From Natural to Arti cial Life: Biomimetic Mechanisms in Animat Designs

26 Pages·2010·0.26 MB·English

From Natural to Arti(cid:2)cial Life(cid:3) Biomimetic
Mechanisms in Animat Designs
Jean(cid:2)Arcady Meyer
AnimatLab
Ecole Normale Sup(cid:3)erieure
(cid:4)(cid:5) rue d(cid:6)Ulm
(cid:7)(cid:8)(cid:9)(cid:10)(cid:11) Paris cedex (cid:11)(cid:8)(cid:12) France
E(cid:2)mail(cid:13) meyer(cid:2)wotan(cid:3)ens(cid:3)fr
Abstract
This paper describes several models that incorporate some biomimetic mechanisms into
the control architecture of an animat that has to survive in a changing environment(cid:2) The
adaptive capacities of these mechanisms are compared to engineering solutions(cid:3) in applica(cid:4)
tion domains that involve navigation(cid:3) action selection(cid:3) and evolutionary design(cid:2) It is argued
that(cid:3) although impressive adaptive capacities are already a(cid:5)orded by such biomimetic mech(cid:4)
anisms(cid:3) these capacities are nevertheless limited by the shortcomings of current biological
knowledge(cid:2) It is also argued that these shortcomings could be remedied were biologists will(cid:4)
ing to take advantage of the bottom(cid:4)up and synthetic point of view with which arti(cid:6)cial life
might complement their traditional top(cid:4)down and analytic approach(cid:2)
Keywords
Animats (cid:2) Navigation (cid:2) Action Selection (cid:2) Evolution (cid:2) Development
(cid:0) Introduction
In its endeavor to unravel the mechanisms and processes that sustain natural life(cid:2) traditional
Biology usually seeks to decompose a system into its constituent sub(cid:3)systems and then to
study these sub(cid:3)systems in isolation from one another(cid:2) according to a top(cid:2)down(cid:2) analytical
and reductionist approach(cid:4) In contrast(cid:2) scientists involved in arti(cid:5)cial life research attempt
to synthesize behaviors characteristic of natural living systems with man(cid:3)made artifacts like
computersorrobots(cid:4) Ideally(cid:2) their approachis bottom(cid:3)upandstartswithacollection ofentities
ormodules exhibiting behaviorsthataresimple andwellunderstood(cid:2) organizingthemintomore
complex systems in which internal interactions generate emergent life(cid:3)like properties (cid:6)Langton(cid:2)
(cid:7)(cid:8)(cid:9)(cid:8)(cid:10) Taylor(cid:2) (cid:7)(cid:8)(cid:8)(cid:11)(cid:12)(cid:4)
Research in arti(cid:5)cial life (cid:6)Langton(cid:2) (cid:7)(cid:8)(cid:9)(cid:8)(cid:10) Langton et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:11)(cid:10) Varela and Bourgine(cid:2) (cid:7)(cid:8)(cid:8)(cid:11)(cid:10)
Langton et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:13)(cid:10) Brooks and Maes(cid:2) (cid:7)(cid:8)(cid:8)(cid:14)(cid:10) Moran et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:14)(cid:12) has given rise to a num(cid:3)
ber of applications(cid:2) notably regarding computer viruses(cid:2) autocatalytic nets(cid:2) cellular automata(cid:2)
arti(cid:5)cial nucleotides(cid:2) immune systems(cid:2) market simulations(cid:2) etc(cid:4) In particular(cid:2) it has been
concerned with the conception and construction of animats (cid:3) that is(cid:2) of simulated animals or
autonomous robots (cid:3) whose structures and functionalities are as much inspired from current
biological knowledge aspossible(cid:2) in thehope thatthese animatswill exhibit atleast some ofthe
adaptive capacities of real animals and provecapable ofsurviving in unpredictable and possibly
threatening environments (cid:6)Meyer and Wilson(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:10) Meyer et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:15)(cid:10) Cli(cid:16) et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:13)(cid:10) Maes
et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:17)(cid:12)(cid:4)
(cid:7)
The main problem facing any animat designer is that of (cid:5)guring out what sensors and what
actuatorsshould be connected through whatcontrolarchitecture in ordertosolve whatsurvival
problem in whatenvironment(cid:4) Although theobjectives(cid:2) methods and results ofanimatresearch
have been extensively reviewed elsewhere(cid:2) the corresponding papers have mostly emphasized
theirsimilarities anddi(cid:16)erenceswithrespecttothoseoftraditionalArti(cid:5)cialIntelligence (cid:6)Meyer
