![](bg5.png)
Finally, if the task trade-off is in favor of the new task,
then the willingness will increase, otherwise it will
decrease.
Agents are assumed to start their operation with
predefined values hδ
0
, γ
0
i, and corresponding chang-
ing steps h∆δ, ∆γi. Moreover, they are equipped with
a set of abstract sensors, actuators, motors, knowledge
and abilities. Also, there are no restrictions assumed
as to whether they change in time, e.g. an agent could
have an initial minimal amount of knowledge which
expands during the operation through learning, or the
agent might be updated to support new abilities and
so on. Agents are assumed to be able to compute a
risk factor for the environment they operate in and for
each other agent in that environment. In addition, they
are able to compute a performance measure for them-
selves. An agent can generate a task t
i
to do, or can
receive a request from another agent for another task
t
j
. The task comes with specific execution require-
ments such as: estimated amount of energy required,
equipment, knowledge, abilities, tools. It is worth
noting that the functions used to determine each of
the variables that affect willingness may vary. For the
purposes of this paper, they are computed in simple
terms (Section 4). Currently the following limitations
hold. (i) The degree to which an agent has an ability,
or a resource is not considered, i.e. these properties
take binary values. (ii) An agent checks if it needs
assistance at the beginning of each task. (iii) A task
is simulated as an atomic step, as such the trade-off
between tasks is not simulated in the current work.
4 SIMULATIONS
The behavior of the agents was investigated through
computer simulations. One trial was conducted, and
is composed of two separate computer simulations,
referred to as phases. In the first phase, the values of
δ and γ are static throughout the whole time. Sim-
ulations are repeated for the following combinations
of hδ,γi: h0.0,0.0i h0.5,0.0i, h1.0,0.0i, h0.0,0.5i,
h0.5,0.5i, h1.0,0.5i, h0.0, 1.0i, h0.5,1.0i, h1.0,1.0i,
h0.2,0.2i h0.5, 0.2i, h0.8,0.2i, h0.2,0.5i, h0.8,0.5i,
h0.2,1.0i, h0.5,0.8i, and h0.8,0.8i. These values are
chosen as representatives of extreme and average self-
ish/unselfish agent behavior. In the second phase,
their values will change during run-time according to
the scheme shown in Figures 2 and 3 with ∆δ = ∆γ =
0.05. Simulations are repeated for all combinations
of hδ
0
,γ
0
i: h0.0,0.0i h0.5,0.0i, h1.0,0.0i, h0.0,0.5i,
h0.5,0.5i, h1.0,0.5i, h0.0, 1.0i, h0.5,1.0i, h1.0,1.0i,
h0.2,0.2i h0.5, 0.2i, h0.8,0.2i, h0.2,0.5i, h0.8,0.5i,
h0.2,1.0i, h0.5,0.8i, and h0.8,0.8i, in which δ
0
and
γ
0
are the initial values for δ and γ. Moreover, for the
second phase of simulations, two update strategies for
δ and γ are investigated. In one strategy, the values
for δ and γ will always be calculated from δ
0
and γ
0
.
In the other strategy, the values for δ and γ will be
calculated based on values calculated on the previous
interaction.
All experiments are repeated for three levels of
difficulty. Difficulty refers to the probability (P
D
) that
an agent lacks any of the abilities, knowledge, equip-
ment, or tools. The higher this probability, the higher
the chances that an agent will ask for help. The val-
ues taken for P
D
are 0.2,0.5,0.8, which means that
for P
D
= 0.2, the probability that the agent will ask
for help is 0.2, and the difficulty of the simulation is
low. The size of the population is 30, and remains
fixed across phases.
Each simulation runs for circa 20 minutes. Within
this amount of time, the agents are able to attempt and
complete a considerable amount of tasks, in the range
of hundreds. Consequently, it is possible to identify
a trend for the number of tasks completed over those
attempted. This choice was considered adequate for
the purposes of this paper. Naturally, it is possible to
let the agents run for a longer time.
Each agent is composed of a set of ROS (Quigley
et al., 2009) nodes. The communication between
agents happens by broadcast (when agents make
themselves known to each other), and by a tailored
action-server mechanism (when agents make one to
one help requests to each other). All agents in the
simulation start in their idle state. Each time they are
in this state, a task could be generated with a proba-
bility P = 0.6.
In the current state of the implementation an agent
reasons at the beginning of each task on whether it
needs assistance. If it does not, then it is supposed to
always succeed by itself. Moreover, the execution of
each task is simulated by having the system pause for
a specific amount of time ∆t which corresponds to a
predefined completion time of a task. This is a sim-
plified scenario that was deemed adequate for the pur-
poses of the simulations described here. The success
criterion for the static case is defined as follows. An
agent fails when it attempts a task while lacking any
of the following internal resources: abilities, knowl-
edge, battery, equipment, and tools. Otherwise it will
succeed. The presence of any of the abilities, knowl-
edge, equipment, and tools, is decided by the diffi-
culty of the simulation.
All agents start with the same level of battery
b = 4000 of energy units, which decreases after every
finished task by the amount of energy required by that
task. If b becomes lower than a threshold b
low
= 300,
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