cdc related research at the dept. of computer science tut jüri vain cdc workshop - tallinn, jan....
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CDC related research at the Dept. of Computer Science TUT
Jüri Vain
CDC Workshop - Tallinn, Jan. 21-22, 2008
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model-based planning in...
model checkingusing...
model-based test generation in...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model-based planning in...
model checkingusing...
model-based test generation in...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
State isomorphism based symmetry reduction
Problem Exploit symmetries to reduce the effects of state
space explosion in explicit state reachability
State isomorphism based symmetry reduction
Solution Given a model in terms of unordered data structures, like
sets and maps, and objects: Construct the state graphs of the states at run
time and Use the computation of graph isomorphism to
determine whether a state with similar structure has been seen.
Results developed with Margus Veanes and Colin Campell and published at FORTE 2007 (June 2007) and in the PhD thesis of J. Ernits (Nov. 2007)
State isomorphism based symmetry reduction
Applications: Method is implemented in model-testing toolkit
called NModel
(by Margus Veanes et al, MS Research Redmond)
There is similar implementation developed independently by Arend Rensink in the GROOVE project
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model-based planning in...
model checkingusing...
model-based test generation in...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
Iterated search refinement with bit- state pruning
Problem Calculate reachability in models that
Run out of resources without producing results with known methods
Produce too long traces to the error/desired state with known methods
Such reachability problems can be used for solving problems related to Scheduling Hardware synthesis Offline test generation
Iterated search refinement with bit- state pruning
Solution Combine two well known methods in a new way:
Iterated Search Refinement, and Bitstate hashing
The key idea is to use collisions in bitstate hash table to randomly filter the search space. Each iteration requires very small amounts of memory A lot of prefixes of paths are covered multiple times, but Provides good results in several examples.
Results published in the PhD thesis of J. Ernits (Nov. 2007)
Iterated search refinement with bit- state pruning
Practical applications 2 well researched case studies:
Synthesis of a memory arbiter for a radar memory case study (J. Ernits, 2005)
Calculating offline test sequences for model-based testing (J. Ernits, A. Kull, K. Raiend, J. Vain, 2006)
The method cannot be used for disproving the reachability, but provides a new alternative for finding a witness trace.
Pre-set test trace generation using MC
Case study: modified INRES protocol
Pre-set test trace generation using MC
Results Time spent for finding
sequences for the 2-switch coverage criterion
Experiments made with Uppaal Cora
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model-based planning in...
model checkingusing...
model-based test generation in...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
Synthesis of test goal directed reactive testers
Problem: Given a
Non-deterministic EFSM MSUT of System Under Test
testing goal in terms of a set Trp of MSUT transitions
Find EFSM MTSR s.t..
MTSR is I/O compliant with MSUT
any trace of MSUT || MTSR includes labels of all transitions in Trp
MTSR chooses a transition from those labelled with current input from MSUT that maximizes some gain function G.
Synthesis of test goal directed reactive testers
Solution: Algorithm of constructing MTSR
Complexity: For all controllable transitions of the MTSR the upper
bound of the complexity of the computations of the gain functions is O(|ESUT|3).
At runtime each choice by the tester takes O(|ESUT|2) arithmetic operations to evaluate the gain functions
(ASE2007, Vain, Raiend, Kull, Ernits)
Experimental results
One Transition Test Purpose
All Transition Test Purpose
Synthesis of test goal directed reactive testers
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model based planning in...
model checkingusing...
model based test generation in...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
Proof techniques for swarm coordination algorithms
Problem: proving properties of swarm (distributed)
coordination algorithms self-stabilization, covergence, ... can be reduced to
reachability analysis reachability analysis collides with scalability barrier
Proof techniques for swarm coordination algorithms (ongoing work)
Some solutions: Pattern based problem encoding provides right level of
abstraction (spec. patterns are problem oriented) General principles of proving:
Compositional approach combining different techniques of component proofs – MC, deduction,...