and Guillot(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:2)Wilson(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:10) Maes(cid:2) (cid:7)(cid:8)(cid:8)(cid:15)(cid:10) Meyer and Guillot(cid:2)(cid:7)(cid:8)(cid:8)(cid:13)(cid:10) Steels and Brooks(cid:2) (cid:7)(cid:8)(cid:8)(cid:14)(cid:10)
Meyer(cid:2) (cid:7)(cid:8)(cid:8)(cid:14)(cid:2) (cid:7)(cid:8)(cid:8)(cid:17) (cid:12)(cid:4) Within the framework of such comparisons(cid:2) the biological roots of the
animat approach haven(cid:18)t been granted special attention(cid:4) This paper will be centered on these
rootsandwill arguethatadaptivebehaviorsgeneratedbybiomimetic mechanismsarepromising
solutions to various survival problems(cid:2) and that such behaviors are likely to provide e(cid:19)cient
substitutesforthepurelyengineering solutionstotheseproblemsthatarewidelyusedatpresent(cid:4)
Current research e(cid:16)orts representing three domains of application will illustrate this point(cid:2) and
will dealsuccessively withproblemsofnavigation(cid:2)ofaction selection(cid:2)andofevolutionary design(cid:4)
(cid:2) Navigation
The ability to navigate (cid:3) i(cid:4)e(cid:4)(cid:2) to reach any goal place from any starting place(cid:2) while avoiding
passing through other places (cid:3) is probably the most basic requisite for an animat(cid:18)s survival(cid:4)
Indeed(cid:2) without such an ability(cid:2) the animat wouldn(cid:18)t be able to reach energy sources(cid:2) to avoid
bumpingintodamagingobstacles(cid:2)ortoescapefromdangeroushazards(cid:4) Engineeringsolutionsto
thenavigationproblem usually resorttointernal geometricalrepresentationsoftheenvironment
(cid:6)Schwartz and Yeap(cid:2) (cid:7)(cid:8)(cid:9)(cid:20)(cid:10) Canny(cid:2) (cid:7)(cid:8)(cid:9)(cid:9)(cid:10) Latombe(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:10) Hwang and Ahuja(cid:2) (cid:7)(cid:8)(cid:8)(cid:11)(cid:12) that are
confronted with various implementation di(cid:19)culties(cid:2) due to memory or time requirements(cid:2) as
well as sensory and motor errors (cid:6)Nehmzow(cid:2) (cid:7)(cid:8)(cid:8)(cid:14)(cid:12)(cid:4) Therefore(cid:2) as many animals are very good
at navigating in apparently simpler and more robust ways(cid:2) it is not surprising that several
research e(cid:16)orts have already been aimed at incorporating the corresponding current biological
knowledge into animat navigation models (cid:6)for a review(cid:2) see Trullier et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:17)(cid:12)(cid:4)
The work of Franceschini (cid:6)Franceschini et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:9)(cid:8)(cid:10) Franceschini(cid:2) (cid:7)(cid:8)(cid:8)(cid:11)(cid:12)(cid:2) for example(cid:2) calls
upon knowledge gained about the compound eye of the (cid:21)y and upon the development of an
on(cid:3)board electro(cid:3)optical system for the guidance of a mobile robot(cid:4) This robot moves about
and(cid:2) from its relative motion between itself and the environment(cid:2) evaluates the distance to
any potential obstacle(cid:4) The control system of the robot includes a main visual system with
compound optics and a panoramic (cid:5)eld that calls upon a circular network of motion detectors(cid:4)
Each such detector measures the relative angular velocity of any contrast point that passes
across its receptive (cid:5)eld as a result of the robot(cid:18)s own motion and makes use of the motion
parallax to evaluate the radial distance to that point (cid:6)Figure (cid:7)(cid:12)(cid:4) The information relayed by
the motion detectors is then input to an analog parallel neural network designed to implement
an obstacle(cid:3)avoidance algorithm(cid:4) This network transforms the polar map of the environment
delivered by the detectors into two analog signals that modulate the speed of the drive and
steering motorsof the robot(cid:4) The control architecture is also equipped with an accessory visual
systemthatconstantlysurveysthe horizon and informsthe robotaboutthe bearings ofa target
lamp(cid:4)
Likewise(cid:2) the navigation system described by Cartwrightand Collett (cid:6)(cid:7)(cid:8)(cid:9)(cid:15)(cid:12)is inspired from
the way bees use nearby landmarks to return to an already known food source(cid:4) This ability
