key issues
• Information that supports ONR planning
– Potential redirection of existing program goals
– 6.1 and 6.2 Broad Agency Announcements targeted at
specific issues
– MURI Topics
– SBIR/STTR
– Other mechanisms