By Alan Winfield

1. Designed by Alan Winfield
Self-Awareness in Autonomic
Systems
Systems with Internal Models

2. Designed by Alan Winfield
Outline
• Why are internal models important?
• What is an internal model?
• Examples from robotics
• A visual abstraction
• The major challenges of internal modes
– The internal representation
– The reality gap
– Connecting the internal model
– Making it work
• Towards a generic architecture for internal models

3. Designed by Alan Winfield
Why do self-aware systems need
internal models?
• Because the self-aware system can run the internal
model and therefore test what-if hypotheses*
– what if I carry out action x..?
– of several possible next actions xi, which should I
choose?
• Because an internal model (of itself) provides the self
in self-aware
*See Dennett’s Tower of ‘generate and test’ in Dennett, D. (1995). Darwin’s Dangerous
Idea, Penguin.

4. Designed by Alan Winfield
What is an internal model?
• It is a mechanism for representing both the
system itself and its current environment
– example: a robot with a simulation of itself and its
currently perceived environment, inside itself
• The mechanism might be centralized (as in the
example above), distributed, or emergent

5. Designed by Alan Winfield
Examples
• Examples of conventional internal models, i.e.
– Analytical or computational models of plant in
classical control systems
– Adaptive connectionist models such as online
learning Artificial Neural Networks (ANNs) within
control systems
– GOFAI symbolic representation systems
• Note that internal models are not a new idea

6. Designed by Alan Winfield
Examples 1
• A robot using self-
simulation to plan a
safe route with
incomplete knowledge
Vaughan, R. T. and Zuluaga, M. (2006). Use your illusion: Sensorimotor self- simulation
allows complex agents to plan with incomplete self-knowledge, in Proceedings of the
International Conference on Simulation of Adaptive Behaviour (SAB), pp. 298–309.

7. Designed by Alan Winfield
Examples 2
• A robot with an internal
model that can learn
how to control itself
Bongard, J., Zykov, V., Lipson, H. (2006) Resilient machines through continuous
self-modeling. Science, 314: 1118-1121.

8. Designed by Alan Winfield
Examples 3
• ECCE-Robot
– A robot with a
complex body uses
an internal model
as a ‘functional
imagination’
Marques, H. and Holland, O. (2009). Architectures for functional imagination,
Neurocomputing 72, 4-6, pp. 743–759.
Diamond, A., Knight, R., Devereux, D. and Holland, O. (2012). Anthropomimetic
robots: Concept, construction and modelling, International Journal of Advanced Robotic Systems 9,
pp. 1–14.

9. Designed by Alan Winfield
Examples 4
• A distributed system in
which each robot has an
internal model of itself
and the whole system
– Robot controllers and the
internal simulator are co-
evolved
O’Dowd P, Winfield A and Studley M (2011), The Distributed Co-Evolution of an
Embodied Simulator and Controller for Swarm Robot Behaviours, in Proc IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2011), San
Francisco, September 2011.

10. Designed by Alan Winfield
A visual abstraction
Maturana, H. R., & Varela, F. J. (1987). The Tree of Knowledge: The Biological Roots of
Human Understanding. Boston, MA: New Science Library/Shambhala Publications.

11. Designed by Alan Winfield
…for a self-aware artificial system
A self-aware
machine
From lecture by Prof Roger Moore: Extending Maturana& Varela’s symbols, FECS, Feb. 2012.

12. Designed by Alan Winfield
Major challenges 1
• To model both the system and its environment
with sufficient fidelity, including:
– The system and its behaviours
– The world and its physics
– The system’s sensorium
– The effect of interactions between the modelled
system and modelled world, on its modelled
sensors
• But what is sufficient fidelity?

13. Designed by Alan Winfield
Major challenges 2
• Example – imagine
placing this Webots
simulation inside
each NAO robot:
Note the simulated robot’s
eye view of it’s world

14. Designed by Alan Winfield
Major challenges 3
• The Reality Gap
– No model can perfectly represent both a system and
its environment. Errors in representation are referred
to as the reality gap.
• The effect of the reality gap will likely be to
reduce the efficacy of the system’s self-awareness
and therefore its ability to accurately model
unexpected events or possible actions
– But this is speculation – it’s an open research question

15. Designed by Alan Winfield
Major challenges 4
• To connect the internal model with the system’s
real sensors and actuators (or equivalent)
– i.e. so that events in the real world, as sensed by the
system, are represented in the model
• To synchronize updating the internal model from
both changing perceptual data, and efferent
actuator data
– i.e. so that the internal model in in-step with the real
system and its environment
• Except when the model is being used to test what-if
hypotheses

16. Designed by Alan Winfield
Major challenges 5
• Making it all work
– Building internal models that
• represent a system and its environment,
• hooking the model up to the system’s perception and
actuation system,
• making use of the model to moderate behaviour (i.e.
for safety), and
• smoothly integrating all data flows to make it all work
– is immensely challenging and remains a difficult
research problem

17. Designed by Alan Winfield
A Generic Architecture
• The major building blocks and their
connections:
Control System
Internal Model
Sense data Actuator demands
The loop of generate and test
The IM moderates action-
selection in the controller
evaluates the consequences of each possible next action
The IM is initialized
to match the current
real situation

18. Designed by Alan Winfield
Conclusions
• Would such a system be self-aware?
– Yes, but only in a minimal way. It might provide
sufficient self-awareness for, i.e. safety in
unknown or unpredictable environments
• But this would have to be demonstrated by the robot
behaving in interesting ways, that were not pre-
programmed, in response to novel situations
• Validating any claims to self-awareness would be very
challenging
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