Monitoring Strategies for Adaptive Periodic Control

in Behavior-based Robotic Systems

Ernesto Burattini, Alberto Finzi, Silvia Rossi

Dipartimento di Scienze Fisiche

University of Naples “Federico II”

Naples, Italy

{ernb,finzi,srossi}@na.infn.it

Mariacarla Staffa

Dipartimento di Informatica e Sistemistica

University of Naples “Federico II”

Naples, Italy

mariacarla.staffa@unina.it

Abstract— The main goal of our current research is the designof a behavior-based robotic architecture that has the capabilityof adapting its behaviors to the rate of change of both theenvironment and its internal states reducing the computationalcosts of input processing. Inspired by research on biologicalclocks, we introduced a simple schema theory model wherereleasing mechanisms are combined with adaptive internal clocks.In this paper, we describe the design and development ofa complete robotic architecture implementing this model. Inparticular, we considered a mobile robot domain that simulatesthe navigation behavior of a Catagliphys ant enhanced withsimple visual capabilities.

I. INTRODUCTION

The main goal of our current research is the design of

a behavior-based robotic architecture that has the capability

of adapting its behaviors both to the rate of change of the

environment and to changes of its internal states. This adaptive

behavior requires effective mechanisms for controlling sensors

and effectors with respect to the internal constraints imposed

by the control system and the external environment. The

executive control is to combine different low-level strategies

(such as obstacles avoidance, walls follow, gates crossing,

etc.), with high-level activities (such as achieving a goal,

returning to the nest, etc.), giving them, from time to time,

different priority values both for allocation of resources and

for action selection processes. The low-level activities affect

the safety of the system, and may be achieved by applying

principles of reactive control. However, high-level activities

generally are achieved by processing more complex tasks, and,

therefore, require high computational costs for both the inputs

processing and the acquisition of data from the environment.

In this context, self-regulating mechanisms balancing sensors

elaboration and behavior execution play a crucial role. Indeed,

frequent sensors readings could result in a high computational

charge that degrades the performances of executive and delib-

erative processes, while, adopting large latencies in sensors

sampling is unsafe since the environment may change too

much in between two consecutive readings. In this sense,

any effort to build a cognitive architecture for dealing with

dynamical and flexible behaviors has to deal with an efficient

processing capability of the sensors elaboration.

In a previous paper, Burattini and Rossi proposed the

concept of periodical/timed activations of behaviors [1], [2].

Inspired by internal self-regulation mechanisms in living or-

ganisms [3], they introduced internal clocks to tune and adapt

the frequencies of the behaviors activities. Such mechanisms

can speed up or slow down the period of behaviors activation

and thereby the reading frequency of the sensors according to

both the robot–environment interactions and the interactions

that may arise within the robots itself [4], [5], [6].

In this paper, inspired from this work, we propose a real

robot implementation of a behavior-based control architecture

endowed with periodic releasing mechanisms. In particular, we

investigate the feasibility and effectiveness of the approach and

we explore how the adaptive clocks affect the computational

load for the elaborations of perceptual data, considering each

single behavior and the overall system. The question we have

to answer is twofold. First, is the robot we are building reliable

and safe? This means that the robot should be able to timely

react to the external stimuli avoiding dangerous situations.

Second, does the introduction of adaptive clocks in a single

behavior provide better performances of the robotic system,

in terms of a reduction of computational resources spent to

achieve the tasks?

To test the proposed model we implemented a behavior-

based control system endowed with these regulation mech-

anisms and evaluated different settings. These mechanisms

should provide an interconnecting layer between adaptive

behaviors and high level cognition [7].

II. TIMED ACTIVATION OF BEHAVIORS: RHYTHMIC

RELEASERS

Our aim is to develop a behavior-based control for a

robotic system capable of adapting its emergent behavior to

the surrounding environment and to its internal state. Our

working hypothesis is that adaptive behaviors can be simulated

in the control activity of a robot starting from self-regulating

mechanisms. In particular, our architecture combines Innate

releasing or inhibiting mechanism and biological clocks.

a) Innate releasing or inhibiting mechanisms (IRMs):

Lorentz [8] and Tinbergen [9] identified in many animals an

innate releasing or inhibiting mechanism (IRM) able to control

and coordinate behaviors. An IRM presupposes a specific

stimulus that releases a pattern of actions. For example, a

prey animal may have, as an IRM, the stimulus coming from

2009 Advanced Technologies for Enhanced Quality of Life

978-0-7695-3753-5/09 $25.00 © 2009 IEEE

DOI 10.1109/AT-EQUAL.2009.34

130

Fig. 1. Schema theory representation of IRMs (left) and AIRMs (right). For each behavior we have a schema composed of a coordinated control program(releaser/clock), a perceptual schema and a motor schema. On the left side, the releasing function activates the motor schema; on the right side, the internalclock directly regulates the frequency of the sensor activations enabling/disabling sensors readings.

the view of the predator, which activates the escape behavior.

