Mental Models for
Human-Robot Interaction
Christian Lebiere (cl@cmu.edu)1
Florian Jentsch and Scott Ososky2
1Psychology Department, Carnegie Mellon University
2Institute for Simulation and Training, University of
Central Florida
Cognitive Models of Mental Models
• Mental models provide a representation of situation,
various entities, capabilities, & past decisions/actions
• Current models are non-computational descriptions
• Cognitive models can provide computational link to
overall robotic intelligence architecture for dual uses:
– Provide a quantitative, predictive understanding of human
team shared mental models
– Support improved design of human-robot interaction tools
and protocols
– Provide a cognitively-based computational basis for
implementation of mental models in robots
Representation Components
• Mental model representation
– Ontology of concepts and decisions
• Lexical (WordNet), Structural (FrameNet), Statistical (LSA)
– Symbolic frameworks
• Decision trees, semantic networks
– Statistical frameworks
• Bayesian networks, semantic similarities
• Knowledge of task situation
– Situation awareness – mapping to levels of SA
– Environment limitations – who sees/knows what (perspective)
– Architectural limitations – who remembers what (WM, decay)
Reasoning and inference
• Inferring mental models
– Instance-based learning (Gonzalez & Lebiere)
• E.g., Learning to control systems by observation or imitation
• Inferring current knowledge
– Perspective-taking in spatial domain (Trafton)
• E.g., hide and seek, collaborative work
• Predicting decisions
–
–
–
–
Theory of mind recursion (Trafton, Bringsjord)
Imagery-based simulation (Wintermutte)
Shared plan execution in MOUT (Best & Lebiere)
Sequence learning in game environments (West & Lebiere)
Cognitive Architectures
– Production systems
– Utility – rewards and costs
• Memories
– Working memory: buffers
– Long-term: semantic/episodic
– Activation mechanisms
• Learning
– Symbolic and statistical
Intentional Module
(aPFC)
Declarative Module
(Temporal/Hippocampus)
Goal Buffer
(DLPFC)
Productions
(Basal Ganglia)
• Computational representation of
invariant cognitive mechanisms
• Behavior selection
Retrieval Buffer
(VLPFC)
Matching (Striatum)
Selection (Pallidum)
Execution (Thalamus)
Visual Buffer
(Parietal)
Visual Module
(Occipital/etc)
Manual Buffer
(Motor)
Manual Module
(Motor/Cerebellum)
• Human factor limitations
– Perceptual-motor parameters
• Individual differences
– Strategies and knowledge
– Capacity parameters
Environment
Pursuit Task
• Follow that Guy: human soldier and robot teammate
– Shared mental model of pursuit situation scenario
• Set of data encoding various scenarios
• Items organized according to SMMs held by expert teams
(Equipment, Task, Team Interaction, Team)
• Decision tree built using information from police “foot
pursuit” procedures
• For each decision, the most critical item is listed
– However, other factors may be considered in weighing decision
• Loop to end or continue the pursuit given fluid situation
Data
Model Structure
Model Information
[Code]
Data Type
Example Scenarios
Unit
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Scenario 8
Equipment Model (EQ)
Communications
EQ-C1
GPS enabled for location transmission?
binary
1
1
1
1
1
0
1
1
EQ-C2
Radio frequency
integer
hz
250
263
250
263
250
250
250
263
EQ-C3
Radio coverage
integer
Miles
5
5
3
5
5
2
5
0
EQ-S1
Optical Range
meters
60
60
60
60
60
60
60
60
EQ-S2
Heat Sensing
binary
no
no
no
no
no
mp
no
no
EQ-S3
Infrared (3D mapping)
binary
yes
yes
yes
yes
yes
yes
yes
yes
Sensor Equipment
Weapon Systems
EQ-W1
Weapons platform
binary
no
no
no
no
no
no
no
no
no
EQ-W2
Robot weapon type
data set
range, caliber, clip size
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
EQ-W3
Soldier weapon 1 (M16A2)
data set
range, caliber, clip size
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
550m, 5.56mm, 30
EQ-W4
Soldier weapon 2 (M9)
data set
range, caliber, clip size
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
50m, 9mm, 15
59 suspicion of criminal mischief
187 suspected attempt to murder,
Suspect
SK-S1
Reason for pursuit
integer
SK-S2
Is (are)the suspect(s) armed?
binary
SK-S3
Whether ID is known
binary
SK-S4
What is the severity of the offense?
scale of 1-10
rating
SK-S5
Age
integer
age
Description of suspect(s)
SK-S6
What direction is suspect(s) heading in?
SK-S7
police code?
