Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.

1. Applications of and Challenges in
Self-Awareness
All participants of the Slides Factory

2. Application 1: SwarmRobotics
• Imagine a swarm of robots
that need to solve a certain
task, e.g.
– Cleaning a devastated area
– Exploring Mars
• In difficult environments with
holes, hills, obstacles, . . . the
robots have to cooperate
– Transport an object together
– Form organisms to cope better
with environment

3. Application 1: SwarmRobotics
• Robots are aware of the task they are
supposed to perform and monitor their
performance in the environment
• Robots should be able to adapt to maximize
their performance
• Adaptations take place on an individual level
as well as on a collective level:
– Individuals adjust their behavior
– Collective behavior emerges (e.g. organisms are
formed by multiple robots)

4. Example project – SYMBRION (1)
Symbiotic Evolutionary Robot Organisms
• Hundreds of small cubic robots are built and deployed in an
environment
• Robots sense each other and the environment and are capable of
aggregating into “multi-cellular” organisms
• Aggregation and disaggregation is self-driven, depending on the
circumstances: different environments, different tasks
• Questions addressed:
– Can we build such robots and program the basic behaviors needed for
appropriate (dis)aggregation?
– Can we provide adaptive mechanisms that enable newly “born” organisms
learn to operate (sense, move, act, …)?

5. Example project – SYMBRION (2)
Scenario movie
http://www.youtube.com/watch?v=SkvpEfAPXn4

6. Example project – SYMBRION (3)
Approach

7. Example project – SYMBRION (4)
Current Results
• Different controllers have been developed for robots
• Evolutionary approaches are able to adapt the controllers
based upon fitness
• Different organisms are formed as required by the
environment
• Some initial versions of hardware have been developed and
are currently being deployed

8. Example project – ASCENS (1)
Autonomous service component ensembles
• Self-aware, self-adaptive, and self-expressive autonomous
components
• Components run in an environment and are called ensembles
• Systems are very difficult to develop, deploy, and manage
• Goal of ASCENS:
– Develop an approach that combines traditional SE approaches based
on formal methods with the flexibility of resources promised by
autonomic, adaptive, and self-aware systems
• Case studies:
– Robotics, cloud computing, and energy saving e-mobility

9. Example project – ASCENS (2)
Approach

10. Example project – CoCoRo (1)
Collective Cognitive Robotics
• Aims at creating an autonomous swarm of interacting,
cognitive underwater vehicles
• Tasks to be performed by the swarm:
– Ecological monitoring
– Searching
– Maintaining
– Exploring
– Harvesting resources

11. Example project – CoCoRo (2)
Scenario movie
http://www.youtube.com/watch?v=OStLml7pHZY

12. Example project – CoCoRo (3)
Approach
• Draw inspiration from nature to generate behavior:
– Cognition generating algorithms:
• Social insect trophallaxis
• Social insect communication
• Slime mold
• ANN
– Collective movement:
• Bird movement
• Fish school behavior

13. Application 2: Power networks
• Current power networks rely mainly on big
companies, generating and distributing energy
• The scenario is quickly changing:
– Renewable energy (solar panels, wind turbines, …)
– “Home-made” energy
– Smart devices
• This opens to a lot of
opportunities, but
requires an appropriate
management

14. A new scenario
• People can produce their own energy
• People can sell energy they do not use
– To their neighbors in a peer-to-peer fashion
• Renewable energy impacts positively on the
environment
• Smart devices can help in controlling the
energy consumption and in providing us with
information

15. Renewable
• US Nationwide energy dispatch without (a) and with
(b) renewable contribution
• Source: Brinkman, Denholm, Drury, Margolis, and Mowers, “Toward a
solar- powered grid,” Power and Energy Magazine, IEEE, vol. 9, no. 3, pp.
24–32, 2011

16. The new scenario’s issues
• The new scenario introduces some peculiarities
– The production is “distributed” among a possibly large
number of producers (or “prosumers” if they consume
energy)
– The production is subject to external conditions (e.g.,
weather)
– Smart devices are better than old ones but must be
coordinated
• In general, we have a more dynamic and
unpredictable scenario

17. Power network control
• But how this situation can be controlled?
• A human control
– Is difficult (many parameters, autonomous
entities, …)
– Can be not impartial (big companies are self-
interested)
• Can a power network control itself?

