Talk at the IJCAI 2017 Workshop on Cognition and Artificial Intelligence for Human-Centred Design, 19 Aug. 2017, Melbourne, Australia

1. Towards A Dual Process Approach to Computational
Explanation in Human-Robot Social Interaction
Agnese Augello, Ignazio Infantino, Antonio Lieto, Umberto Maniscalco,
Giovanni Pilato, Filippo Vella
ICAR-CNR, National Research Council, Palermo, Italy
Dipartimento di Informatica, University of Turin, Italy
IJCAI 2017 Workshop on Cognition and Artificial Intelligence for Human-Centred Design, 19 Aug. 2017, Melbourne,
Australia

2. Topics/Outline
- Explanatory Capabilities of AI Systems
- Current Problems
- Proposal based on a dual process-approach
to computational explanation
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3. “Explanatory Needs” are not New in AI
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Cybernetics
Computational Cognitive Science
From Human to Artificial Cognition (and back)

4. Explainable AI - Nowadays
- The current request for Explainable AI (XAI) is something
different with respect to the previous notion of “explanation”
- AI is looking for systems able to provide a transparent
account of the reasons determining their behaviour (both in
cases of a successful or unsuccessful output)
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5. Explainable AI - Nowadays
- The current request for Explainable AI (XAI) is something
different with respect to the previous notion of “explanation”.
- AI is looking for systems able to provide a transparent account
of the reasons determining their behaviour (both in cases of a
successful or unsuccessful output)
Problem: The adoption of current Machine Learning and Deep
Learning techniques faces the classical problem of opacity in
artificial neural networks (this classical problem explodes in Deep
Nets)
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6. 6
Clarification: “Opacity” does not mean, in principle, “impossible to
Explain”
Inputs can be either removed or modified until the output changes
in a way that is important to the user. This is a trial and error
process, time consuming…very complicated in practice.
E.g. Model based neural networks (mid’80): their connections are
parametrised to satisfy specific constraints implied by a putative
causal model (e.g. approximated).
There are also recent attempts to provide an interpretation of deep
nets (e.g. Zhou et al. 2015) but the general problem remains
largely unsolved.
Opacity and Explanation

7. 7
Since the adoption of deep ANNs is important for improving the
performance of artificial systems but is problematic for solving the
explanatory problem we demanded the latter task to a second
component:
- inspiration from the dual process theory of reasoning
(Stanovitch and West, 2001; Evans and Frankish 2009;
Kahnemann 2011).
- the two software components perform different types of
reasoning.
Our Proposal

8. Dual Process Reasoning
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(Stanovitch and West, 2000; Kahnemann 2011).
In human cognition, type 1 processes are executed fast
and are not based on logical rules. Then they are checked
against more logical deliberative processes (type 2
processes).
… …
Type 1 Processes Type 2 Processes
Automatic Controllable
Parallel, Fast Sequential, Slow
Pragmatic/contextualized Logical/Abstract

9. Dual Process Reasoning
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In human cognition, type 1 processes are executed fast
and are not based on logical rules. Then they are checked
against more logical deliberative processes (type 2
processes).
Type 1 Processes Type 2 Processes
Automatic Controllable
Parallel, Fast Sequential, Slow
Pragmatic/contextualized Logical/Abstract
Deep Nets as S1 systems Ontologies as S2 systems

10. The Scenario
• Robotic Reception in a public office
welcoming visitors in the waiting room and
directing them to proper office rooms
• The robot must be able to discriminate the
not appropriate behaviors of the visitors
and act accordingly.

11. The Scenario
• The robot learns how to detect not appropriate and
in particular aggressive behaviors, by examining the
postures and the gestures of people during a
training phase.
• During the interaction, considering its expectations
and its experience, he must be able to quickly
recognize the exhibited social signs (S1
component).
• If required, the robot must be able to provide an
explanatory account of some sort of this process of
interpretation (S2 Component).

12. The S1 System
• Deep networks can effectively be used for
the processing and classification of
sequence of data
• Long Short Term Memory
– avoid the long-term dependency problem
– a more complex cell structure
• Cell structure

13. The Proposed Network Architecture

14. The S1 System
• We have chosen to gradually stack LSTM layers and
measure the trend of the F1-score to determine what
the correct number of layers can be.
• Each LSTM layer is separated from the next one by
a Rectified Linear Unit function.
• Given a sequence length, we attempted to determine
how many neurons are needed for the
representation to be of good quality.

15. The S1 System
• Number of neurons in the LSTM layers
– set to 64, 128 or 256;
• Considered stacked LSTM levels
– one, two or three
• sliding window
– from 2 to 20
• The training has been performed for 10 epochs.

16. The S1 System
• A dataset of 20 different actions has been
used used to train the network (subset of
the Vicon Physical Action dataset)
• The actions of the dataset have been
divided in
– “normal” behavior
(Bowing, Clapping and Handshaking)
– “not friendly” behavior
(Punching, Slapping and Frontkicking)

17. The S2 System
• The main perceptual differences between different
classes of gestures (e.g. aggressive vs not aggressive
ones) are represented through an explicit ontological
model (available at: http://www.di.unito.it/~lieto/
ExpActOnto.html)
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18. The S2 System
• Example of ontological features considered to distinguish
among these two classes of gestures are: velocity of the
gesture execution, distance of the final gesture position
from the body etc.
• In other words: we tried to provide an explanatory account of
the output of the opaque S1 component by using an apriori
ontological model of a given situation
• The S2 component allows also to model the differences
between gestures. These models can be used to describe why
a particular sign, e.g. categorized as ’aggressive’, has been
additionally recognized, for example, as a “Punching” Action.

19. Ex. Provided Explanation for the Detected
“Punching” Action
“Punching Action” is characterized by the fact of being an action executed
at a certain velocity (X), categorized as ’High Velocity’, and at a certain
distance (Y) from the Body, categorized as ’Close Distance’ according to the
ontology.
In addition to these traits, common to all the “Aggressive Actions”, the
“Punching” action is also characterized by the fact of being executed with
“Close Hands”. 19

20. Ex. Provided Explanation: “why” punching and
not slapping
The S2 additional model-based explanation about why the previous
’Punching’ cannot be classified, for example, as a ’Slapping’ (both are
’Aggressive Actions’).
Also in this case the fact that the detected body part executing the gesture
is a ’Close Hand’ and not a ’Open Hand’ (as in the case of ’Slapping’)
represent a crucial element for explaining that categorization decision. 20

21. Upshot and Future Work
We sketched a preliminary account of a dual process based framework
able to provide a partial explanation of the reasons driving a robotic
system to some decisions in task of gesture recognition is a social
scenario.
As a future work we plan to evaluate in detail the feasibility of the
proposed framework with a Pepper robot interacting in a real environment.
We want to extend the level of detail of the possible explanation provided
by such framework by considering more complex scenarios and a
multimodal interaction involving both visual and linguistic elements.
Finally, we plan to provide a tighter integration of the two software
components that, currently, operate in a relatively independent way.
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