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In-Depth Information
executes
Algorithm
8 described in the
(SD)
step; and, finally, (vi) the Selecting Plans
function chooses a single applicable plan from the set of options based on their pri-
orities and executes
Algorithm
9 described in the
(SP)
step. The applicability of the
NBDI architecture and its implementation was demonstrated by the developing of the
non-combatant evacuation scenario, presented in section 3.
Algorithm 9 .
Selecting Plan
Require:
fulfilSet
NF: norms stored in the fulfil set
Require:
P: all plans that achieve the selected desire
1:
for all
Norm n in
NF
do
2:
for all
pinP
do
3:
for all
state in p
do
4:
if
(
n.State
==
state
)
∩
(
n.DeonticConcept
==
Obligation
)
then
5: p.annotatePriority(+1)
6:
else
7:
if
(
n.State
==
state
)
∩
(
n.DeonticConcept
==
Prohibition
)
then
8: p.annotatePriority(-1)
9:
end if
10:
end if
11:
end for
12:
end for
13:
end for
14:
return
getPlanHighestPriority()
6
Related Work
Our work was influenced by the architecture proposed in [2]. Such architecture to build
normative agents also contemplates functions to deal with the adoption of norms and
the influence of norms on the selection of desires and plans. However, our work presents
details about the verifications that must be satisfied in order to agents adopt norms, the
evaluations that must be made to select the norms the agents intend to fulfil and vi-
olate and a guidelines to help agents on selecting plans according to the norms they
want to fulfil and violate. The BOID (Belief-Obligation-Intention-Desire) architecture
proposed in [1] is an extension of the BDI architecture that considers the influence of
beliefs, obligations, intentions and desires on the generation of the agent desires. The
BOID architecture applies the notion of agent types to help on the generation of the
desires. Thus, their approach could have been used in the
(SD)
function being proposed
in our paper since this function is the one responsible to select the desires. Instead of
basing the selection of desires on the agent type, we have used the norm contribution
and the priority of the desires (and intentions) to provide a quantifiable solution to the
selection of the agent desire. The approach described in [4] proposes an architecture
to build norm-driven agents whose main purpose is the fulfilment of norms and not
the achievement of their goals. In contrast, our agents are desire-driven that take into
account the norms but are not driven by them. In [6] the authors provide a technique
to extend BDI agent languages by enabling them to enact behaviour modification at
runtime in response to newly accepted norms, i.e., it consists of creating new plans to