18. What is needed?
• In both cases, for networks’ self
management/organization we need:
– Mechanisms, which can enable the network to act
on itself
– Policies or goals, which leads the networks in
taking decisions

19. Example project - PowerTAC
• Represent each house by means of an agent
• Agents are aware of their current and
expected future energy expenditure
• Agents act based upon this knowledge
• Can either sell or buy energy
• PowerTAC: competition to develop
appropriate mechanisms and agents for selling
and buying energy

20. Application 3: Data management
• More and more content is being generated
• Content needs to be effectively managed in
order to avoid user form being swamped
• Task is to:
– Manage existing content
– Acquire new content

22. Example project - SAPERE
Self-aware Pervasive Service Ecosystems
• Computers for handling data and providing services are
integrated into an “ecosystem”
• System is extended with
– methods for data and situation identification
– decentralized algorithms for spatial self-organization, self-
composition, and self-management
• Thus, we obtain automated deployment and execution of
services and for the management of contextual data items

23. Scenario
• Pervasive computing
– Sensor rich and always connected smart phones
– Sensor networks and information tags
– Localization and activity recognition
– Internet of things and the real‐time Web
• Innovative pervasive services arising
– Situation‐aware adaptation
– Interactive reality
– Pervasive collective intelligence and pervasive participation
• Open co‐production scenario, very dynamic, diverse
needs and diverse services, continuously evolving

24. Architecture
• Open production model
• Smooth data/services
distinction
– live semantic annotations (LSA)
• Interactions
– Sorts of bio‐chemical reactions
among components
– In a spatial substrate
• Eco‐laws
– Rule all interactions
– Discovery + orchestration
seamlessly merged
• Built over a pervasive network
world

25. Infrastructure and applications
• Infrastructure
– A very lightweight infrastructure
– Ruling all interactions (from discovery to data exchange and
synchronization) by embedding the concept of eco‐laws
– To most extent, acting as a recommendation and planning engine
– Possibly inspired by tuple space coordination models
– Yet made it more “fluid” and suitable for a pervasive computing
continuum substrate not a network but a continuum of tuple spaces
• Applications
– The “Ecosystem of Display” as a general and impactfultestbed
– To put at work and demonstrate the SAPERE findings
– Active and dynamic information sharing in urban scenarios
– Active participation of citizens to the working of the urban
infrastructure

26. Example project - RECOGNITION
Relevance and Cognition for Self‐Awareness in a Content‐Centric
Internet
• Project draws inspiration from human cognitive processes to
achieve self-awareness
• Try to replicate core cognitive processes in computer systems:
– e.g. inference, beliefs, similarity, and trust
– embed them in ICT
• Application domain: internet content
– Manage and acquire content in an effective manner by means of
self-aware computing systems

27. Motivation: Technological Trends
• Participatory generation of content
– Prosumers, diversity, expanding edges
– Long tail, swamping, scale!
• Content in the environment
– Linkage of the physical and virtual worlds
– Embedding content and knowledge
• Acquiring knowledge through social mechanisms
– Blogging, social networking, recommendation, RSS
feeds…
• How content reaches users will continue to
change…

28. Self-awareness to support
technological trends
• Intention: Paradigm to support ICT functions
– Enabling content centricity
• Better fitting of users to content and vice-versa
– Synchronize content with human activity and
needs
• Place, time, situation, relevance, context, social search
– Autonomic management
• Of content, its acquisition and resource utilization

