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University of Milan

University of Milan. ORESTEIA -M O DULAR HYB R ID ART E FACT S WI T H ADAPTIV E FUNCT I ON A LITY. http://www.image.ntua.gr/oresteia/. Contents. Executive Summary Problems to be Solved Attention Data Fusion Emergence Architecture and partners' tasks Diagram Module Explanation

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University of Milan

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  1. University of Milan ORESTEIA -MODULAR HYBRID ARTEFACTS WITH ADAPTIVE FUNCTIONALITY http://www.image.ntua.gr/oresteia/

  2. Contents

  3. Executive Summary • Problems to be Solved • Attention • Data Fusion • Emergence • Architecture and partners' tasks • Diagram • Module Explanation • Feature Extraction through Signal Modelling • Methods for State mappings • CAM/SPM • PAC Meditation / Fuzzy Relaxation • Demo 1 • Demo 2 • Data Collection • Laboratory Studies • Car Driving Simulator • Linguistic Rules • MicroPower Generation – Wireless Communication

  4. Executive Summary

  5. Scope/Aims • Create a guidance system for humans, for more efficient and less hazardous living and interacting with their environment, through a set of decision-making facilities embedded in the environment and suitably adapted to the particular user • Investigate enabling technologies for DC in the form of energy harvesting and low power wireless communications

  6. Inputs • Low level sensorial data • Physiological class of sensors • Environmental class of sensors • Other • Symbolic knowledge, a priori available (linguistic rules) • Subsymbolic knowledge, constructed based on numerical data (Input/Output pairs) • Attention-based functionality, inspired from brain operation

  7. Outputs • Decisions • Actions on behalf of the user for: • Managing repetitive and trivial jobs, • Providing indication of abnormal user activity and state, • Providing planning facilities, • Providing information filtering facilities, • Maintaining good user state (physiological, psychological, etc)

  8. Key Properties • Autonomy • Responsiveness • Robustness

  9. Approach • Develop a multi-level attention-based agent architecture adapted to solve decision /guidance problems arising from sensors of various types, some worn by humans, others in devices (such as cars) being used by the humans. The decision/ guidance response of an agent is as to what is the state of the human, given the sensor data, or what is optimal continued use of the device on the basis of joint sensor data arriving at the agent for a given user from all sources • Develop multi-agent systems that handle data available, also, from a set of agents (from interacting users), providing for decision/guidance as to overall optimal (best) use, and ranking of the users as to which needs further analysis

  10. Problems to be solved

  11. Data Fusion Attention • Emergence

  12. Problems to be solved:Attention

  13. I shouldn’t produce these outputs, with these inputs… • Self-evaluation error The input signals have irregular patterns… Artefact • Irregular inputs My battery is low! Self Evaluator • Hardware failure Attention Controller Data History OUTPUTS INPUTS Attention Signal

  14. Problems to be solved:Data Fusion

  15. “WORLD MODEL” Intelligent Artefact Artefact data data data Artefact data Artefact data data data data data data data data data data data data data data data data Artefact data Artefact “Clever Space” You can make use of the two bottom Artefacts Which of these data shall I need? AGENT

  16. Problems to be solved:Emergence

  17. “Clever Space” Artefact Artefact AGENT AGENT AGENT Artefact Artefact Artefact Artefact Artefact Artefact Artefact Artefact Artefact How much are you willing to pay for the services? WHY NOT BE MINE? I need to use these! THESE ARE MINE! I’ll take these! HEY,I SAW THEM FIRST! And I need to use these!

  18. Architecture of a single Agent

  19. Overall View

  20. Level 1: Sensors • Data Content: Classes of signals used by higher levels (Level 2-4) • Data collection (KCL-QUB, ALTEC) • Synthetic data generators (NTUA, UM, KCL) • Sensor autonomy • Efficient energy harvesting (ICSTM) • Communication links • Low power consumption (ICSTM)

  21. Level 2: Preprocessing • Signal preprocessing (NTUA, KCL) • Noise Reduction • Buffering • Transforms • Feature extraction • Which features? (ALTEC, KCL-QUB) • How? (NTUA, KCL) • Modeling signals • Extraction of hidden parameters (UM)

  22. Architecture of a single Agent Level 3: Domain Experts – State Representation

  23. State Mappings • ANN Hourglass • Subsymbolic state representation (UM, NTUA) • Neurofuzzy • Symbolic state representation (NTUA, UM)

