1. Social Network Models for the TDR System YingJie Tang, HaoRan Zhang, ZhiJian Liang and Shi-Kuo Chang Department of Computer Science University of Pittsburgh

2. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

3. TDR System
Ren
Human
Tian
Heaven Di
Earth

4. TDR System

5. An experimental TDR system
The TDR system consists of multi-level super-components each with different computation cycles specified by an abstract machine model. The TDR system has three major super-components: Tian (Heaven), Di (Earth) and Ren (Human), which are the essential ingredients of a human-centric psycho-physical system following the Chinese philosophy. This experimental TDR system provides a platform for exploring novel visual languages and interaction patterns among different super-components in personal health care, emergency management and social networking.

8. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

9. Slow Intelligence System
The slow intelligence system is an approach to design environment aware systems. The general characteristics of a slow intelligence system include enumeration, propagation, adaptation, elimination, concentration and multiple decision cycles. In our previous work, an experimental test bed was implemented that allows designers to specify interacting components and super-components for slow intelligence systems.

10. Multi-Level Slow Intelligence System

11. Interaction among Components

12. Interaction among Components

13. Patterns in Slow Intelligence System

14. Patterns in Slow Intelligence System

15. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

16. An abstract machine Model
An abstract machine for slow intelligence systems:
Msis = { P, S, Po, cycle1, cycle2, …, cyclen}
The problem space P is a nonempty, enumerable set of problem elements p1, p2, …, pm. A problem set Pk is a finite subset of P. The solution space S is a nonempty subset of the problem space P. A solution set S is a finite subset of S.
Starting from an initial problem set Po, the objective of the abstract machine is to derive a problem set Pj that is also a solution set, by applying one or more of the computation cycles cycle1, cycle2, …, cyclen.

17. An abstract machine Model
A computation cycle is a sequence of slow intelligence operators to transform problem sets. Two elementary slow intelligence operators:
Enumerator: P1 -enum< P2 is the enumerator that takes each problem element of P1 to generate a number of new elements, which are put into P2. Therefore the cardinality of p2 is usually larger than that of P1.
Eliminator: P1 >elim- P2 is the eliminator that eliminates non-solution elements of P1 and put the rest into P2. Therefore the cardinality of P2 is usually smaller than that of P1.

18. An abstract machine Model
cycle1: [guard1,2] Po -enum< P1 >elim- P2
A guard for cyclei is a predicate of the form guardi,j defined on problem sets and evaluated whenever a problem set is generated. If the predicate is evaluated to be true then control is transferred to cyclej. If the predicate is evaluated to be false then Msis remains in the current cycle. If this is the last problem set then the machine halts.
The default guard is “problem set is empty”.

19. Puzzle Solving Example

20. Short Computation Cycle
Short computation cycle is:
cycle1: P0 -enum< P1 >elim- P2

21. Another Puzzle Solving Example

22. Long Computation Cycle

23. Long Computation Cycle
Long Computation Cycle is:
cycle2: P0 -enum< P3 -enum< P4 -enum< P5 >elim- P6
Result is:
P6= {<123,804,765>}

24. An Exhaustive Search Machine
cycle1: [guard1,1] P0 -enum< P1 >elim- P2
A slow intelligence exhaustic search abstract machine can always be constructed to find a solution, but it may not terminate.

25. More Slow Intelligence Operators
Concentrator: P1 >conc= P2 is the concentrator that selects some problem elements of P1, which are put into P2.
Adaptor: P1 +adap= P2 is the adaptor that inputs information from the environment and modifies elements of P1 to produce P2 according to the adaptation rule.
Propagator: P1 =prop+ P2 is the propagator that outputs information to the environment and modifies elements of P1 to produce P2 according to the propagation rule. In the simplest case P2 is identical to P1.

26. An Adapt-to-Environment Example
A machine that constantly adapts to the environment can be defined as follows:
cycle1: [guard1,1] P0 +adap= P1
-enum< P2 >elim- P3
An adapt-to-environment abstract machine can always be constructed.
Other machines for divide-and-conquer, follow-the-leader can also be constructed.