relies upon the use of the remembered image of what was present on their retina when they
were at the food source(cid:22) bees seem to adjust their (cid:21)ight path by continuously comparing their
retinal image of the landmark con(cid:5)guration with their memorized snapshot and to lessen the
(cid:11)
(cid:2)
(cid:4)
(cid:2)
(cid:3)
(cid:3)
(cid:5)(cid:6)
(cid:4) (cid:4) (cid:5)(cid:6) (cid:7) (cid:8)(cid:9)(cid:10)(cid:11)(cid:3)
(cid:2)
Figure (cid:2)(cid:3) Principle of motion parallax(cid:2) The distance D to a point P located at azimuth
(cid:3) can be calculated by an animat translating at speed V(cid:0)(cid:4) if it is equipped with sensors
measuringthe angular speed (cid:5) of P(cid:2)
discrepancy between the two (cid:6)Figure (cid:11)(cid:12)(cid:4)
Other models involve spatial representations that seem to be encoded in the hippocampus
and para(cid:3)hippocampal formations in the brain of some animals (cid:6)O(cid:18)Keefe and Nadel(cid:2) (cid:7)(cid:8)(cid:20)(cid:9)(cid:12) and
to involve so(cid:3)called place cells and head direction cells(cid:4) In relation to the spatial layout of
surrounding landmarks(cid:2) the activity of the former depends on the animal(cid:18)s current location(cid:2)
whatever the direction faced (cid:6)for a review(cid:2) see Muller et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:12)(cid:2) whereas the activity of
the latter depends upon the absolute orientation of the head(cid:2) whatever the animal(cid:18)s location
(cid:6)Taube et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:8)(cid:23)a(cid:2) (cid:7)(cid:8)(cid:8)(cid:23)b(cid:12)(cid:4) Various more or less realistic equivalents of place cells and head
direction cells have been used in several navigation systems(cid:4) For example(cid:2) when the animat of
(cid:0)
Zipser (cid:6)(cid:7)(cid:8)(cid:9)(cid:17)(cid:12)explores its environment(cid:2) it recruits atregular time intervals a so(cid:3)called view cell
whose activity is tuned to the distance and bearings of given landmarks seen from the current
position(cid:4) If the animat moves slightly from this position(cid:2) the activity of the corresponding view
cell diminishes according to a Gaussian law so that(cid:2) when the distances and bearings of the
landmarks signi(cid:5)cantly di(cid:16)er from those to which the view cell has been tuned(cid:2) the cell will no
longer be activated(cid:4) Likewise(cid:2) every time a view cell is recruited(cid:2) a so(cid:3)called goal cell is also
recruited(cid:2) whose output codes the direction of the goal from the current position (cid:6)Figure (cid:15)(cid:12)(cid:4)
After exploration(cid:2) when the animat moves in its environment(cid:2) the degree of activation of
each view cell it has previously recruited will depend upon the position and orientation of the
animat relative to the landmarks(cid:4) Such an activity pattern uniquely characterizes the place
the animat is currently situated in and(cid:2) therefore(cid:2) constitutes what(cid:2) since Tolman (cid:6)(cid:7)(cid:8)(cid:13)(cid:9)(cid:12)(cid:2) has
comed to be called a cognitive map(cid:4) Insofar as the activity level of each view cell is used to
weight the direction information provided by its associated goal cell(cid:2) the direction in which the
animat should movein order toreach its goal fromthe current position is given by the weighted
average of the directions coded by each goal cell (cid:6)Figure (cid:13)(cid:12)(cid:4)
Such an implementation endows an animat with minimal adaptive capacities(cid:4) In partic(cid:3)
ular(cid:2) its ability to self(cid:3)estimate its position in the environment relies upon information that
(cid:0)
It is not called a place cell because its activity also depends upon the current orientation ofthe animat(cid:2)
(cid:15)
(b)
(a)
(c)
Figure (cid:4)(cid:3) Cartwright and Collett(cid:6)s snapshot model(cid:2) The model bee is represented by two
concentric circles(cid:4) the outer circle represents its current retinal image and the inner one
represents itsmemorizedsnapshot(cid:2) (cid:7)a(cid:8)Atfoodlocation(cid:4)the imagesofthe landmarks(cid:7)(cid:9)lled
circles(cid:8) project onto the retina and the snapshot(cid:4) as shown(cid:2) To return to the feeding site
(cid:7)b(cid:8)(cid:4) the modelbee tries to match its retinal image (cid:7)dark areas on the outer circle(cid:8) and the