  24. Action Module • Stores the ‘response’ of the system. Three levels of sophistication: • No real action. • Simple suggestive actions/messages. • Simple action sequences. • Responsible Partners: KCL, NTUA, UM

  25. Rules Module • Consists of three components: • World Model. Contains all the information needed for forming useful functionalities and maintaining a set of artefacts. • Autonomic. Maintains rules that are necessary for the robust run-time behaviour of the system. • Other. Aids the implementation of alternative (to the ones implemented in the State module) decision-making systems. • Responsible Partners: KCL, NTUA, UM

  26. Goals Module • This module includes three parts: • Values. Is closely associated with the World Model (in the Rules module) to provide default (universal) values for the various thresholds and triggers present in the architecture. • User Profile. Provides specific user deltas (i.e. deviations from the default values defined in the Values part). • Services. Includes a catalogue of services that are offered by the artefact. • Responsible Partners: KCL, NTUA, UM

  27. Monitor Module • Creates an error signal level after comparing the current State with a Historic State. It fulfils two basic requirements: • Universal definition of an error function. Independent of the output of State Module (UM, NTUA, KCL) • Standard definition of an error function. Context sensitive, seamless knowledge of state representation (UM, NTUA, KCL)

  28. Attention Controller • This module is inspired from motor control systems in the brain as well as from engineering control ideas. It operates in two modes: • Feedforward mode. The controller sends a signal, governed directly by the Goal module, to produce a desired response from the action module (KCL) • Feedback mode. Feedback information from the Monitor module is used as a feedback component (KCL)

  29. Level 4: Agent Construction • Combination of conceptual blocks • Agent Formation • Data Fusion • Overall system training • Reinforcement signal production/handling • Attention Control Responsible partner: KCL

  30. Feature Extraction through Signal Modelling

  31. HYBRID TRAINING ÑE(w) = Ñe(y1,..., yT) DYNAMICS Integrated by a fourth order Runge-Kutta method SIGNALS FROM BODY ECG Symbolic health diagnosys

  32. Methods for State Mappings:CAM/SPM Module

  33. Overall View

  34. CAM Module

  35. Scope • The Connectionist Association Module (CAM) provides the system with the ability of grounding the symbolic predicates • Using the CAM, the set of features is associated with the set of evaluated symbolic predicates (partitioning the input space)

  36. Why Neural Network? • Generally the internal state defined by the neural network output is not so simple to be considered as a simple fuzzy partitioning; • Instead the neural network performs the appropriate data clustering to provide the evaluation of the required symbolic predicates based on numerical data

  37. Diastolic Pressure Example

  38. Attention Signal Handling • To which input elements have to be concentrated on?

  39. SPM Module

  40. Scope • It implements a semantically rich reasoning process. It takes as inputs a set of features and gives a set of recognised situations. • It performs the conceptual reasoning process that finally results to the degree of which the output situations are recognised

  41. Why Neurofuzzy? • Fuzzy relational systems represent symbolic knowledge in a formal, numerical framework. • On the other hand, neural networks are typical learning systems that work in a numeric framework.

  42. Rule Insertion • Rules describing situations are based on linguistic terms and are generally of the formIf fuzzy_predicate(1) and fuzzy_predicate(2) then output(3)” • Each rule consists of an antecedent (the if part of the rule) and a consequence (the then part of the rule)

  43. Rule Insertion • The antecedent part of the rule is used to create the weight matrix of the first layer • The consequence part of the rule is used to create the rule matrix of the second layer • The antecedent of all the rules existed is the set of the fuzzy predicates describing the system • The consequence of all the rules is the set of the recognised situations of the system

  44. Rule Insertion (Example) Layer 1 Layer 2

  45. Methods for State Mappings:PAC Meditation / Fuzzy Relaxation

  46. PAC Meditation mapping formula fitness fitness ok? n y formula fuzzy relaxation formula prejudice ok? n y final formula end

  47. PAC Meditation 0-level inner border 0-level outer border 1-level inner border 1-level outer border

  48. SA Algorithm CurrentState := InitialState CurrentTemperature := InitialTemperature Repeat GetTemperature(CoolingSchedule) ProposedState := SelectNeighborState ProposedCost := EvaluateCost(ProposedState) If (Accepted(ProposedState, ProposedCost)) Then CurrentState := ProposedState UntilStoppingRule Return(CurrentState) Fuzzy relaxation 

  49. DEMO 1: Health Monitoring

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