29. Approach: Human Awareness
Behaviour
• Capture & exploit key behaviours of the most
intelligent living species
– Human capability is phenomenal in navigating
complex & diverse stimuli
– Filter & suppress information in “noisy” situations
with ambient stimuli
– Extract knowledge in presence of uncertainty
– Exercise rapid value judgment for prioritisation
– Engage a and multi‐scale social context multi
learning

30. Application 4: Cooperative E-Vehicles
• In a few years the e-mobile cars of a big town will be able to communicate
with
• each other and the time tables of the users
• traffic management servers,
• battery loading stations,
• parking lots, etc.
• In such an ensemble, the communicating entities and users may pursue
different goals and plans
– several users may share cars, but have different time tables
– Loading stations have only limited capabilities; so cars may not be able to use
the nearest station for changing the battery

31. Application 4: Cooperative E-Vehicles
• Communication and cooperation between the entities of the ensemble
leads to better Quality of Service w.r.t.
– reliability
• e.g. transport/delivery reliability, adherence to schedules, guarantee to reach
the goal, recharging-in-time assurance
– adaptability to changes
• e.g. traffic flow, daily personal schedule of the driver
– predictability of plans
• confidence in reaching a desired location at a preferred time

32. Application 5: Science Cloud
• consists of a collection of notebooks,
desktops, servers, or virtual machines
– running a cloud platform
/application
– communicating over the Internet
(IP protocol), forming a cloud
– providing data storage and
distributed application execution
• Every participant is
– provider and possible user of
resources
– knowsabout
• itself(properties set by
developers),
• its infrastructure (CPU load,
available memory),and
• other SCPis(acquired through
the network)

33. Application 5: Science Cloud
• The science cloud
– is dynamically changing
• Participants may dynamically join or leave the cloud or just
disappear from the cloud
– is fail-safe
• Continues working if one or several nodes fail
– provides load balancing
• By parallelly executing applications if the load is high, but
not before that.
– aims at energy conservation
• virtual machines are shut down or are taken out of the
configuration if not required

34. Current research questions and
challenges
• Dilemma of wishing to make our designed artefacts autonomous but not too much
(safety).
• To have a metrics to measure properties related to awareness, autonomy.
• We do not know how to engineer self-organization and emergence.
• We do not know how to cope with autonomy and variability. Dilemma of system stability
and reliability incorporating randomness and variability.
• How to design and implement self-aware systems?
• What kind of tools and methodology can we use here?
• Is it ethical to build self-aware systems?
• Can we build autonomic self-aware systems that behave in an ethical way? Related: legally
correct behaviour, behaviour compliant with some set of rules and regulations.
• What makes known natural systems self-aware?
• Describing the scope of the future behaviour of a self-aware system.

35. Current research questions and
challenges
• Predicting the behaviour of autonomic systems and their interactions with the
environment.
• How to ensure safety and security of autonomic self-aware systems? How to differentiate
malicious from benign behaviour?
• What does the system theory of autonomic self-aware systems look like?
• How to build an autonomic self-aware system that would last 100 years?
• To what extent can Big Data be treated as an autonomic self-aware system?
• Can you separate an autonomic self-aware system from its environment?
• In what sense is human and machine self-awareness different? What implications do these
differences have on developing them?
• How can we draw inspiration from human self-awareness for designing machine self-
awareness?
• How to do the second order design needed in autonomic self-aware systems?
• Will autonomic self-aware systems develop their own medical science?
• Goal: build an autonomic self-aware energy production system.
• Goal: build a smart city / computer network / communication network.

36. References
• Sapere
– http://www.sapere-project.eu/
– C. Villalba and F. Zambonelli, "Towards Nature-
Inspired Pervasive Service Ecosystems: Concepts and
Simulation Experiences", Journal of Network
Computers and Applications, vol. 34(2), pp.589-602
– F. Zambonelli, "Pervasive Urban Crowsourcing: Visions
and Challenges", The 7th IEEE Workshop on PervasivE
Learning, Life, and Leisure (PerEl 2011), pp.578-583,
21-25 March 2011