IRMs were included in the schema representation of behaviors

[10] in the form of releasers, controlling when behaviors

must be activated or deactivated. A releaser is an activation

mechanism that depends on exogenous factors (e.g. presence

of a predator) and/or endogenous factors (e.g. hunger).b) Adaptive Innate Releasing Mechanisms (AIRMs): The

releaser’s function, somehow, recalls the notion of “internal

clock”, already introduced in some approaches [11], [5], [6]

in order to activate motivational states for a robot (for example,

hunger or sleep). In fact, an internal clock, similarly to a

releaser, represents an internal mechanism which regulates

behaviors activations depending on endogenous and/or ex-

ogenous factors. However, there are substantial differences

between IRMs and internal clocks. Indeed, while a releaser

is an instantaneous activation mechanism, the internal clock is

periodical and adaptive.

In [1], [2], the authors inspired by the internal clocks,

connected the concept of IRM to the concept of a periodical

activation of behaviors introducing the Adaptive Innate Releas-

ing Mechanisms (AIRMs). An AIRM is a releasing mechanism

endowed with and adaptive period of the releaser activation.

IRMs and AIRMs are illustrated and compared in Figure 1

deploying a schema theory representation [10]. Here, for each

behavior, we have a schema composed of a Perceptual Schema

(PS), Motor Schema (MS), and an activation mechanism,

either a clock or a releasing function ρ(t). On the left side,

IRM enables/disables data flow σ(t) from PS to MS, the MS

is activated only in the presence of the stimulus, but sensing

data are always processed (i.e. at each machine cycle); in

contrast, on the right side, AIRM directly enables/disables data

flow σr(t) from sensors to PS, therefore when the activation

is disabled sensing data are not processed (sensors readings

minimization). Furthermore, the internal clock regulates the

frequency of the activations, hence the frequency of data

processing (behavior adaptation), using a feedback mechanism

on the processed sensing data σ(t). The AIRM mechanism will

be detailed in the next sections.

III. THE ARCHITECTURE

The robotic system is controlled by a behavior-based ex-

ecutive where each behavior can be described by a schema

theory model [10]. Each behavior of the Robotic System (RS)

is provided with a periodical and adaptive clock that controls

its activations (AIRMs as defined in [1], [2]). In particular, we

assume that (see Figure 1):

• Each clock has a base period (or initial rhythm) called pb,

ranging in the interval [pbmin, pbmax]. The values of pb

and range are experimentally tuned. pb depends on sev-

eral factors such as the sensors precision, environmental

conditions and purposes of the behavior;

• The releasing function ρ(t), is a delta function that, with

period pβ(t), is equal to 1 at each pb machine clocks

and 0 otherwise, and tells the robot when the PS has to

process inputs. This timed releasing function takes data

from a PS and returns an enabling/disabling signal to the

Perceptual Schema itself;

• σr(t) is the actual function representing the data per-

ceived by the sensor at each time step t;• σ(t) is the function that represents data coming from

sensors at each sampling step: σ(t) = f(σr(t))xρ(t),i.e. f(σr(t)) is for processed sensing data and ρ is the

releasing function.

• π(t) is the command sent by the MS to actuators. While

the PS is inactive, no new commands will be sent. This

means that an actuator can keep its output constant until a

new command will come (e.g. the velocity of the wheels

of a robot), or its output will produce an action with its

own duration and then it waits for a new command (e.g.

a gripper that lifts a box).

Each time the behavior is activated, thus enabling sensory

data flow, sensory data are passed in feedback to the in-

ternal clock which updates its period depending on internal

and environmental conditions. The mechanism for period/rate

regulation is called the updating policy and will be detailed in

the following sections.

IV. MONITORING STRATEGIES AND ADAPTATION

The emergent behavior will result from a combination of

the following parameters:

• the initial period pb;

• the range of allowed values for the period [pbmin, pbmax];• the updating policy.

