Hello my name is Sonia Stange, I'm from Bielefeld University and I would like to
present my supervisor Stefan Kopp's and my work on self-explaining social robots.
In our project context we want to create a lively social robot who will be
living in people's homes and who is supposed to proactively initiate
behaviors that may not always be expected.
This video shows an exemplary user, sitting in their home and watching a documentary,
when a robot not only drives through the Picture, but decides to end its path in the middle of it.
The user is visibly surprised and not in a good way. In order to avoid negative
consequences for the relationship between the robot and the user, the robot
could for example explain its behavior
"My Need for social contact is high"
The robot here explains its reason for the path he chose and stopping in front
of the TV with a need for social contact.
But is this a good way to explai'n its behavior?
And are explanations a useful tool for increasing users'
understanding and desirability of behavior in the first place?
As you saw in the example video increasing a social robots liveliness may lead to a lack of
transparency, which is why we should endow robots
with the ability to explain their behavior in order to render it more
transparent, reduce uncertainty and eventually increase trust towards the robot.
Therefore robots should have the ability to access their reasoning and
express it comprehensively by giving explanations that resemble the way
humans explain behavior
It was found that people's explanations vary as a
function of intentionality, surprisingness and social desirability of the behavior.
That's why we decided to conduct two studies in order to first create a
robust set of robot behaviors and evaluate how they are perceived among
these scales and second evaluate different explanation types and the
impact they have on the users' perception of a robot behavior.
e aim at creating lively social robot behavior motivated through an intrinsic need structure
the robot thus currently has three internal needs which its behavior
originates from and each of these is currently
linked to one specific strategy to fulfill the Need.
For the first study we created twelve robot behaviors, all designed as intentional and supposedly
varying in their desirability and surprisingness,
leading to four categories of behaviors
and I will show you one example for each category.
The first behavior video should be perceived as desirable and not surprising.
The robot moves to its charger because it needs energy, while the user is reading a book.
The second video differs and so far as the user now watches TV and the robot
explicitly chooses a different path to its charger in order to avoid disturbing the user.
Behavior that should be perceived as surprising and desirable
The third video shows behavior that originates from the robots need for social contact.
The robot playfully blocks the users way.
Behavior that is not very surprising, yet depending on the situation, rather undesirable.
And the last Video, which is the one we already saw,
also originating from the robots need
for social contact, also not very desirable but more surprising.
In order to evaluate these behaviors we did an online rating study via Amazon Mechanical Turk.
Intentionality was generally perceived as high with no
significant differences between the behaviors.
When looking at desirability
repeated-measures Anova reveals significant differences between
desirability ratings of the different behaviors, with
the first half receiving higher ratings than the second half in general.
Regarding surprisingness ratings,
in each of the Groups, the first three were designed as less surprising than the second three
As you can see, behaviors 1 to 3 were
perceived as less surprising than behaviors 4 to 6.
For the less desirable behaviors the surprisingness ratings were in general rather high.
As the necessity to explain behavior arises from the fact that it is surprising,
we decided to pick 6 behaviors that are more surprising, for the explanation preference study
leading to the inclusion of the two most surprising
desirable behaviors, number four and six and behavior 8, 9, 11 and 12
But what should such explanations look like?
It is generally assumed that
giving explanations of a robot's behavior that resemble the way humans explain behavior
is intuitively understandable to humans
also, folk-psychological studies have
shown that humans explain intentional behavior in terms of beliefs and desires
human explanations of agent's behavior can also be mapped to mental categories
such as beliefs, desires and intentions. That's why our aim is to generate explanations of
robot behavior, based on these concepts and grounded in the robot's Architecture.
Bertram Malle has conducted various studies on how people explain behavior.
Assuming, that people want to receive the same types of explanations they give from a
robot explainee, we based our robot's explanations on these concepts and
developed five types of explanations a desire based explanations stating the
robots need it is currently addressing such as I needed social contact
An intention based Explanation, stating the robots intended strategy such as
I intended to make eye contact
and a mere action explanation a lower level concept
of the robot action answering the question of what the robot is doing
rather than why such as I positioned myself in your view.
we also decided to include two types of causally structured explanations, combining these reasoning
steps - an intention formation based Explanation, combining the need and
intention based Explanation, and an action selection based explanation
combining the intention and the action based Explanation.
For our second study, which is also the main study, we had the following hypothesis:
First, explanations will generally increase ratings of understandability and desirability
second, causally structured explanations type two and four, will lead to a higher
gains and understandability, desirability and better justify the behaviors and not
causally structured explanations and third why-explanations will analogically
differ from what explanations.
We had 6 behavior videos and each participant saw
are all of these Videos.
After seeing the video, participants rated the robot's intentionality
the behavior's surprisingness,
how well it was understandable and how desirable it was.
Then, each participant saw one of the five types of explanations.
After receiving an explanation participants rated how well the explanation was understandable,
how well the behavior was now understandable, how well the explanation justified the behavior
and how desirable the behavior now was.
We recruited 111 participants via MTurk, 14 of which had to be excluded due to failing the attention check,
leading to 97 participants. Each of the participants are all six behavior videos
but each participant received only one type of explanation.
Concerning the understandability you can see the ratings participants
gave before and after receiving an explanation for each behavior.
Generally, ratings of understandability increased for all behaviors.
As you can see, there were significant differences in the understandability ratings of the
different behaviors before receiving an explanation with behavior four and six
being rated as more understandable than the rest, as well as behavior 9 being
more understandable than behaviors 8, 11 and 12.
Paired samples t-Tests revealed that receiving an Explanation, regardless of the type of Explanation,
significantly increased the understandability of all behaviors except for behavior four.
concerning desirability we were able to reproduce our findings from the pre-study.
Behaviors 4 and 6 were rated as far more desirable than the rest of the behaviors.
Further, behavior 9 was rated as more desirable than behavior 8. 11 and 12.
Again you can see that desirability ratings for all behaviors increased
after receiving Explanation.
Directed paired samples t-tests indicate a
significantly higher perceived desirability after the explanation for behaviors 8, 9 and 11.
As you could see on the previous slide, our dependent
variables were highly correlated which is why we chose to analyze the data with
a multivariate analysis of variance.
Multivariate ANOVA shows, causally structured explanations
led to a significantly higher increase in
understandability than the other types of explanations.
The same goes for desirability:
causally structured explanations led to a significantly
higher increase in the desirability ratings than the other types of explanations.
Concerning the question of how well the different types of
explanations justified the behaviors, we also found a significant difference
between causally structured explanations and not causally structured explanations.
Regarding the what- versus why- explanations, a multivariate ANOVA only
displayed a statistically significant effect of the behavior,
but no significant effect of the explanation type.
So what can we learn from this?
Giving explanations grounded in the robot's architecture does generally lead
to a better understanding and higher social desirability of surprising robot behaviors.
Causally structured explanations lead to a higher gain in
understandability and desirability and better justify behaviors than not causally structured explanations.
And, why-explanations do not significantly differ in their effects, from what-explanations,
implying that stating the mere actions
robot is performing can help the user understand the robot's behavior
Thank you for watching this video and in case you have any questions, remarks or
Feedback, please don't hesitate to contact us!