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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
Self-Awareness All participants of the Slides Factory Application - - PowerPoint PPT Presentation
Self-Awareness All participants of the Slides Factory Application - - PowerPoint PPT Presentation
Applications of and Challenges in Self-Awareness All participants of the Slides Factory Application 1: SwarmRobotics Imagine a swarm of robots that need to solve a certain task, e.g. Cleaning a devastated area Exploring Mars In
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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)
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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, …)?
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Example project – SYMBRION (2)
Scenario movie http://www.youtube.com/watch?v=SkvpEfAPXn4
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Example project – SYMBRION (3)
Approach
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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
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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
- n 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
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Example project – ASCENS (2)
Approach
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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
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Example project – CoCoRo (2)
Scenario movie http://www.youtube.com/watch?v=OStLml7pHZY
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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
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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
- pportunities, but
requires an appropriate management
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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
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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
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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
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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?
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What is needed?
- In both cases, for networks’ self
management/organization we need:
– Mechanisms, which can enable the network to act
- n itself
– Policies or goals, which leads the networks in taking decisions
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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
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Application 3: Data management
- More and more content is being generated
- Content needs to be effectively managed in
- rder to avoid user form being swamped
- Task is to:
– Manage existing content – Acquire new content
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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
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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
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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
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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
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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
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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…
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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
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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
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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
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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
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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)
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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
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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.
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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.
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References
- Sapere