27. Discussion
A theoretical foundation is provided for the slow intelligence approach by introducing the abstract machine model with multiple computation cycles.
From practical standpoint, the computation cycle is controlled by the controller super-component .

28. Discussion 1
Each time a super component is added, a controller is created to control the timings of this computation cycle.
Thus the SIS systems supports multiple computation cycles controlled by multiple controllers.
Other types of super components (SI patterns) can also be specified.

29. Discussion 2
Fast Cycle﹕Super components are not involved in a computation cycle (intuitive﹑involuntary)
Slow Cycle﹕Super components are involved in a computation cycle (deliberate﹑voluntary)
Environment variables (concept, time and space) and system variables﹕
conceptual domain such as high blood pressure, spatial domain such as home, public area, temporal domain such as morning, after lunch, etc.
Cycle switching rules are defined based upon environment variables and system variables.

30. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

31. TDR System

32. Tian Super-Component

33. Parrot Plant Sensor

34. Three Layers Tian Super-Component
Top Layer: Tian coordinator
Middle Layer: Tian1/Tian2 controllers
Bottom Layer: Flower1/Flower2 monitors, GUI1/GUI2, Uploader1/Uploader2 advertisers

35. Multi-Level Computation Cycles
The Tian super-component has computation Cycle1:
Cycle1 [guard1,2]: P10 +adapA1= P11 -enum< P12
>elim- P13 =propA2+ P14
Likewise, the Di super-component has computation Cycle2:
Cycle2 [guard2,3]: P20 +adapA2= P21 -enum< P22
>elim- P23 =propA3+ P24
Finally, the Ren super-component has computation Cycle3:
Cycle3 [guard3,1]: P30 +adapA3= P31 -enum< P32
>elim- P33 =propA1+ P34
Notice the three computation cycles together form a higher-level computation cycle Cycle4:
Cycle4 [guard4,4]: P34 +adapA4= P41 -enum< P42
>elim- P43 =propA1+ P10

36. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

37. The Alien’s Visual Language from the 2016 sci-fi movie Arrival

38. The Alien’s Visual Language from the 2016 sci-fi movie Arrival
This Visual Language is: holistic, dynamic, independent of speech, and time-circular

39. Dashboard on PC screen

40. Detailed data for components

41. Subjective data from social network

42. Gaze data from smart phone

43. Gaze data from smart phone

44. Data with fuzzy membership value

45. Components may change color due to changes in data value

46. Super-component may change color due to components’ color change

47. Dashboard and data on smart phone

48. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

49. Student/Teacher Social Network Model

50. Student/Teacher Social Network Model

51. Scenario on how Social Network works in TDR model
1. The SocialNetwork Sensor gathers information of Chi
attributes (tongue, fatigue, weakBreath, pulse, sweaty) as well
as the id and originator and send it to SocialNetwork Monitor.
2. SocialNetwork Monitor receives this message and calculates
the total-Chi attribute using the same algorithm as from Chi
attribute.
3. SocialNetwork Monitor sends the six attributes to SocialNetwork Advertiser.
4. SocialNetwork receives this message and passes it to the
SocialNetwork Model.
5. SocialNetwork Model gets the most recent value of the
total-Chi attribute from Chronobot Database and compares it
with the total-Chi from this message to evaluate the
correctness of this record.
6. The SocialNetwork model adjusts the weights of each
profile in the model and uploads the Chi attributes to Database.

52. Experimental Results for Student/Teacher Social Network Model
Six users learn from one teacher

53. Outline
Multi-Level Slow Intelligence System
An Abstract Machine Model
TDR System Architecture
Web GUI
Student/Teacher Social Network Model
Discussion

54. Sentient Systems
A Sentient System is a multiple-level slow intelligence system.
TDR system provides an experimental platform for exploring different visual languages and interaction patterns among super-components for personal health care, emergency management and social networking.

55. Further Research Topics
Patterns for monitors, advertisers, controllers and coordinators
Patterns for Tian, Di and Ren
Harmony of slow cycles, fast cycles
A computation theory for slow intelligence
Visual languages for slow intelligence

56. Q&A