In particular, the combination of these parameters defines a

monitoring strategy. A monitoring strategy is a policy for

scheduling sensing activities and has to balance the cost of

monitoring and the risk of inaccurate and partial information

about the environment.

We will compare our monitoring strategy (adaptive and

periodical) with respect to other relevant monitoring strategies

proposed in literature. In particular, we consider four different

131

cases: a) the robot is not equipped with any internal clock;

b) the robot is equipped with internal clocks and has a priori

knowledge about the task to achieve (for example about the

distance to cover); c) the robot is equipped with clocks, but

does not have any a priori knowledge about the environment;

d) the robot is equipped with internal clocks but not adaptive.

To illustrate these cases we introduce the following example.

Let us consider a robotic system whose purpose is to cover a

certain distance (goTo behavior).

Fig. 2. Monitoring strategies for goTo behavior. Number of activations inthe case of: (a) continuous monitoring; (b) periodic and adaptive monitoringwithout a priori knowledge; (c) periodic and adaptive monitoring with a prioriknowledge; (d) periodic monitoring.

Without an internal clock (a), goTo will be activated at each

control cycle. If the covered distance is constant at each control

cycle, we have that the number of activations n of goTo is

proportional to the distance dist to be covered n: n ∝ dist(see Figure 2). In case (d), if the value of the clock period is

pb, we have the relation: n ∝ distpb

. Cases (b) and (c) fall in the

category of those strategies called of “Interval Reduction” [12],

which are characterized by a variable period for the monitoring

strategy. For cases (b) and (c), it has been demonstrated that

these strategies, asymptotically, are more effective than those

characterized by a constant periodic monitoring [12], [13] in a

wide class of problems. Moreover, in this setting, an interval

reduction strategy has to increase the behavior activations

frequency while approaching the goal. If the robot is equipped

with adaptive clocks and knows a priori the distance to cover,

we might set the initial period pb proportional to half of

the distance to cover and then halve the period of the clock

after each activation of the behavior following the interval

reduction strategy. In this way, the number of activations

would be: n ∝ log2(dist). We assume that this strategy is

the “optimum”, where for optimum, we mean a strategy that

allows to achieve the goal through the minimum number of

activations, without the risk of jeopardizing the correct and

efficient functioning of the robotic system and its safeguard.

Let us now describe how the robot behaves in the case (c),

namely, when it is characterized by adaptive rhythms, but

without a priori knowledge. In this case, the rhythm must

change gradually in accordance with a law that does not

diverge too far from the optimum case. In fact, the robot, even

if not provided with a priori knowledge, can obtain information

from the surrounding environment, thanks to the use of its

sensors and, through these values, it may determine the choice

of the rhythm. For example, the number of activation in this

case may be approximated as: n ∝ log2(dist)+ (distcov)/pb,

where distcov is the distance already covered by the robot.

We can see that the number of activation in the case (b) will

have as an upper bound the case (a), and as a lower bound

the case (c).

Overall, the benefits brought by adaptive and periodical

monitoring strategies are mainly two:

• periodical mechanisms of activation can reduce the num-

ber of activations of the behavior (with respect to the

standard case (a) in which the activations are performed

at every machine cycle), causing a relative decrease in

the computational burden, and improving performance of

the entire system;

• the use of adaptive mechanisms allows us to obtain a

behavior that adapts itself to the specific environmental

conditions (e.g. the robot reads sensors more often if there

is a dangerous situation and less often in cases of a safe

operational situation).

In the following, we will show that, by calibrating appro-

priately the basic rhythms and using the appropriate policies

to update them, we can obtain a significant improvement in

performance compared to an architecture without rhythms.

V. IMPLEMENTATION

Based on the model introduced in the previous sections,

we designed a system whose behavior is mainly guided by

the visual information in a 3D environment, constituted by an

office space, that implements the three monitoring strategies

described here. We used a PIONEER 3DX provided with

a blob camera and sonars. The robot is controlled by a

Player/Stage client [14].

c) Catagliphys domain: We evaluated our approach us-

ing a mobile robot that simulate the navigation behavior of

a Catagliphys ant enhanced with simple visual capabilities.

