A Practical Approach to Understanding Robot Consciousnesspersonal.psu.edu/faculty/l/n/lnl/papers/Conscious_Robot... · 2017-09-01 · UNCLASSIFIED UNCLASSIFIED The Nation’s Premier

UNCLASSIFIED

UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces

UNCLASSIFIED

A Practical Approach to Understanding Robot Consciousness

Kristin E. Schaefer1, Troy Kelley1, Sean McGhee1, & Lyle Long2 1US Army Research Laboratory 2The Pennsylvania State University

Designing a Conscious Robot Workshop, Tucson, AZ, 25 April 2016

UNCLASSIFIED

UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces

Can a robot be conscious? OR

Is it the degree to which the human interacting with the robot perceives it to be trustworthy?

UNCLASSIFIED


CONSCIOUSNESS is MORE than just INTELLIGENCE Kelley, T. D., & Long, L. N. (2010). Deep Blue cannot play checkers: The need for generalized intelligence for mobile robots. Journal of Robotics.

Can a robot be conscious?

UNCLASSIFIED



Is CONSCIOUSNESS the same thing as AWARENESS? •  Subjectivity: Meaning derived from ones

own ideas, moods, and sensations

•  Unity: All sensor modalities melded into one experience

•  Intentionality: Experiences have future meaning

•  Others?

Kelley, T. D., & Long, L. N. (2010). Deep Blue cannot play checkers: The need for generalized intelligence for mobile robots. Journal of Robotics.

Ex Machina, Universal Pictures

UNCLASSIFIED


If a robot exhibits emotions, does that make it conscious?

Emotions such as fear, anger, sadness, happiness, disgust, and surprise can be modeled theoretically, and vary due to rewards and punishments. Long, Lyle N., Kelley, Troy D., and Avery, Eric S., "An Emotion and Temperament Model for Cognitive Mobile Robots," 24th Conference on Behavior Representation in Modeling and Simulation (BRIMS), March 31-April 3, 2015, Washington, DC


Eight emotions that vary with time

Positive and negative

reinforcement

Fixed coefficients that define

temperament

UNCLASSIFIED


BUT, is trying to design a conscious robot a practical approach?

Photo: Mark Crosby (2015) My Robot Helper

Photo: http://www.army.mil/article/19042/Robots_reduce_risks_for_paratroopers/ http://www.theatlantic.com/technology/archive/2013/09/funerals-for-fallen-robots/279861/

Example of a MARCbot

Practical Example

Is it the degree to which the human interacting with the robot perceives it to be trustworthy?

UNCLASSIFIED


“Never trust anything that can think for itself if you can't see where it keeps its brain.”

~ J. K. Rowling

•  Most people do not know how a robot makes decisions •  So, is it really “seeing” the brain, or our perception of

the robot and the actions associated with the decisions that can impact our trust?

TO TRUST OR

NOT TO TRUST

Sensors Action The

“Black” Box

Data

A Practical Approach

UNCLASSIFIED


Human-Robot Trust

Physical Form affects perceptions of trustworthiness

•  Stimuli: 49 pictures different real-world robots, 7 robot domains •  Participants: Over 200 novice participants •  Findings: Ratings of perceived intelligence (PI), “robotness” (RC), and

negative social influence (SI) can be used to predict trustworthiness of a robot from providing no other information than a picture of the robot

Ŷtrustworthiness = Constant + PI + RC – SI

People form expectations before ever interacting with a robot.

Schaefer, K.E., Sanders, T.L. Yordon, R.E., Billings, D.R. & Hancock, P.A. (2012, September). Classification of Robot Form: Factors Predicting Perceived Trustworthiness. Proceedings of the 56th Annual Human Factors and Ergonomics Society (pp. 1548-1552). Boston, MA.

UNCLASSIFIED

UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces Schaefer, KE (2013) The Perception and Measurement of Human-Robot Trust. Doctoral Thesis. Figure 14

Rep

utat

ion

Culture

Measure Trust

Propensity

Human Society

Gather Information about HRI

task

Team Members

Robot

Pre-Interaction Trust

Measure Initial Trust

Measure Human States

HRI Information

Robot Capabilities

LOA Intelligence

Mode of communication

Expectancy

Previous

Experience

Measure Attitudes toward robot

Societal Influence HRI

Tasking

Human-Robot Trust

Ŷtrustworthiness = Constant + PI + RC – SI

Boston Dynamics, BigDog

Expectations

UNCLASSIFIED

UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces (Schaefer, 2013; Figure 14) <<additional interaction with robot>>

<<in

tera

ctio

n co

mpl

ete>

> Rep

utat

ion

Culture

Measure Trust

Propensity

Human Society

Gather Information about HRI

task

Team Members

Robot

Pre-Interaction Trust

Measure Initial Trust


HRI

Measure Post-

Interaction Trust

Post -Interaction Trust HRI Information

Robot Human

Robot Capabilities

LOA Intelligence


Environment: Team & Task

Robot Capabilities

LOA Intelligence


Expectancy

Perceived Risk

HRI

Previous

Experience


Measure Attitudes toward robot

Societal Influence

Human-Robot Trust

UNCLASSIFIED


A Paradigm shift - from Tool to Team Member

From teleoperation…

…towards autonomous operation

•  Understands its environment

•  Conducts useful activity

•  Acts independently, but…

•  Acts within prescribed bounds

•  Learns from experience

•  Adapts to dynamic situations

•  Possesses a shared mental model

•  Communicates naturally

An Unmanned System that

“Give me a robot that acts like my bird dog” ~MG William Hix, Deputy Director, ARCIC

UNCLASSIFIED


Robot Design

Long, L.N., & Kelley, T.D. (2010). Review of Consciousness and the Possibility of Conscious Robots. Journal of Aerospace Computing, Information, and Communication, 7, 68-84.