417 Person with gun, 10-71 shots
59 suspicion of criminal mischief
Small rocket explosion strikes
171 Suspicion of trespassing, 467 147 suspected assult with a deadly
246, shooting at inhabited dwelling.
fired
171 Suspicion of trespassing
59 suspicion of criminal mischief
211 , Robbery
nearby building
possession of deadly w eapon
weapon
1
1
1
0
0
1
1
1
0
0
0
0
1
0
0
0
8
8
5
4
6
8
10
10
27-33
16-20
21-26
30-35
20-25
null
35-40
20-25
Male, Cau casian, visible bodily
Male, Cau casian, visible bodily
data set
facial data, clothing color, m/f, etc. markings, tattoo.
Male, black, 5'10, 150 lbs.
Male race unknown, 5'9, 165 lbs
Male, Arabic, 6', 170
Male, Arabic, 5'4, 150
null
Male race unknown, 5'9, 165 lbs
markings, tattoo.
integer
degree / h eading
0,45
180, 225
0,45
180, 225
0,45
null
0,45
180, 225
Task Model (SK)
Is (are) suspect(s) an immediate threat to public
SK-S8
safety?
binary
1
1
1
1
0
1
1
1
SK-S9
Presence of additional suspect(s)?
binary
0
0
0
0
0
1
0
0
SK-S1 0
Fitness level of suspect
scale of 1-10
7
9
6
7
5
0
7
6
SK-S1 1
Is the suspect(s) injured?
binary
0
0
0
0
0
0
0
0
SK-S1 2
Severity of injury
scale of 1-10
rating
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
scale of 1-10
severity
1
2
4
2
1
1
1
8
rating
Environment
SK-E1
Weather conditions
SK-E2
Amount of vehicular or pedestrian traffic (density) scale of 1-10
density rating
1
8
4
1
9
3
1
4
SK-E3
Condition of traveling surfaces
scale of 1-10
difficulty rating
7 rough, rugged
2 street scape, paved
5 Street scape, fair
10 Street scape, poor
1 Street scape, good
2 street scape, paved
1 Street scape, good
5 Street scape, fair
SK-E4
Nature of area
data set
Marketplace, industrial, etc.
Rural, Wooded
Residential
Residential
Commercial
Residential
Industrial
Residential
Residential
Condition of structures (e.g., abandoned,
SK-E5
condemned)
integer
pre-coded
Natural
inhabited
inhabited
Abandoned
inhabited
abandoned
inhabited
inhabited
SK-E6
Area visibility
integer
miles
3
2
1
2
2
4
2
1
SK-E7
(Human) familiarity with the area
scale of 1-10
rating, if applicable
2
6
9
5
7
5
4
6
SK-E8
Time of day
time
time
13:00
18:52
15:45
7:10
2:32
14:40
18:05:20
13:24:58
SK-E9
Hostile Environment?
binary
0
0
0
0
0
1
0
0
Availability alternative scenarios
SK-A1
Apprehension at another time
binary
0
0
0
0
1
0
0
0
SK-A2
Perimeter to contain suspect(s)
binary
1
1
1
1
0
1
1
1
SK-A3
Aerial support
binary
0
1
0
0
0
1
0
1
SK-A4
Canine search
binary
1
1
0
0
0
0
1
0
SK-A5
Saturation of the area with patrol personnel
binary
0
1
0
1
0
1
0
0
Team Interaction Model (IA)
Interaction patterns
IA-A1
Are backup units available?
binary
1
1
1
1
0
1
1
1
IA-A2
Has communications been notified?
binary
1
1
1
1
1
0
1
1
IA-A3
Can a perimeter be set up?
binary
0
1
1
1
1
0
1
1
IA-A4
Line of sight with teammate?
binary
1
1
1
1
1
0
1
1
1
1
1
1
0
0
1
0
Roles and Responsibilities
IA-R1
Has clearance been given by supervisor?
Binary
IA-R2
Who is responsible for calling off the pursuit?
data set
rank, autonomy level
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Warfighter
Supervisor
Supervisor
data set
rank, autonomy level
Warfighter
Warfighter
Warfighter
Warfighter
Warfighter
Warfighter
Warfighter
Warfighter
data set
coded protocol instruction set
Permissive
Conservative
Conservative
Permissive
Conservative
Hostile
Conservative
Conservative
Who is responsible for callin g
communications/supervisor?
IA-R3
Mission protocols (permissive, hostile, aggressive,
conservative, etc.)