The robot has the task of searching food in its environ-

ment without any a priori knowledge and then return to its

nest following a straight path, without taking into account

the trajectory followed during the searching of food. The

Catagliphys domain is a good testbed for our purposes: the

domain is interesting from a behavioral point of view and

well analyzed in several ethological field trials [15]; the ant

is provided with internal mechanisms such as guidance and

dead-reckoning; we can associate with this testbed all the

monitoring strategies previously presented (constants, with a

priori knowledge, without a priori knowledge).

d) Behavior-based control: The robot behavior is ob-

tained as the combination of the following primitive behaviors

(see Figure 3): AVOID, WANDER, PATH INTEGRATION,

MOVE TO FOOD, MOVE TO NEST and FIND LANDMARKS,

132

organized in two meta-behaviors called LOOK FOR FOOD and

RETURN TO NEST.

Fig. 3. Control architecture for the mobile robot in the Catagliphys domain.Each behavior receives data from sensors and generates the actions that canbe combined (circled +) or subsumed (circled s).

e) System assessment: To assess the system perfor-

mances, we compared the adaptive control system with respect

to a not adaptive one (i.e. with sensor readings fixed at

every machine cycle). Our aim is to show that a significant

improvement in performance can be obtained by appropriately

tuning the basic periods of clocks and the policies to update

them. Therefore, for each specific behavior we will evaluate

the updating policies, seeking, on one hand, to optimize

performances (i.e. less sensor readings), and, on the other

hand, to enhance the robot safety and the correctness of the

overall system behavior.

f) Behaviors settings: For each behavior, we have to

define the monitoring strategy composed of the 1) updating

policy and the 2) base period, that is empirically defined after

a phase of testing.

1) Updating policies: We start describing each behavior and

the associated updating policy.

The WANDER behavior provides a random search in the

environment. Its output consists only in a random direction

for the robot. Since the activation of this behavior is periodic,

but not adaptive, WANDER can be associated with a constant

clock. That is, the updating policy is the following: pβ(t) =pb = const, where, the clock period pβ remains equal to the

base period pb.

In contrast, the AVOID behavior, responsible for obstacle

avoidance and its output consists in a direction and sets the

navigation velocity for the robot. This behavior is safety

critical and needs an adaptive clock and an associated updating

policy to timely react to dangerous situation. In this case, a

good policy is to change the AVOID clock period according

to the first derivative of the sensory input σ(τ), that represents

the distance from the nearest object, evaluated by the sonars.

More formally, the clock period pβ is updated as follows

pβ(t) = ϕ(σ(t)−σ(t−p′

β)

p′

β

× ρ(t)), where p′β is the period at

the previous behavior activation (initially pβ(t0) = pb), ρ(t)is the releasing function that enables the sensory sampling,

ϕ is a normalizing function mapping the derivative into a set

of permitted values and σ(t − p′β) is the value of σ at the

previous behavior activation. Intuitively, the clock frequency is

adaptive with respect to the environmental changes: the higher

the change the smaller the sensory sampling.

MOVE TO FOOD detects the food and guides the robotic

system towards it, setting its direction. In order to obtain

reliable information from the camera, this behavior forces

the robot to slow down its velocity. By choosing an adaptive

clock period for this behavior (i.e. reducing the number of

activations), we allow the robot to reach the food as soon as

possible. However, we have to set the base period and the

updating policy taking into account that we want to reach

the goal with the minimum docking error. Thus, we have

to balance effectiveness and precision. The idea is to keep

constant the base period pβ(t) until the robot reaches a fixed

distance threshold and then to decrease the period linearly with

the food distance as follows: pβ(t) = ϕ(distance(t) × ρ(t)),where ϕ is a normalizing function, distance(t) is the distance

at time t, ρ(t) is the releasing function that enables sensory

sampling. Here, the behavior performances will depend on the

optimal balance between the clock frequency and distance.

PATH INTEGRATION manages the direction and the dis-

tance to return to nest. This behavior uses odometric sensor

information to calculate the robot shift w.r.t. the previous

position. Analogously to WANDER the behavior remains always

active with a constant clock period, until it reaches the food. In

the return process, the behavior is activated only after an abrupt

change of course, e.g. due to the presence of obstacles, which

diverts the trajectory of the robot that has to be recalculated.

Therefore, we can state that pβ(t) = pb × ρ(t).

