1. The ORESTEIA Attentional Agent Stathis Kasderidis Department of Mathematics, King’s College, Strand, London WC2R 2LS, UK

2. CONTENTS Requirements Agent structure Agent function Attention Control State Evaluation Rules Computational model Artefacts Example Runtime

3. Requirements Scalability (+ combinatorial explosion) Advanced self-management and robustness mechanisms (graceful degradation of performance) Support for emergent behaviour and ad- hoc configurations Adaptation to the user Transparent access to resources and common monitoring strategies

4. Agent structure Distributed entity with four layers:  L1: Sensors  L2: Pre-processing  L3: Local decision  L4: Global decision

5. Agent function Four main sub-systems:  Attention Control  Local (Sensor monitoring, Detection of irregular behaviour)  Global (Competition)  State Evaluation  Includes learning  Decision-making (Rules)  Computational Model

6. Attention Control

7. State Evaluation

8. Attention Control and State Evaluation

9. Rules

10. Computational Model

11. Artefacts: L3 The Level 3 Architecture:

12. Artefacts: L4 The Level 4 Architecture:

13. Effective Flow

14. Example: Driving and Hazard Avoidance LEVEL 1: Class A: Physiological Sensors. 1. Skin Temperature 2. Heart Rate. (Substituted or Augmented by a full ECG sensor). 3. Respiration sensor. Considered for future inclusion. 4. Galvanic Skin Response. Considered for future inclusion. Class B: Environmental Sensors. 1. Temperature. 2. Visibility. 3. Humidity. Considered for future inclusion. 4. Ambient Light. Considered for future inclusion.

15. Example II Class C: Car Sensors. 1. Speed vector sensor. 2. Acceleration vector sensor. 3. Breaking Force vector sensor. 4. Thrust Force vector sensor. 5. Friction vector sensor. 6. Steering wheel angle sensor. 7. Friction coefficient sensor. 8. Heading vector sensor. Class D: Proximity Sensors. 1. ‘Object’ position vector sensor. 2. ‘Object’ speed vector sensor. 3. Number of ‘Objects’ in distance R (either radius or ahead/behind cone).

16. Example III LEVEL 2: Same number as participating sensors. 1-1 relationship. LEVEL 3: Class E: Biometric Artefact Class F: Environmental Status Artefact Class G: Car State Artefact. Class H: Threats Artefact. LEVEL 4: Class I: Hazard Avoidance.

17. Runtime Step.1

18. Runtime Step.2

19. Runtime Step.3

20. Runtime Step.4

21. Runtime Step.5: Evaluate

22. Runtime Step.6: Attention

23. Runtime Step.7: ACT

24. Runtime Step 8: Summary Biometric [Health Monitor]  State: HR, “Takens” embedding  Monitor: D=||State-Predicted|| (+HT)  Classifier: Hstatus {OK, ASeek, Dang}  ATTNInd=1-Prob(D>T)  ATTN: Priority Queue, Dispatch: First  Observer: NN, 3-2-1,  Rules: “Notify physician/Warn”  Handler: “Change sampling rate”

25. Runtime Step.8: Summary Car computer [Proximity Monitor]  State: P,V,A,T,B,H Cartesian  Monitor: D=||State-Predicted|| 2 (+HT)  Classifier: Pstatus {Rep,Warn,Dang}  ATTInd=exp(-CollTime/CharTime)  ATTN: Priority Queue, Dispatch: First  Observer: Linear interpolation (2)  Rules: “Warn driver”  Handler: “Take control if limit passed”

26. Runtime Step.8: Summary Personal Assistant [Hazard Avoidance, Drive Fast]  State: {Bstatus, Pstatus} Cartesian  Monitor: History Trace  Classifier: Status {OK,Aseek, Dang, Coll1,Coll2,Dang+Coll1,Dang+Coll2}  ATTNInd=(wHA+ABIO+APRX)/3  Rules: NOP  Handler: “Take control of the car and stop”

27. Runtime Step.9: Miscellaneous Competition / Cooperation 2-D execution space: {Computational, Action}  Computational: Time sharing  Action: Exclusive access / Sharing OBSERVER Goal Further requirements from learning Further refinement of goal categories Executive

28. Information Flow