remembered snapshot (cid:7)dark areas on the inner circle(cid:8)(cid:2) Each dark area on the snapshot is
pairedwiththeclosestdarkareaontheretinalimage(cid:7)dashedlines(cid:8)(cid:2) Eachpairyieldsaradial
vector(cid:7)forward(cid:10)backwardmotion(cid:8)forthesizeadjustmentandatangentialvector(cid:7)left(cid:10)right
rotation(cid:8) for the orientation adjustment (cid:7)un(cid:9)lled arrows(cid:8)(cid:2) The resulting movement(cid:4) which
globallyreduces the discrepancy between the snapshots(cid:4) is computed (cid:7)c(cid:8) fromthe summed
vector (cid:7)(cid:9)lled arrow(cid:8)(cid:2)
(cid:13)
b Animat’s position and orientation
a 4 c Landmark
2 G Goal location
1 G Oriented view-field
3 Orientation of the field (local reference direction)
5
Direction to the goal associated to the field
Computed direction to the goal
Figure (cid:5)(cid:3) Zipser(cid:6)s view(cid:11)(cid:9)eld model(cid:2) Each view(cid:11)cell in the modelis associated with a view(cid:11)
(cid:9)eld(cid:4) de(cid:9)ned by the distances from the center of the (cid:9)eld to three landmarks and by an
orientation(cid:2) This orientation(cid:4) which serves as a local reference direction(cid:4) corresponds to
the orientation the animat had at its (cid:9)rst visit to the place(cid:4) and is de(cid:9)ned by where the
three landmarksare with respect to the animat(cid:6)shead(cid:11)direction (cid:7)left(cid:4)ahead(cid:4) or right(cid:8)(cid:2) The
direction to the goal from the view(cid:11)(cid:9)elds is coded as vectors(cid:4) oriented with respect to each
local reference direction(cid:2) The direction to the goal is interpolated from the activities and
the vectors associated with all the view(cid:11)cells (cid:7)see text and Figure (cid:12)(cid:8)(cid:2)
is distributed across numerous place cells in its cognitive map(cid:2) and the accidental destruction
ofafewsuchplacecells wouldcauseagracefuldegradationoftheanimat(cid:18)snavigationcapacities(cid:4)
The spatial representations built by other navigation systems also encode the topological
links that characterize the animat(cid:18)s environment(cid:4) During exploration(cid:2) when the animat moves
fromone given place to another(cid:2)a link is drawn between the place cells thatare associated with
these places(cid:2) and this link is indexed by the associated move(cid:4) Thus(cid:2) subsequently(cid:2) because the
animat knows which moves are likely to lead to which place from which other place(cid:2) it can plan
a trajectory to the goal(cid:4) This(cid:2) for example(cid:2) is the case with Matari(cid:24)c(cid:18)s robot (cid:6)Matari(cid:24)c(cid:2) (cid:7)(cid:8)(cid:8)(cid:23)(cid:12)
that explores the environment by following walls(cid:4) This robot uses the information provided
by its sensors and its e(cid:16)ectors to characterize di(cid:16)erent places in its environment and builds a
topological cognitive map that records the possible moves from place to place(cid:4) Thus(cid:2) while the
robot explores the environment shown on Figure (cid:14)(cid:2) it records in its map thatplace C(cid:13)is passed
through while it moves in the direction indicated by its compass and while its sonars detect
similar nearby obstacles on its right and on its left (cid:3) thus suggesting that place C(cid:13) is actually a
corridor(cid:4) Likewise(cid:2) it records that a right turn leads from C(cid:13) to another place(cid:2) RW(cid:17)(cid:2) which is
passed throughwhile therobotmovesin a given direction and while it detectsa nearbyobstacle
on its right (cid:3) thus suggesting that place RW(cid:17) is adjacent to a right wall(cid:4) In other words(cid:2) places
like C(cid:13) and RW(cid:17) on the robot(cid:18)scognitive map play the role of place cells thatbecome activated
when the robot passes through the corresponding places in the environment(cid:2) and such a map
allows the robot to position itself correctly(cid:4)
Because the map also provides information about the physical length of each place(cid:2) the
shortest path leading to any given goal from the current place can be generated by initiating
a spreading activation process throughout the map(cid:2) in all directions from the goal(cid:4) Insofar as
the speed of this process depends upon the length of the places through which it travels(cid:2) the
