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 difficult environments with holes, hills, obstacles, . . . the robots have to cooperate – Transport an object together – Form organisms to cope better with environment
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)
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, …)?
Example project – SYMBRION (2) Scenario movie http://www.youtube.com/watch?v=SkvpEfAPXn4
Example project – SYMBRION (3) Approach
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
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
Example project – ASCENS (2) Approach
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
Example project – CoCoRo (2) Scenario movie http://www.youtube.com/watch?v=OStLml7pHZY
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
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
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
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
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
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?
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
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
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
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
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
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
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
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
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…
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
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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