immunity in self-aware systems
DESCRIPTION
by Jon TimmisTRANSCRIPT
Immunity in self-aware systems
Professor Jon TimmisDepartment of Computer Science
Department of ElectronicsYork Centre for Complex Systems Analysis
ImmunityPlants and animals have immune systems to help protect and repair
Images: Mark Coles
Swarming, FlockingApparent co-ordination of many individuals - no central control
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Collective BehaviourCollectives work together to solve problems that one (or a few) can not
manage on their own
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Co-ordination
Fire flies appear to synchronise across a swarm
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“Swarm” immunity?immune cells acting together in an individual?
Survival as a collective?
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An engineering perspectiveBuilding systems that operate for long periods of time without human
intervention is a challenge
Big dog
A swarm of robots
Building systems that can cooperate to perform tasks an individual can not do is also a challenge
boston dynamics; swarmanoid
So reality is different to sci-fi!
An immune system for a robot?
Sense and react to events external to the robot
Sense and react to events internal to the robot and swarm
Sense and react to internal and external events in an organism
An immune system sensing and reacting to the environment
We live in hazardous environments. T cells are one line of defence.
There are many hazardous environment we might like to monitor.
Sniffer dog robot with an immune system for sensing and reaction
The sniffing robot
An immune system for a “swarm” of robots?
Simple tasks ...
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Foraging• Define behaviour as a FSM
shaped environment with object placed randomly within the
arena. Each robot is programmed with behaviour-based sub-
sumption architecture [1]. With this architecture, behaviour
modules for a foraging task include obstacles avoidance,
moving to object, grab object, move to base, deposit, scan
arena and random walk. These behaviours can be expressed
with a finite state machine and is given in Fig. 4. A simulation
starts with robots dispatched from the base to explore within
the arena to find, collect, and deposit attractors continuously
until end of simulation or until all energy used. Initial energy is
set to 15000 unit and energy consumption for each action per
second is as follow: avoidance 0.9, randomWalk 0.8, moveTo-
Food 0.8, grabObject 1.2, scanArena 0.8, depositObject 1.2
and moveToBase 0.8 unit.
grabObject moveToObject
depositObject
avoidance scanArenamoveToBase
leaveBase randomWalk
ObjectInGripper
Gra
bS
uccess
AtB
ase
ClearClear
ObstacleDetected ObstacleDetected
Done InArena
ObstacleDetected
Obje
ctI
nS
igh
t Ob
jectL
ost
Obje
ctI
nS
ight
ObstacleDetected ObstacleDetected
Fig. 4. Finite state machine for behaviours of a robot in a foraging task.
B. Experiments Settings
Fixed Parameters for Simulation. Table I is the settings
for fixed parameters in this study. The values for these parame-
ters are set purely by arbitrary choice and can be changed if de-
sired for other studies. Estimated time for a robot to collect and
deposit one object τ̂ � (arenawidth+arenaheight)/2motorspeed � 10
0.15 s.
To collect all 3000 object by 15 normal robots in optimal
case, an estimatedtotalobjecttotalrobot x τ̂ �13333.33 is required but
for completeness this number is rounded up to 13500 giving
the simulation duration. For our current work, we only study
anomaly detection for single robot failure but future work will
consider failures on multiple robots.
TABLE I
FIXED PARAMETERS FOR ALL SIMULATIONS.
Parameter Value Parameter value
Arena size 12 m x 12 m Robot normal speed 0.15 m/s
No. robot 10 unit Component fault Motor (Wheels)
Static OPR 0.15 unit/s Simulation duration 13500s
Max. object 400 unit Fault injection time 5000s
Initial object 100 unit Sampling period 250s
Total object 3000 unit No. faulty robot 1 unit
Variable Parameters for Simulation. We are interested in
detecting anomalies caused by component faults in the robot
under various environmental conditions. These conditions are
time-varying and thus the behaviour of the SRS changes
accordingly, i.e., normality changes. We are interested in
detecting anomalies under these conditions and analyse the
effects it has on the detection accuracy. Table II shows the
time-varying environmental conditions, the types and magni-
tudes of the component faults.
TABLE II
VARIABLE PARAMETERS FOR VARIOUS SIMULATION SCENARIOS.
Parameter Value
Environment static, varying object distribution VOD ,
varying object placement rate VOPR, vary-
ing presence of obstacles VPO
Fault type complete Pcp, partial Ppt, gradual Pgr
Fault magnitude partial 0.045m/s to 0.125m/s,
gradual -0.00005m/s to -0.001m/s
In this paper, we analyse the effect of these environmental
conditions against the detection accuracy of RDA utilising
relative normality (rnRDA). In addition, we also look at the
sensitivity of the detection against different magnitudes of
failures.
• Env.VOPR:. Effect of time-varying object placement rate
(availability of object) on the detection accuracy.
• Env.VOD:. Effect of time-varying object distribution (lo-
cation of object) on the detection accuracy.
• Env.VPO:. Effect of time-varying presence of obstacles
on the detection accuracy.
• F.Ppt:. Detection accuracy with different magnitudes of
partial failure.
• F.Pgr:. Detection accuracy with different magnitudes of
gradual failure.
Parameters of rnRDA. In this study, the input data to the
rnRDA is the vector of 3 variables <OBJ, ENG, DIST>
where OBJ={obj1,obj1, ...,objm} is the number of object
collected, ENG={eng1,eng1, ...,engm} is energy usage, whilst
DIST={dist1,dist1, ...,distm} is distance travelled within 250s
and m is the number of neighbor at particular time. For
each variable, 20 equally spaced receptors scaled from 0 to
maximum expected value are used. Total stimulation for each
receptor in Eq. 1 is calculated using Gaussian kernel with
kernel width h[obj]=1, h[eng]=8, and h[dist]=2. For other
parameters, β=0.01, b=0.1, gb=1.1, and α=1. As in Eq.5,
anomaly is detected when receptor position rtp(x) ≥ l for
any variable.
IV. DISCUSSION ON RESULTS
Performance evaluation. The performance of the detection
algorithm is evaluated based on true positive rate TP , false
positive rate FP , true negative rate TN and false negative rate
FN . Accuracy of detection is represented by
Accuracy =(TP + TN)− (FP + FN)
TP + TN
Simula;on of foraging
State diagram for robot behaviour
A bit more complicatedKeeping a swarm of robots together
State diagram to control a robot
Things can go wrongMaybe not as robust as we first thought?
Collective repairThe immune system has many responsesWhich we can develop computer models ofWhich we can exploit in swarm robots
An individual immune system that monitors everyone else as well
Swarm to Organism
SYMBRION
Organisms
Self-Assembling “Swarm” failure Self-Assembling
“Organism” failureRecovery after assembly
What about underwater?
Creating healthy robots and swarms
Made some initial advances, but we are a long way from really reliable robots and swarms that can operate autonomously
Make use of insights from immunology to help drive our technology
Can a robot have an immune system?Yes, but not the same as you and I !!
Acknowledgements
• University of York, Royal Society, European Commission, EPSRC, BBSRC, MRC, Dstl
• Kieran Alden, Paul Andrews, Iain Bate, Lynette Beattie, Mark Coles, Richard Greaves, James Hilder, Pete Hickey, Rita Ismail, Paul Kaye, Vipin Kumar, Kelvin Lau, Tiong Lim, Alan Millard, Lachlan Murray, Daniel Moyo, Mark Neal, Jenny Owen, Nick Owens, Fiona Polack, Mark Read, Susan Stepney, Andy Tyrrell, Alan Winfield, Richard Williams, Eva Quarnstrom
A bit of fun