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Academic Course: 13 Applications of and Challenges in Self-Awareness


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Academic Course: 13 Applications of and Challenges in Self-Awareness

  1. 1. Applications of and Challenges in Self-Awareness All participants of the Slides Factory
  2. 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. 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. 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. 5. Example project – SYMBRION (2) Scenario movie
  6. 6. Example project – SYMBRION (3) Approach
  7. 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. 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. 9. Example project – ASCENS (2) Approach
  10. 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. 11. Example project – CoCoRo (2) Scenario movie
  12. 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. 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. 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. 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. 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. 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. 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. 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. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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…
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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)
  32. 32. 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
  33. 33. 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.
  34. 34. 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.
  35. 35. References • Sapere – – 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