RETURN TO NEST set the direction of the robot toward

the nest. Like MOVE TO FOOD it requires reliable information

from the camera and so will slow down the robot velocity

while its PS is active. This behavior exploits a priori knowl-

edge (i.e. the distance to the nest) that allows us to set the

base period value proportional to half of the distance from

the nest, pb = distanceToNest(t0/(2k)) and then reduced

accordingly, i.e. pβ(t) = ϕ(distanceToNest(t)×ρ(t)), It has

been shown that, asymptotically, this strategy is very efficient

[12], [13].

2) Setting the base period: Once we have defined the

behaviors’ schemata and the associated updating policies, it

remains to set the base periods pb. These are empirically

defined after a phase of testing. In the following, we detail

the case of MOVE TO FOOD.

As previously mentioned, the updating policy is to keep

constant the clock period until a certain distance threshold

is reached, then it should be linearly reduced. During the

exploration of the environment, the robot can detect the food at

different distances, hence we need different distance threshold.

In order to optimize the performances and simultaneously

133

distance=200cm distance=120cm distance=60cm

activation docking activation docking activation dockingpb time (s) error (cm) time (s) error (cm) time (s) error (cm)

1 20.274 ∓ 0.630 0.178 ∓ 0.018 7.759 ∓ 1.320 0.289 ∓ 9, 25e−01

0.149 ∓ 0.006 0.215 ∓ 1.125e−004

2 7.638 ∓ 0.480 0.28 ∓ 2.37e−004

3.667 ∓ 0.274 0.287 ∓ 3, 11e001

0.194 ∓ 0.003 0.206 ∓ 6.75e−005

4 5.116 ∓ 0.884 0.28 ∓ 1.17e−004

3.046 ∓ 0.145 0.292 ∓ 3, 25e−01

0.167 ∓ 0.004 0.173 ∓ 1.57e−004

8 2.991 ∓ 0.311 0.28 ∓ 2.17e−004

1.392 ∓ 0.335 0.231 ∓ 4, 25e−01

0.237 ∓ 0.004 0.116 ∓ 6.30e−004

16 1.213 ∓ 0.452 0.250 ∓ 0.004 0.649 ∓ 0.059 0.195 ∓ 0.002 0.167 ∓ 0.004 0.020 ∓ 2.83e−004

32 1.292 ∓ 0.363 0.085 ∓ 0.0019 40.45 ∓ 0.053 0.123 ∓ 5, 7e+001

0.199 ∓ 0.003 0.012 ∓ 2.70e−006

Fig. 4. An evaluation of the MOVE TO FOOD behavior, in terms of activation time and docking error, varying the distance and the base period pb.

take care of the system security constraints, we tested the

MOVE TO FOOD behavior performance under different robot–

food distances. The aim here is to define, for each distance

threshold, how to dynamically reset the base period pb (w.r.t.

the distance) before proceeding with its linear reduction.

We tested the behavior under three different initial values of

the robot–food distance and compared the obtained result w.r.t.

all the possible allowed values for the period [1,base period].For each case, we established the period with the best results

in terms of minimum activation time and acceptable docking

error. In Figure 4, we report the average and variance of the

values gathered in 10 runs for each case. For example, looking

at the results, we notice that, if the initial distance from food is

equal to 120cm, the period with the best performances is pβ =8, because it allows to minimize the time of behavior activation

with a very small docking error. Moreover, for each case, a

horizontal line separates safe/unsafe settings: above the line

there are settings where the robot can stop at a safety distance

from the target, hence not incurring in dangerous situation.

Finally, we noticed that if the robot detects, for the first time,

the food at a distance smaller than 60cm the period has to

reach the minimum value immediately. Looking at the docking

error and the total amount of time for the behavior activations

obtained in each test, we can choose the best period/distance

that balances the tradeoff between safety and efficiency.

VI. EMPIRICAL RESULTS

In order to assess the system performances, we compared

the system behavior when endowed with adaptive clocks with

respect to a not adaptive periodic version (e.g. activations at the

machine clock). In particular, for each behavior, we considered

the number of activations and the total amount of time spent in

behavior execution. In Figure 5, we show some results from

the analysis of the RETURN TO NEST behavior, where for

each case we plot the results of one trial. We note that the

number activations of this behavior, with an adaptive clock, is

proportional to log2(dist) whereas, in the case of non adaptive

clocks, these increase linearly with time. Furthermore, we note

a significant reduction of the execution time of the behavior

endowed with the adaptive clock.

If we consider the overall system we observe a considerable

advantage in performances in the case of adaptive clocks.

