Transcript No Slide Title

Interactive Resource Management in
the COMIREM Planner
Stephen F. Smith, David Hildum, David Crimm
Intelligent Coordination and Logistics Lab
The Robotics Institute
Carnegie Mellon University
Pittsburgh PA 15213
[email protected]
412-268-8811
Carnegie Mellon
Carnegie Mellon
IJCAI-03 Workshop on Mixed-Initiative Intelligent Systems - August 9 2003
Outline of Talk
– Brief Introduction to Comirem
– Mixed-Initiative Perspective
– Connection to Workshop Themes
Carnegie Mellon
COMIREM
A light-weight, interactive system for resource
management in continuous planning domains
Domain: SOF planning
Motivating Themes:
– Resource management cannot be considered a
separable post-process to plan generation
– Planning is an ill-structured, iterative process that is
rarely amenable to total automation and not well
supported by batch-oriented solution generators
– Planning involves collaboration among
(increasingly) mobile decision making agents
Carnegie Mellon
Embassy Rescue Scenario
Rebel
Enclave
Staging
Area
Task Force Charlie
(56 Troops)
Bridge
Task Force Bravo
(64 Troops)
Task Force Alpha
(24 Troops)
Home
Airport
Carnegie Mellon
Available at Home Airport
–
7 MH60s
–
–
–
–
5 MH47s
5 MC-130Hs
2 C-141s
1 AC-130U
Rebel
Controlled
Airport
Embassy
250
AmCits
Mixed-Initiative Design Goals
Adjustable decision-making autonomy
– User will want to make decisions at different levels of detail
in different contexts
Translation of system models and decisions
– User should be able to inject decisions without having to
understand system search models and vice-versa be able to
effectively interpret system results
Incremental problem solving
– Constraints typically become known incrementally
– Controlled change facilitates comprehension
– Solution stability is crucial in continuous planning domains
Carnegie Mellon
Constraint Management and Search
Infra-structure
Comirem is a flexible times scheduler:
– Activity start and end times float to the extent that problem
constraints allow
– Activities requiring the same resources are sequenced
Simple Temporal Problem (STP) constraint network solver is
used to manage temporal constraints
– Constraint graph of time points (nodes) and distances (arcs)
Higher-level domain model super-imposed to add reasoning
about resource usage constraints
– Required and provided capabilities
– Resource location (positioning, de-positioning, repositioning)
– Resource carrying capacity (manifests and configurations)
Decisions (user and system) are made opportunistically
Carnegie Mellon
Elements of Mixed-Initiative Approach
Highly interactive - spreadsheet metaphor
Levels of automated decision-making
– Individual decision expansion and options
– Temporal and resource feasibility checking
– Automated solution generation -biased by user goals and
preferences
– User over-ride of any constraint in system model
Interaction via mutually understood domain model
– Translation of domain model edits into internal constraint
models
– Complementary use of domain model to convey and
interpret results
Visualization of decision impact
Carnegie Mellon
Carnegie Mellon
Generating Options
Light-Transport-Activity
MH-60
Capacity: 14
Resource Reqs.
instance
Deploy(A,B,?Res)
MH-60-5
MH-60-4
MH-60-3
MH-60-2
MH-60-1
instance
MH-47
Capacity: 40
Manifest: 120
MH-60 Option
Carnegie Mellon
MH-47 Option
MH-60-4
MH-60-3
MH-60-2
MH-47-1
Generating Conflict Resolution Options
LFT
A
Move
1xMH-60
B
EST
Dur(MH-60) > LFT(Move) - EST(Move)
MH-60
Nom. Speed: 150
Option4: Deploy
earlier
Airdrop-Activity
TF-Deploy-Time
<relMove,∞ >
CZ
Carnegie Mellon
C130
Nom. Speed: 200
StartTime-Constraint
instance
Res reqs.
Option1: Override
computed duration
Option2: Assign
a faster resource
instance
Move(A,B,MH-60)
<dMH-60, dMH-60>
DueDate-Constraint
instance
<0,ddMove>
Detected Cycle
Magnitude: m
TF-Engage-Time
Option3: Delay
engagement
Comirem Positions on Workshop Issues
Task - User manipulates goals, constraints and preferences; system
solves within specified parameters
Control - User in control; system offers decision options wherever
possible and solutions when user delegates
Awareness - Mutually understandable domain model used to
bridge user and system models
Communication - Summarization, visualization of decision impact
Evaluation - increased efficiency/effectiveness; system manages
complexity; user brings knowledge outside of system models
Architecture - Spreadsheet model of interaction; incremental
change
Carnegie Mellon
END
Carnegie Mellon
Functional Capabilities
Interactive Planning and Resource Allocation
–
–
–
–
–
Option generation
Visualization of decision impact
Requirements and capabilities editing
Automated assignment and feasibility checking
What-if exploration
Resource Configuration
– Construction and allocation of aggregate resources
Execution Management
– Resource tracking
– Plan tracking
– Conflict analysis
Carnegie Mellon
A More Complex Conflict Involving
Shared Resources
C
Move2
1xHMMVV
B
A
Move1
1xHMMVV
LFT
EST
Dur(HMMVV) > LFT(Move) - EST(Move)
• Resource sequencing constraint in conjunction
with the timing constraints of Move1 and Move
2 causes “cycle”
Carnegie Mellon
Gantt and Vector Activity Views
Comirem
User
Interface
Resource
Aggregation
Resource Usage & Positioning
Carnegie Mellon
Resource Tracking
Carnegie Mellon