1. ´ ´
WHITE BOX ADAPTATION
-- Roberto Bruni, UNIPI
-- Andrea Corradini, UNIPI
-- Fabio Gadducci, UNIPI
-- Alberto Lluch Lafuente, IMT
-- Andrea Vandin, IMT

3. What is
ADAPTATION?
Andrea Vandin Fabio Gadducci Andrea Corradini Roberto Bruni
IMT Lucca University of Pisa University of Pisa University of Pisa

4. OUTLINE
1 White box adaptation
2 Russian dolls & robots
3 Adaptable transition systems

5. OUTLINE
1 A conceptual framework for adaptation [FASE 2012]
2 Modelling and Analyzing Adaptive Self-assembly
Strategies with Maude [WRLA 2012]
3 Adaptable Transition Systems [WADT 2012]

6. OUTLINE
1 White box adaptation
2 Russian dolls & robots
3 Adaptable transition systems

7. What is
Adaptation?

8. Lotfi A. Zadeh,
Proc. IEEE, 1963

9. “... a software system self-adaptive if
modifies its own behavior in response to ...”
[*] Oreizy, P., Gorlick, M.M., Taylor, R.N., Heimbigner, D., Johnson, G., Medvidovic, N.,
Quilici, A., Rosenblum, D.S., Wolf, A.L.: An architecture-based approach to self-
adaptive software. Intelligent Systems and their Applications 14(3), 54–62 (1999)
[*] Robertson, P., Shrobe, H.E., Laddaga, R.: Introduction to self-adaptive software:
Applications. In: Robertson, P., Shrobe, H.E., Laddaga, R. (eds.) IWSAS. LNCS, vol.
1936, pp. 1–5. Springer (2001)
[*] Self-Adaptive Software: Landscape and Research Challenges, Mazeiar Salehie,
Ladan Tahvildari, ACM Transactions on Autonomous and Adaptive Systems 4, 2.

10. IS THIS ADAPTATION?
...
if p(x) then
do_this;
else
do_that;
endif
...

11. IS THIS ADAPTATION?
“if the hill is too steep then
assemble with other robots and try again”

12. is obstacle avoidance adaptation?
“Obstacle avoidance may count as adaptive
behaviour if […] obstacles appear rarely. [...]
If the ‘normal’ environment is […] obstacle-rich,
then avoidance becomes [...] “normal” behaviour
rather than an adaptation.”
- Ezequiel di Paolo, course on “Adaptive Systems”,
University of Sussex, Spring 2006.

13. BLACK-BOX ADAPTATION

14. White box
adaptation

15. WHAT IS A PROGRAM?
“program = control + data”
- Niklaus Wirth, “Algorithms + Data Structures = Programs”,
Prentice-Hall (1976)

16. WHAT IS AN ADAPTABLE PROGRAM?
“adaptable program =
control + data

17. WHAT IS AN ADAPTABLE PROGRAM?
“adaptable program =
control + data
control data + the rest ”

21. WHAT IS AN ADAPTABLE PROGRAM?
CONTROL DATA OTHER DATA
CONTROL
“adaptation is the run-time
modification of control data”

22. AUTONOMIC MANAGER
Analyze Plan
Monitor Knowledge Execute
EFFECTOR
MANAGED ELEMENT
CONTROL
DATA

23. A PROGRAM IS...
“...adaptable if it has a distinguished collection of CD
that can be modified at runtime.”
“...adaptive if its control data is modified at runtime
for some computation.”
“...self-adaptive if it modifies its own control data at
runtime.”

24. EXAMPLES OF CONTROL DATA
plans
aspects
rules
contexts
configuration policies
variables programs
CONTROL DATA

25. “an excellent discussion of
the precise meaning of the
term adaptive software.”
- Carlo Ghezzi, Evolution, Adaptation, and the
Quest for Incrementality, LNCS 7539/2012,

26. 1 White box adaptation
2 Russian dolls & robots
3 Adaptable transition systems

27. SELF-ASSEMBLING ROBOTS
“if the hill is too steep then
assemble with other robots and try again”

28. - J. Meseguer et al., “Semantic models for distributed object reflection”,
ECOOP 2002, LNCS 2374. Springer, 1–36.