direction from which goal activation (cid:5)rst arrives in each place indicates the direction in which
to move in order to reach the goal(cid:4)
Such a navigation system provides an animat with the highly adaptive capacity of planning
its trajectory to the goal(cid:4) According to the experience in the head metaphor (cid:6)Craik(cid:2) (cid:7)(cid:8)(cid:13)(cid:15)(cid:12)(cid:2)
trajectory planning makes it possible to run internal simulations that(cid:2) being decoupled from
overt behavior(cid:2) avoid the hazards of dangerous encounters(cid:4)
(cid:14)
Visual input
Left Ahead Right
L dL L dL L dL
a b c a b c a b c Landmark identity cells
View cells
1 2 3 4 5
Cognitive map
Representation of the goal location
Goal cells
weighted vectors
weighted average resulting vector
To the motor system
Figure (cid:6)(cid:3) Zipser(cid:6)s navigationsystem(cid:2) The upper module of the neural network (cid:7)bordered
in gray(cid:8) is the oriented place representation by view cells(cid:2) Each landmark (cid:7)a(cid:4) b or c(cid:8)(cid:4)
indicated by its identity L(cid:4) is seen left(cid:4) ahead or right relative to the head(cid:11)direction and
at a certain distance dL(cid:2) The landmark identity cells a(cid:4) b(cid:4) and c (cid:9)re proportionally to
the distances to the associated landmarks(cid:7)dL(cid:8) when the associated landmarks are in their
gross angular (cid:9)eld (cid:7)L is (cid:13) or (cid:14)(cid:8)(cid:2) For instance(cid:4) in the position of the animat in Figure (cid:15)(cid:4)
landmark identity cells a and b for the (cid:16)Ahead(cid:17) visual (cid:9)eld and landmark identity cell c
for the (cid:16)Right(cid:17) visual (cid:9)eld are active (cid:7)shading(cid:8) and all the other landmark identity cells
remainsilent(cid:2) The combinationofthelandmarkidentitycells activatestheviewcells(cid:2) Here(cid:4)
view(cid:11)cells (cid:14) and (cid:18) (cid:9)re the most and view(cid:11)cell (cid:15) (cid:9)res the least (cid:7)shading(cid:8)(cid:2) The lower module
represents the subsequent population coding of the direction to the goal(cid:2) Each view(cid:11)cell is
associated with the direction to the goal(cid:4) and the corresponding vector is represented by a
goal cell(cid:2) The vectors representing these directions are weighted by the activities of their
associated view cells and the weighted average yields the estimate of the direction to the
goalfromthe animat(cid:6)scurrent position(cid:2)
(cid:17)
LW4
0
LW0
LW8 the environment 12 4
C4
RW6
8
C0
RW10 compass reading
Start C12
the corresponding cognitive map
C0 C4 LW0 LW4 LW8 C12 RW6 RW10
Figure (cid:7)(cid:3) Example of an environment and of the associated graph in Matari(cid:19)c(cid:6)s model(cid:2) A
placeislabeledaccordingtothecharacteristics ofthe rangesensor readings(cid:7)RW(cid:20)rightwall(cid:4)
LW(cid:20)leftwall(cid:4)C(cid:20)corridor(cid:8)andtothecompassreading(cid:7)from(cid:13)to(cid:14)(cid:18)(cid:8)whentherobot(cid:4)driven
by its lower(cid:11)level guidance strategy (cid:7)a wall(cid:11)followingprocedure(cid:8)(cid:4) travels through it(cid:2) In the
corresponding cognitive map(cid:4) each place is represented by a node(cid:4) and any two nodes can
be linked dynamicallyto code the adjacency relationship(cid:2)
Finally(cid:2) another variety of navigation model calls upon the representation of metric spatial
relations between two or more places on a plane and upon vector manipulations(cid:4) Usually these
modelsinvolveasystemofcoordinatesbyreferencetowhichthepositions ofplacesarerecorded(cid:4)
This(cid:2) for example(cid:2) is the case with the model of Wan et al(cid:4) (cid:6)(cid:7)(cid:8)(cid:8)(cid:13)(cid:12) that integrates multi(cid:3)modal
(cid:2)
information involving kinesthetic signals provided by e(cid:3)erent copies (cid:2) proprioceptive signals
supplied by the integration of the angular velocity of the animat(cid:18)s head(cid:2) and exteroceptive
signals from its visual sensory system (cid:6)Figure (cid:17)(cid:12)(cid:4)
In this model(cid:2) the correspondence between the animat(cid:18)s cognitive map and its position
in the environment is coded by the activity of so(cid:3)called place code units(cid:4) This activity is
determined bythe product ofseven Gaussianfunctions(cid:2) respectively tuned(cid:2) atexploration time(cid:2)
