Information Technology Reference
In-Depth Information
Following the example above, if the weather of the area operated by one of the two
rescue entities is bad, both norms 2 and 3 are activated, since the activation condition of
both norms is “The weather is bad”. If the norms are activated, the rescue entity must
not rescue NGO members and must not use helicopters. Both norms are deactivated
when the expiration condition unifies with the information about a good weather stored
in the agent's belief base.
4.2
Norm Selection Function
The main goal of the
Norm Selection Function
(Figure 3) is to select the norms that the
agent has the intention to fulfil. In order to do this, such function performs two tasks:
1)
Evaluating Norms
and 2)
Identifying and Solving Conflicts
. The first task helps the
agent on selecting, from the set of activated norms, the norms that it has the intention to
fulfil and the ones it has the intention to violate. The function evaluates the benefits of
fulfilling or violating the norms, i.e., it checks how close the agent gets of achieving its
desires if it decides to fulfil or if it decides to violate the norms. The function groups the
activated norms in two sub-sets: norms to be fulfilled and norms to be violated. Finally,
the second task of this function identifies and solves the conflicts among the norms that
the agent has the intention to fulfil and among the ones that the agent has the intention
to violate.
Evaluating the Norms (EN).
In order to evaluate the benefits of the fulfilment or vio-
lation of a norm according to the agent's desires and intentions, the steps below should
be followed:
(Step 1)
In case of obligations, it checks if the state described in the norm
is equal to one of the states that the agent has desire (or intention) to achieve. In affir-
mative cases, the contribution is positive and the function
g(n.DeonticConcept, n.State)
returns a value indicating the level of norm's contribution that is calculated according
to the priority of the desire that is similar to the state described by norm. The function
receives as parameters
n.DeonticConcept
representing the deontic concept type, i. e.,
obligation or prohibition, and
n.State
representing the state that is been regulated. In
any other case, the contribution is zero since it does not disturb the achievement of the
agent's desires or intentions. Such step is represented in
Algorithm
3 from line 2 to 8.
(Step 2)
In case of prohibitions, it checks if the state described in the norm is equal to
one of the states that the agent has desire ( or intention) to achieve. In affirmative cases,
the contribution is negative since it disturbs the achievement of the agent's desires or in-
tentions and the function
g(n.DeonticConcept, n.State)
calculates the absolute value of
the contribution. In any other case, the prohibition will contribute neutrally. Such step is
represented in
Algorithm
3 between lines 9 and 15.
(Step 3)
After analyzing the state be-
ing regulated, this step considers the influence that the rewards have to the achievement
of the agent's desires. We consider that rewards can never influence the agent negatively
but always positively or neutrally since they give permissions to achieve a set of states.
Such step is represented in
Algorithm
3 by line 16. Function
r(n.Rewards)
verifies the
desires (or intentions) that are equal to the rewards and returns a value indicating the
contribution that is the sum of the priorities of the agent's desires benefited by the re-
wards.
(Step 4)
Finally, the punishments are evaluated in order to check if they will