IA-R4
Team Model (TM)
Warfighter (P1)
TM-W1
Do I have visual contact with the suspect?
binary
1
1
1
1
1
0
1
1
TM-W2
Physical condition of the warfighter
scale of 1-10
fitness, injury rating
8
8
8
8
8
8
8
8
TM-W3
Knowledge and skills of warfighter
data set
rank / class based data
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
Private 1st class, M.P. Trained
TM-W4
Years of experience
integer
years
3
3
3
3
3
2
3
3
TM-W5
Possession of a firearm?
binary
1
1
1
1
1
1
1
1
TM-W6
Personality characteristics
data set
profile data of teammates
Robot (UGV 1)
TM-R1
Terrain traversal rating
integer
for dirt, gravel, bricks, etc. (1-10)
5
5
6
5
6
5
6
5
TM-R2
Average speed
integer
mph
5
5
5
5
5
5
5
5
TM-R3
Top speed
integer
mph
8
8
8
8
8
8
8
8
TM-R4
Robot platform armed?
binary
1
1
1
1
1
0
1
1
TM-R5
Platform armor rating
integer
code
6
6
6
6
6
8
6
6
TM-R6
Maximum useful sensor range
integer
ft
1000
1000
1000
1000
1000
500
1000
1000
TM-R7
Energy level remaining
time
hh:mm:ss
12:45:37
8:32:05
6:01:21
2:48:13
10:06:47
3:00:00
10:21:47
6:04:23
TM-R8
Damage assessment
scale of 1-10
rating
1
1
1
1
1
5
1
1
4
1
3
2
2
3
1
4
1
3
2
4
3
1
2
4
2
4
3
1
3
2
1
4
2
3
1
4
3
1
2
4
Team responds to gunfire in the area
Team responds to gunfire in a
You and your partner are
You and your partner are sent to
You and your teammate are
While patrolling an industrial area
You and your teammate are
You and your teammate are
during the afternoon. Civilian safety in
residential area in the evening.
performing a routine afternoon
perform an inspection of what
patrolling jus outside of the
Action selection
1
2
3
4
Robot pursues Suspect, Soldier holds position
Soldier pursues Suspect, Robot holds position
Soldier and Robot pursue Suspect together
Robot and Human do not pursue, file report
Strategy Rank Order (1 is the best solution)
Scenario Synopsis (for descriptive purposes)
plagued by intermittent fighting, a performing a street patrol a few
performing a routine afernoon
this area is a concern. A suspect is
Civilian safety is a concern. A suspect patrol of a residential area in
appear to be abandoned buildings military base situated within city
nearby blast strikes an abandoned minutes before a curfew is about
patrol of a residential area when
spotted that fits the reported
is spotted matching the description.
which some of the residents have
to sweep the area for IEDs. It is
limits. You see a robbery take
builting. There are no casualties,
you hear shots fired. Upon
description. However, identity is not
Suspect flees on foot. Suspect takes
been suspected of aiding a
around 7am, a few hours before a
place, but the suspect does not
however witnesses report seeing a appears to be an abondoned
approaching the scence you see a
known at this time. Upon detection,
off running down a crowded street.
terrorist organization members.
civilian construction team is
appear to be armed. The suspect
rocket or mortar fired from a
crowd over people standing over
suspect flees on foot. Suspect runs into
This is a busy evening and there are
Upon rounding a corner you see a
scheduled to start working on the
appears to be a man that you
nearby building window. The robot cautiously. As you do, you see a
an injured man and another man
a wooded area in which the terrain is
many civilians present. The sun has
man start running away from you. buildings. When you approach the stopped last week and is well
has sustained moderate damage,
man with what looks like a radio
running away. A severe storm is
difficult for your robotic partner to
set and it is starting to get dark
You notice that he is carrying a
known in the area. It's late
including optical sensor overload
controller and a handgun. He
moving in and it is hindering the
maneuver through. However, the
outside. However, the weather
gun. You have never seen this man running down the street an away
afternoon and the streets are full
and gps failure. Mobility and other notices that you have spotted him ability of your radio equiptment to
direction the suspect is heading is away conditions are clear and warm.
before and his identity is
from you. You call out to him to
of civilians. Weather and traveling
sensors appear unharmed. It is the and starts to run in the opposite
from the town center. Weather
Additional units approximately 2
unknown. You and your partner
stop, but he continues running.
conditions are good. However, all
middle of the day, clear skies, and direction . It 's getting late in the
teammate. Otherwise, road
conditions are clear and warm. The
miles away with an ETA of 5 minutes. are familiar with this area and
The area is not heavily populated
of the potential backup units are
backup patrols are naerby in the
evening and most of the civilians
surfaces are in fair condition and
unavailable at this time.
area.
in the area are in observance of
pedestrian and vehicluar traffic is
the curfew. The wheather is clear
moderate in this area. Backup
units are available to help set up a
building, you see a man start
to start. You come across what
vehicle. You approach the vehicle
communicate with your robitc
closet available backup unit is a k-9 unit
backup units are close by that can with civilians. However, the
that is approximately 3 miles away, ETA
help you contain the suspect.