to the distances and allocentric bearings of two selected landmarks(cid:2) to the egocentric bearing
di(cid:16)erence between twoother selected landmarks(cid:2) and to the estimated Cartesian coordinates of
the animat(cid:18)s position relative to an identi(cid:5)ed reference point(cid:4) Should one or more items taken
into account in such a calculation be missing(cid:2) the corresponding term or terms would simply be
excluded from the product(cid:4) This model thus accounts for the experimental persistence of place
cellactivityindarkness(cid:6)Quirketal(cid:4)(cid:2)(cid:7)(cid:8)(cid:8)(cid:11)(cid:12)(cid:2)becauseinformationprovidedbythepathintegrator
mechanism is capable ofsubstituting forthemissing visual information(cid:4) In this model(cid:2) thelocal
view module (cid:6)McNaugton(cid:2) (cid:7)(cid:8)(cid:8)(cid:7)(cid:12) transforms the egocentrically sensed visual information into
an allocentric representation that is stored in the place code module(cid:2) by simply summing the
head direction and the egocentric bearing(cid:4) Conversely(cid:2) by subtracting the egocentric bearing
provided by the visual input to the allocentric bearing recorded in the place code module(cid:2) the
local view module makes it possible to update the estimate of the head direction (cid:6)Figure (cid:20)(cid:12)(cid:4)
(cid:2)
Motor subsystems that drive muscles can map an e(cid:2)erence copy or corollary dischargeback onto the per(cid:3)
ceptual system(cid:4) which can thus distinguish self(cid:3)induced movements from world(cid:3)induced movements (cid:5)McCloskey(cid:4)
(cid:6)(cid:7)(cid:8)(cid:6)(cid:9)(cid:2)
(cid:20)
Vestibular ΔΦh Head reset Local < r i ,θi , θi −θj > Visual
Input Direction View Input
Φ
h
r ,Φ
i ki
Φ PC
h k θi −θj
efference
Motor copy Path reset Place
Cmd
Integrator Code
<x ,y >
p p
Figure (cid:8)(cid:3) Functional block diagram of Wan et al(cid:2)(cid:6)s model(cid:2) It shows how multimodal
informationmight be combined and mutuallyin(cid:21)uenced(cid:2) Place is represented as the fuzzy
conjunction of multimodal information(cid:4) coupling path integration (cid:7)(cid:0) xp(cid:2)yp (cid:3) Cartesian
coordinates with respect to ana priori globalreference frame(cid:8)(cid:4)visual information(cid:7)distance
ri and relative bearing (cid:4)i of each visible landmark i(cid:4) transformed into absolute bearing
(cid:3)ki that depends on the current place k(cid:8)(cid:4) and head(cid:11)direction (cid:7)(cid:3)h(cid:8)(cid:2) The head(cid:11)direction
representation is updated through the integration of angular velocity (cid:22)(cid:3)h and reset by
place recognition when there is some drift (cid:7)PCk(cid:8)(cid:2)
Likewise(cid:2) the place code module makes it possible to reset the Cartesian coordinates of the
animat(cid:2) which can then be used by a vector manipulation module to plan a trajectory to any
given goal(cid:4)
Besides the particular adaptive capacities that the integration of multimodal signals and
the reset of the error(cid:3)accumulating information given by the kinesthetic and proprioceptive
integrators provide to the above speci(cid:5)c implementation(cid:2) such a navigation system a(cid:16)ords an
animat other important potentials for survival(cid:2) since additional vector manipulation abilities
make metric detours and shortcuts possible(cid:4)
(cid:3) Action selection
The chances of survival of any animal are obviously heavily dependent upon its ability to solve
the action selection problem (cid:6)McFarland(cid:2) (cid:7)(cid:8)(cid:9)(cid:7)(cid:10) Maes(cid:2) (cid:7)(cid:8)(cid:8)(cid:13)(cid:12)(cid:2) i(cid:4)e(cid:4)(cid:2) upon its ability to choose(cid:2) at
any time(cid:2) the proper action to perform in order to optimize the achievement of its goals(cid:4) Since
the days of the robot Shakey (cid:6)Nilson(cid:2) (cid:7)(cid:8)(cid:9)(cid:15)(cid:12)(cid:2) traditional engineering solutions to this problem
haverelied upon the assumption thattheenvironment can be accurately andexhaustively mod(cid:3)
eled bythe animat and havebeen reduced tothe problem of planning asequence ofactions that
will(cid:2) if performed(cid:2) achieve a single and (cid:5)xed goalimposed bythe experimenter(cid:4) Other engineer(cid:3)
ing solutions (cid:6)Kaelbling and Rosenschein(cid:2) (cid:7)(cid:8)(cid:8)(cid:23)(cid:12) have been designed to deal with sets of (cid:5)xed