A theoretical approach for designing the underlying information processing architecture

Goal: Move away from over-specialized design to more generalizable decision-making capabilities

UNCLASSIFIED


•  Two Algorithms to identify novel events and enhance episodic indexing

•  Benefits of this approach: •  This allows associative cues to be set

to novel information •  Allows the anticipation of future novel events

following one exposure to new stimuli •  New Approach: This provides the

computational justification for episodic indexing of information as a post hoc process

•  Provides justification for certain robot behaviors

Example Algorithms

Novel Event

Event Cue Event Cue

Is there a link between the underlying computational architecture and the associated perceptions of the person?

UNCLASSIFIED


Novelty Algorithm

Kelley, T. D., & McGhee, S. (2013, May). Combining metric episodes with semantic event concepts within the Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS). In SPIE Defense, Security, and Sensing (pp. 87560L-87560L).

Let α = vector of observations Let β = number of observations in α Let µ = matrix of observation correlations Let T = threshold value for a correlation Let B = % of T, give set of observations Let γ = number of correlations that exceed the threshold T

Let α = vector of observations Let β = number of observations in the vector α Let µ = matrix of observation correlations (2 dimensions) Let T = threshold value for a correlation value to be considered “bored” Let B = percentage of T for the robot to be considered “bored” for the given set of observations Let γ = number of correlations that exceed the boredom threshold T γ 0 for i = 0 β – 1

for j = 0 β – 1 if i == j continue µ i,j correlation(α i , α j) if µ i,j > T then γ γ + 1 end

end or, where x = correlation(α i , α j):

γ = # 𝑓(x)𝛽−1

𝑖,𝑗=0𝑖≠𝑗

> T

Let τ = percentage of correlations that exceed the boredom threshold T τ = γ/ ((β2 – β)/2) if τ > B then RobotStatus “BORED” else RobotStatus “NOT BORED” end

Let α = vector of observations Let β = number of observations in the vector α Let µ = matrix of observation correlations (2 dimensions) Let T = threshold value for a correlation value to be considered “bored” Let B = percentage of T for the robot to be considered “bored” for the given set of observations Let γ = number of correlations that exceed the boredom threshold T γ 0 for i = 0 β – 1

for j = 0 β – 1 if i == j continue µ i,j correlation(α i , α j) if µ i,j > T then γ γ + 1 end

end or, where x = correlation(α i , α j):

γ = # 𝑓(x)𝛽−1

𝑖,𝑗=0𝑖≠𝑗

> T

Let τ = percentage of correlations that exceed the boredom threshold T τ = γ/ ((β2 – β)/2) if τ > B then RobotStatus “BORED” else RobotStatus “NOT BORED” end

Nothing has changed

Novel event occurred

% of correlations that exceed the threshold

UNCLASSIFIED


Episodic Indexing Logic Flow

Episodic indexing allows anticipation of future novel events following one exposure to new stimuli

Identify the event prior to the novel event (3e) Create New Episode (nE) starting with the event just prior to the novel event

(nE) = (3e:6e) Convert 3e to symbolic information based on current goal (g) and current symbolic perceptual (p) information

(nE) = (ei (g,p):6e) Convert Novel events (4e:5e)= to symbolic (s) event information

(nE) = (ei (g,p):(s):6e) Convert 6e to reinforcement information (R)

(nE) = (ei (g,p):(s):R) Repeat until the end of collected episodes

resulting in

1 2 3

4 5 6

..n

Episode (E) = set of events (1e:ne)

Last event before novel Event (3e)

Novel Events (4e:5e)

Reinforcement (R)

{nE | nE = en(goal/perception):(s):R} Kelley, Troy D., (2014), Robotic Dreams: A Computational Justification for the Post-Hoc Processing of Episodic Memories, Intl. Jnl. of Machine Consciousness, Vol. 6, No. 2, pp. 109- 123.

UNCLASSIFIED


Trust Calibration

Episodic Indexing could help calibrate trust

Do expected behaviors match actual behaviors? •  Improving the underlying architecture could be

linked to outward robot behaviors that exude the capability to learn

How does the person know that the robot knows what is going on? •  Appropriate feedback is important to enhancing

situation awareness and calibrating trust (Schaefer & Straub, 2016)

•  If it is possible to identify early event cues, then it could be possible to provide better feedback timing.

Example: Why did the driverless vehicle stop?

The New Yorker, Nov 2013 Schaefer, KE, & Straub, E. (2016). Will passengers trust driverless vehicles? Removing the steering wheel and pedals. In Proc. IEEE CogSIMA. San Diego, CA.

UNCLASSIFIED


Conclusion

Practical Approach: Near-term robots that can make appropriate decisions in novel, high-risk environments

Successful Human-Robot Interaction: This is based in part on the trust perceptions of the person interacting with the system •  Individuals may have very limited knowledge of how a robot makes

decisions or processes information •  All they “know” is based on the behaviors of the robot and the feedback

from the robot

Possible Considerations: •  Information processing approach to robot design is fast and

relatively simple •  Episodic indexing was found to be efficient process for recognizing

novel events and helping to store memories •  Trust Calibration: The concept of episodic indexing could be linked to

the timing of robot feedback

UNCLASSIFIED


Contact Information

Trust: Kristin E Schaefer [email protected]

Novelty Algorithm: Sean McGhee

[email protected]

Intelligence/Episodic Indexing: Troy Kelley [email protected]

Information Processing/Robot Emotions: Lyle Long

[email protected]

Documents

A Practical Approach to Understanding Robot Consciousnesspersonal.psu.edu/faculty/l/n/lnl/papers/Conscious_Robot... · 2017-09-01 · UNCLASSIFIED UNCLASSIFIED The Nation’s Premier