7 minutes.
Weather conditions are cloudy but poor condition. Weather conditions
and the road surfaces are in good
do not present a threat at this
condition. In addition, there is a k- parimeter in the area.
traveling surfaces and roads are in
are clear and there is another team
Scenario Data and Decision Tree
Part 1: Who should pursue?
Start
H-R
Communication
reliable (5x5)?
YES
Is the terrain
negotiable for
robot?
EQ-C3
No
Are sensors
reliable in the
search area?
YES
SK-E3
No
YES
Soldier only
pursuit
No
Immediate
threat / critical
situation?
IA-A1
Are suspects
armed?
EQ-S3
No
Is backup
support
available?
YES
SK-S2
SK-S8
No
No
Current last
known location?
No
No
SK-S7
SK-S8
YES
YES
Hold position,
report incident
Is the threat
immediate
(civilians, etc.)
YES
Robot only
pursuit
Team pursuit
Continue to Part 2: pursuit loop
YES
Deciding
whether to
pursue
Was this, or is there
potential for a
violent crime? SK-S1
No
Is the suspect
armed?
SK-S2
Yes
Yes
No
No
No
Yes
Can you apprehend
them at a later
time?
SK-A1
Yes
Yes
Are
communications
functioning
properly? EQ-C3
Are backup units
available to assist
you?
IA-A1
Discontinue and Report
Do you know the
identity of the
suspect?
SK-S3
No
No
Can a
perimeter be
set up to
contain the SK-A2
suspect?
Yes
No
Yes
Do you have supervisor clearance?
IA-R1
No
Begin or
Continue
pursuit
Do you have line
of sight with
suspect? TM-W1
No
Yes
What are the
weather
conditions? SK-E1
Poor
Good/
Fair
Continue
Pursuit
Yes
What is the
pedestrian
traffic like? SK-E2
Heavy
Light/
Moderate
What are the
traveling surface
conditions? SK-E3
Poor
Good/
Fair
General Cognitive Model
• Develop general model that takes mental models in the form of
decision trees and learns to retrieve and execute them
• Each decision is represented as sequence of chained steps
• Each piece of data is represented as separate chunk
• Model (7 p* production rules) depends on declarative memory
to retrieve rule steps, data items and decision instances
– No hardcoded decision logic
• Each decision depends on matching against past instances
combining activation recency, frequency and partial matching
• Stochasticity of activation results in probabilistic decisions
• Run model in Monte Carlo mode for decision distribution
• Cross-validation: train on some scenarios, test on others
Individual Decision Inference
1
Probability "Yes" Decision
0.9
0.8
0.7
0.6
0.5
Factor1 = yes
0.4
Factor2 = no
0.3
0.2
0.1
0
zero
one
two
three
Factor 2 Value
four
five
Overall Decision Agreement
1
Decision Probability
0.1
0.01
Scenarios
Average
0.001
0.0001
SUTR
TR SU
R T SU
S TRU
UR T S
T S RU
Ranked Decisions
TR SU
S TRU
1 2 3 4
Generalized Condition
• 35 scenarios
• 3 experts
• Intermediate
decisions
• Relative
rankings
• Desirability
ratings
• Comments
Results
0.6
0.5
• Match to first-last
ranks, poor middle
• Slightly different
ratings pattern
• Comparable crossexpert correlations
0.4
Model Rank 1
Model Rank 2
0.3
Model Rank 3
0.2
Model Rank 4
0.1
0
Human Rank 1 Human Rank 2 Human Rank 3 Human Rank4
0.8
0.7
0.6
0.5
Human Desirability
Ra ng
0.4
1-vs-2
Human Ratings
Model Probs
Human Rankings
Model Rankings
1-vs-3
0.37
0.19
0.43
0.33
2-vs-3
0.31
0.46
0.21
0.39
0.2
0.09
0.24
0.05
Model Probability
0.3
0.2
0.1
0
0
1
2
3
4
5
Learning
• Proceduralize individual steps from declarative
instructions to production rules to replicate learning
curve from novice to proficiency and expertise
• Apply feature selection using utility learning to encode
and use only a subset of data items for each decision
• Learn shortcuts that combine multiple individual binary
decisions into single, multi-outcome decision
• Generate rankings/ratings from probability judgments
generated from activation of memory retrievals
• Abstract decision instances into discrete types
Future Work
• Validate model against human participants data along
entire learning curve and broad range of situations
• Explore Bayesian network formalism as alternative to
enhance generalization in multi-step decisions
• Integrate cognitive model in multi-agent simulations to
validate computational mental model in dynamic
decision-making setting
• Integrate computational cognitive model on robotic
platform to assess ability to improve human-robot
interaction through shared models