goals by automatizing the process of designing the arbitration circuitry that selects the proper
action(cid:4) These solutions require theexperimenter tospecify the animat(cid:18)srepertoires ofgoalsand
actions(cid:2) and how these goals can be reduced to other goals or actions(cid:4) Then(cid:2) a compiler anal(cid:3)
ysesthisspeci(cid:5)cation andgeneratesacircuit thatimplements thedesired goal(cid:3)seeking behavior(cid:4)
Numerous research e(cid:16)orts have been directed at dealing with the more challenging action
selection problems that any animal experiences in nature and at unraveling the basic mecha(cid:3)
(cid:9)
nisms that allow him to survive (cid:6)fora review(cid:2) see Guillot and Meyer(cid:2) (cid:7)(cid:8)(cid:8)(cid:13)(cid:12)(cid:4) The corresponding
models usually involve several endogenously(cid:3)generated time(cid:3)varying goals and implement a mo(cid:2)
tivational system (cid:6)McFarland(cid:2) (cid:7)(cid:8)(cid:20)(cid:13)(cid:10) Toates(cid:2) (cid:7)(cid:8)(cid:9)(cid:17)(cid:10) Colgan(cid:2) (cid:7)(cid:8)(cid:9)(cid:8)(cid:12)(cid:4) The Booker model (cid:6)Booker(cid:2)
(cid:7)(cid:8)(cid:9)(cid:9)(cid:12)(cid:2) for instance(cid:2) uses a classi(cid:4)er system (cid:3) i(cid:4)e(cid:4)(cid:2) a rule(cid:3)based structure designed to reproduce
various cognitive processes in animals and men (cid:6)Holland et al(cid:4)(cid:2) (cid:7)(cid:8)(cid:9)(cid:17)(cid:12) (cid:3) in order to allow an
animat to learn to produce goal(cid:3)seeking sequences through the use of rules that manipulate ob(cid:3)
jects(cid:2) goals(cid:2) and object(cid:25)goal associations(cid:2) rather than rules that code simple stimulus(cid:25)response
associations(cid:4) Such a structure allows the animat to move about on a grid containing food ob(cid:3)
jects and noxious objects(cid:2) these objects being located in the middle of stimulus auras whose
intensity diminishes with the distance from the object in question(cid:4) Contact with food gives
rise to a reward(cid:2) while contact with a noxious object entails a punishment(cid:4) To survive(cid:2) the
animat must learn which actions are appropriate in which situations(cid:10) it(cid:2) therefore(cid:2) must build a
model ofits environment(cid:4) To this end(cid:2) its controlarchitecture mimics the hierarchical structure
described by Tinbergen (cid:6)(cid:7)(cid:8)(cid:14)(cid:7)(cid:12) to model instinctive behavior in animals(cid:4) Basically(cid:2) it involves
three instinctive centers concerned with locomotion(cid:2) food(cid:3)seeking(cid:2) and pain aversion(cid:2) as well as
innate releasing mechanisms that prompt the animat to pursue two objectives(cid:22) to eat in the
presence of food and to (cid:21)ee in the presence of a noxious object (cid:6)Figure (cid:9)(cid:12)(cid:4)
The animat(cid:18)s default activity consists in exploring its environment(cid:4) This activity is in(cid:3)
terrupted when the strength of the sensory or motivational signals associated with a lower
instinctive center allow it to be activated and to trigger an approach or avoidance behavior(cid:4)
These behaviors can be expected to prompt the animat to feed or to (cid:21)ee(cid:2) according to what
kind of object it has come in contact with(cid:2) and to shut o(cid:16) the activity of the corresponding
center(cid:2) thus allowing the animat to resume its exploration(cid:4)
In this approach(cid:2) the animat(cid:18)s adaptive capacities derive from its ability to learn represen(cid:3)
tations(cid:4) These representations are used to classify the objects encountered in the environment
into categories that have a(cid:16)ective signi(cid:5)cance and accordingly to trigger the appropriate be(cid:3)
havior(cid:4) In other words(cid:2) Booker(cid:18)s animat learns to respond to the regularities hidden behind
the equivocal nature of its sensory cues and to take advantage of the useful and structured
information a(cid:16)orded by the environment (cid:6)Gibson(cid:2) (cid:7)(cid:8)(cid:9)(cid:17)(cid:12)(cid:4)
According to Tyrrel (cid:6)(cid:7)(cid:8)(cid:8)(cid:15)(cid:12)(cid:2) such a hierarchical control architecture gives rise to several
problems when dealing with more complex environments that pose more challenging survival