This is illustrated in Figure 6 where we compare the per-

formances in term of activations for different behaviors (i.e.

with different adaptation strategies) considering the overall

8 16 32 64 1280

20

40

60

80

100

120

140

base periodexecution tim

e (

s)

without Internal Clock

with Internal Clock

with Internal Clock without Internal Clocknumber of execution number of execution

pb activations time (s) activations time (s)

8 3 1 8 816 4 5 16 1732 5 12 32 3464 6 25 64 69

128 7 50 128 132

Fig. 5. A comparison between the execution of the RETURN TO NEST

behavior with or without the adaptive clock changing the base period pb.

system. We notice that the adaptive monitoring strategy of

RETURN TO NEST (i.e., with a priori knowledge) produces

better results in terms of number of activations.

VII. CONCLUSIONS AND FUTURE WORKS

In this paper, we explored the feasibility and the effec-

tiveness of a behavior-based robotic architecture where the

behavior activities are modulated by periodic and adaptive

releasers. In particular, inspired by internal self-regulation

mechanisms in living organisms, we introduced internal clocks

to tune and adapt the frequencies of the sensors and activations

associated with the system behaviors. Such mechanisms can

speed up or slow down the period of behaviors activation

and thereby the reading frequency of the sensors according

to both the robot-environment interactions and the interaction

that may arise within the robots itself. To test the model

we implemented a behavior-based control system endowed

with these mechanisms and evaluated different monitoring

strategies. In particular, we developed an application which

simulates the behavior of the Catagliphys ant.

134

Behavior based robotic usually resolves conflicts by deploy-

ing a subsumption architecture or by implementing some con-

trol mechanisms in order to switch between tasks, selecting the

action [16], [17]. For example, in [18] the authors presented a

schema theoretic model for a praying mantis which behaviors

are driven by motivational variables such as fear, hunger and

sex-drive. In this approach, the action selection module selects

only the motivational variable with the highest value. In our

approach, the modulation of behavior is not controlled by an

on/off switch for changing the task. Indeed, the global behavior

emerges from the each single behavior through a rhythmic

controller modulated by a monitoring strategy.

Other authors dealt with this kind of problems. For example,

in [11] the authors presented a parallel architecture focused on

the concept of activity level of each schema which determines

the priority of its thread of execution. A more active perceptual

schema can process the visual input more quickly and a more

active motor schema can send more commands to the motor

controller. However, while in our approach such effects are

obtained through rhythmic activation of behaviors, in [11]

the variables are elaborated through a fuzzy based command

fusion mechanism.

In this research, we performed a preliminary assessment of

an architecture in which the activities of each behavior are

triggered by a clock. In our experiments, we observed that the

number of activations of each behavior decreases strongly, i.e.

the computational overload is lowered. Furthermore, our tests

have also shown interesting results about the synchronization

and the scheduling of the behaviors. In fact, since each

behavior is endowed with its own clock, whose period changes

are based on external and internal conditions, we have that, in

some circumstances, a behavior activated more frequently than

others with the consequence that its influence on the emergent

behavior is stronger than other behaviors. We observed that our

architecture is only partially reactive since we store at each

time the values of the clocks for each behavior and sensor

data. In fact, the mechanism of adaptive clock provides both a

quick reaction to the perceived stimuli and takes into account

the rate of changing in the external environment and internal

states. In other words, our system refers, in some way, to the

history of the rhythms fluctuations caused by the changes of

the environment and of the internal state. This means that in a

pure reactive system we have the same action in presence of

the same stimuli while in our system the action depends also

on previous stimuli.

In future works, we plan to investigate the introduction of

learning mechanism to select the proper rhythms for each

behavior. Moreover, in this work we assumed a setting where

each behavior modulates its own rhythm of activation, how-

ever the behaviors interconnection can be enhanced, in this

direction we plan to investigate how the adaptive periodical

activations of behaviors may influence and constrain each

other.

0

50

100

150

200

250

300

350

AVOID MOVE−TO−GOAL RETURN−TO−NEST

Nu

mb

er

of

Be

ha

vio

r A

ctiva

tio

ns

with Internal Clock

without Internal Clock

Fig. 6. Number of behaviors activations with or without adaptive clocks.

ACKNOWLEDGMENT

The research leading to these results has received funding

from the European Community’s Seventh Framework Pro-

gramme (FP7/2007-2013) under grant agreement no.216239.

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