Global properties by structural induction on the size of swarm/task Base: prove sufficiency of assumptions on a swarm fragment of
some tractable size, e.g., using MC with symmetry reduction, (typically the size of fragment can’t be trivial either)
(see Int. WS on HAM, Vain, Kuusik, Tammet, 2006)
Step: Show that the induction step does not violate the assume part for the new fragment defined by the step
Proof techniques for swarm coordination algorithms
Demo: dynamic cleaning problem different zones of the room deteriorate with different rate a swarm of cleaning robots should keep deterioration below
treshold robots of the swarm communicate trough pherromone trace
Problem: given a treshold CTresh and initial value of the deterioration vector Sdet(0), s.t. Si
det(0) > CTresh for all i, prove that
1) the swarm is always able to reach the state Sdet(t), s.t. Si
det(t) < CTresh for all i,
and
1) for all t’ > t, condition Stdet (t’ ) < CTresh is invariantly true.
Proof techniques for swarm coordination algorithms
Demo
Research lines
(Timed) automatalearning
Proof techniques for swarm coordination algorithms
State isomorphism based symmetry reduction
Iterated search refinement with bit-state pruning
Test trace generation using MC
Synthesis of test goal directed reactive testers
FMs for
model based planning in...
model checkingusing ...
model based test generation in ...
single agent systems
multi-agent systems
state space reduction by abstraction
determinitic systems
non-determi-nistic systems
(Timed) automata learning
Problem: Planner synthesis for a human adaptive Scrub Nurse Robot (SNR) human adaptive = on-line learning from H-H/H-R
interactions learning = constructing/modifying a human motion model
(Timed) automata learning
Context: SNR control architecture
SNR’s “world” model
Nurse’s behaviour model
Surgeon’s behaviour model
Reactive action planningReactive action planning
Targeting and motion controlTargeting and motion
control
Motion recognitionMotion recognition
3D position tracking3D position tracking
Direct manipulator controlDirect manipulator control
Predicted further motions
Hand position data samplings
Fource & position feedback
Control parameters
SNR action to be taken
Recognizedmotion
NN modelof Surgeon’s
motions
Scenario of the surgery
React
ive c
trl.
layer
Collision avoidanceCollision avoidance
Delib
era
tive c
trl.
layer
(Timed) automata learning
Learning algorithm Input:
Time-stamped sequences of observations of phase switching (set of timed traces TrT(Obs))
Parameters for state rescaling operator - distinquishing model observables/controllables - TA
defining quotient state space
Output: Extended (Uppaal version) timed automaton TA s.t.
TrT(TA) = (TrT(Obs)|TA) /~ % bisimilarity of timed traces
(see also Vain, Miyawaki, MIC 2008, Insbruck)
(Timed) automata learning (example)Time Chest
XElbow
XChest
YElbow
YChest
ZElbow
ZAction
1 153 60 158 189 342 151 idle
17 126 127 147 255 356 150 prepare_instr
42 13 562 126 464 341 156 pick_instr
48 27 551 135 465 340 159 hold_wait
70 50.3 402 174 455 338 171 pass
78 152 115 171 258 352 164 withraw
88 153 60.2 158 189 342 151 idle
109 147 84 163 219 360 169 stretch
122 126 109 157 253 359 164 wait_return
139 96 159 151 286 358 157 receive
146 59 591 134 444 367 158 move_back
152 35 549 155 461 367 158 put_on_tray
156 153 60 158 189 342 151 idle
175 14 561 127 463 340 157 pick_instr
182 27 550 134 467 343 161 hold_wait
196 50 401 172 457 337 171 pass
205 128 110 158 253 357 161 wait_return
225 95 160 149 287 356 155 receive
233 56 589 133 445 366 159 move_back
239 34 548 156 460 369 157 put_on_tray
243 153 60 157 189 342 150 idle
Thank You!