problems and thatentail the management ofmore than three elementary behaviors(cid:4) To demon(cid:3)
stratethispoint(cid:2) hedevised asimulated environmentwhich posed(cid:7)(cid:15)di(cid:16)erentsurvival problems
to an animat(cid:2) i(cid:4)e(cid:4) obtaining food(cid:2) obtaining water(cid:2)keeping clean(cid:2) regulating body temperature(cid:2)
avoiding predators(cid:2) being vigilant for predators(cid:2) staying close to cover(cid:2) avoiding the boundaries
of the territory(cid:2) avoiding dangerous places(cid:2) avoiding irrelevant creatures(cid:2) sleeping at night in a
den(cid:2) not getting lost and reproducing(cid:4) To solve these problems the animat has a choice of (cid:15)(cid:14)
actions that it can undertake(cid:2) such as drink(cid:2) clean self(cid:2) sleep(cid:2) move north(cid:2) move south(cid:3)west and
look around(cid:4) By choosing appropriate actions in appropriate situations(cid:2) the animat can exert
some control over the values of its internal variables (cid:3) like its levels of food and water (cid:3) and
over its environment (cid:3) like how well it perceives its local environment or can be perceived by
predators(cid:4) The overall quality of a given action selection controler is measured by an equivalent
of genetic (cid:5)tness(cid:2) i(cid:4)e(cid:4) by the number of times the animat manages to reproduce before it dies(cid:4)
Comparative results obtained by Tyrrell on various control architectures suggest that the
most e(cid:19)cient design is that of a set of overlapping free (cid:5)ow hierarchies(cid:2) each hierarchy being
devoted toeachsurvival problem thattheanimat is confrontedwith(cid:4) Such hierarchies aremade
up of nodes interconnected via weighted connections(cid:2) which are similar to standard arti(cid:5)cial
(cid:8)
Local View
φ
h -
φ
ki
Head
Direction Allocentric
Bearing
Memory
φ θ
h i
θi−θj Visual Input
r
i
PCk φki θi−θj ri
Place Code
Figure (cid:9)(cid:3) In Wan et al(cid:2)(cid:6)s model(cid:4) the animat learns the relationship between egocentric
and allocentric bearings of speci(cid:9)c cues as viewed from a set of locations(cid:2) Information
about the current (cid:7)estimated(cid:8) head(cid:11)direction (cid:7)input (cid:3)h(cid:8)(cid:4) the current (cid:7)estimated(cid:8) position
(cid:7)PCk(cid:8) and the egocentric bearings of landmarks (cid:7)(cid:4)i(cid:8) converge into the Allocentric Bearing
Memory(cid:4) which computes the head(cid:11)direction independent (cid:7)allocentric(cid:8) bearings of these
landmarks(cid:7)(cid:3)ki forlandmarki at place k(cid:8)(cid:2) Thismodulecan alsocorrect the head(cid:11)direction
representationbyusingthememorizedallocentricbearings(cid:4)therepresentationofthecurrent
place and visual information(cid:2)
)(cid:24)(cid:9)(cid:30)&*(cid:9)(cid:10)
$(cid:31)(cid:10)#(cid:31)&(cid:26)
(cid:12)(cid:3)(cid:13)(cid:3)(cid:14)(cid:3)(cid:15)(cid:16)(cid:3)(cid:17) (cid:20)(cid:28)(cid:29)(cid:30)(cid:6)(cid:27)(cid:26)
(cid:23)(cid:24)(cid:10)(cid:25)(cid:26)(cid:27)
(cid:10)(cid:6)(cid:28)(cid:9)(cid:6)(cid:24)(cid:8)
%(cid:6)(cid:6)# (cid:8)(cid:9)(cid:25)(cid:10)(cid:31)(cid:30)
(cid:8)(cid:9)(cid:25)(cid:10)(cid:31)(cid:30) (cid:22)(cid:20)(cid:18)(cid:3)(cid:20)’(cid:3)(cid:16)(cid:4)(cid:17)( (cid:19)(cid:29)(cid:29)(cid:27)(cid:6)(cid:31) ! (cid:19)(cid:5)(cid:2)(cid:20)(cid:19)(cid:21)(cid:16)(cid:17)(cid:22)(cid:16)(cid:3)(cid:17) (cid:19)"(cid:6)(cid:9)#
% (cid:6)(cid:6)(cid:6)(cid:10)#&(cid:31) & (cid:13)(cid:6)(cid:10)(cid:8)(cid:24)$(cid:26) (cid:10) (cid:6)(cid:6)(cid:10)(cid:28)&(cid:9)(cid:6)(cid:31) (cid:24)&(cid:8) (cid:20)(cid:8) (cid:31)(cid:29)(cid:26)
Figure (cid:10)(cid:3) Control architecture of Booker(cid:6)s animat(cid:2) The rectangles correspond to instinct
centers and arrows to innate releasing mechanisms(cid:2) Each instinctive center is characterized
by a releasing stimulus and an associated action(cid:4) and can be submitted to two sources of
control(cid:20) the current motivationalstate andother instinctive centers higher inthe hierarchy(cid:2)
Here(cid:4) the food(cid:11)seeking and pain(cid:11)aversion centers are located on the same hierarchical level(cid:4)
under the control of the locomotion center(cid:2) The food(cid:11)seeking center is subjected to an
additionalmotivationalcontrol exerted by the degree of hunger the animatexperiences(cid:2)
(cid:7)(cid:23)