29. RRDs IN A NUTSHELL
Nested configurations
of interacting objects
Reflection (meta-programming)

30. REFLECTIVE COMPONENTS
manager
state rules state rules
interpreter

31. SELF-AWARENESS
“self-awareness means manager
that the system is aware of
its self states
and behaviors. ” state rules
- M.G. Hinchey, et al, “Self-managing software”.
IEEE Computer 39(2) 107–109, 2006.
interpreter
(white-box component)

32. Specification in Maude
Statistical Model Checking
1
0.8
0.6
0.4
0.2
0
0 3 6 9

33. % of robots crossing the hole
0.9
0.8
0.7
0.6
0.5
0.4
0.3 any shape
0.2
anyshape, no double-grip
line
0.1
quasi-orthogonal line
0
0 3 6 9
number of robots in the scenario

34. 1 White box adaptation
2 Russian dolls & robots
3 Adaptable transition systems

35. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

36. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
Is it
task? self task?
v e?
U{}[] U{}[t] U{}[tt]
a da pti
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

37. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

38. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

39. task? task?
D{}[] D{}[t] D{}[tt]
ffer ent
di
down! up! up!
,140
= 4 U{}[t] task?ions!
B 8 task?
U{}[] at U{}[tt]
ret schedule!
int erp
schedule!
finish! finish!
U{t}[] U{t}[t]
task?

40. Adaptable
Interface
automata

42. IA = Automata + Interface (i/o distinction)
task? task?
down! up! up!
task? task?
schedule! schedule!
finish! finish!
task

43. IA = Automata + Interface (i/o distinction)
task? task?
down! up! up!
task? task?
schedule! schedule!
finish! finish!
task?

44. task? task?
down! up! up!
task? task?
schedule! schedule!
finish! finish!
task?
AIA = IA + control propositions (colors)

45. task? task?
down! up! up!
task? task?
schedule! schedule!
finish! finish!
task?
AIA = IA + control propositions (colors)

46. Now what?

47. AUTONOMIC MANAGER
Analyze Plan task? task?
D{}[] D{}[t] D{}[tt]
Knowledge Execute down! up! up!
Monitor
task? task?
U{}[] U{}[t] U{}[tt]
MANAGED COMPONENT schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

48. some behavioural relation

49. i.e. normal output actions become inputs
i.e. M “listens” outputs of B

50. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish? finish!
U{t}[] U{t}[t]
task?

51. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule? schedule?
finish? finish?
U{t}[] U{t}[t]
task?

52. i.e. control output actions become inputs
i.e. B “obeys” outputs of M

53. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

54. task? task?
D{}[] D{}[t] D{}[tt]
down? up? up?
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

55. ISO
(for deterministic AIA)

56. Beyond
isomorhism

57. i.e. control output actions become inputs
i.e. B “obeys” outputs of M
i.e. collapse transitions labelled with B
i.e. M “ignores” B-moves

58. task? task?
D{}[] D{}[t] D{}[tt]
down! up! up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule? schedule?
finish? finish?
U{t}[] U{t}[t]
task?

59. task? task?
D{}[] D{}[t] D{}[tt]
task? task?
U{}[] U{}[t] U{}[tt]
finish? finish?

61. alternating simulation

62. (for deterministic IA)

63. task? task?
D{}[] D{}[t] D{}[tt]
down? up? up!
task? task?
U{}[] U{}[t] U{}[tt]
schedule! schedule!
finish! finish!
U{t}[] U{t}[t]
task?

64. 1 White box adaptation
2 Russian dolls & robots
3 Adaptable transition systems

67. 1 Keep working on what we saw
2 Study cina's adaptation
3 Apply our ideas to scel