Page 165 - DCAP405_SOFTWARE_ENGINEERING

Software Engineering

Notes this second category can vary widely. Furthermore, modelling and simulation software
environments provide these services only to varying degrees and consequently, when they are
needed; care must be taken in choosing an environment that is able to deliver the required
services at an adequate level.
The manner in which the support services to augment the simulation model are invoked varies
significantly among software environments. Almost always there is at least some set of
parameters that need to be assigned values in order to choose from available options. Often
some explicit programming steps are needed. Considerable care must be taken when developing
the simulation program to maintain a clear demarkation between the code of the simulation
model and the code required to invoke the ancillary services. Blurring this separation can be
detrimental because the resulting simulation program may become difficult to verify,
understand, and/or maintain. It has, in fact, been frequently noted that an important quality
attribute of a simulation software platform is the extent to which it facilitates a clear separation
of the code for the simulation model from the infrastructure code required for the experimentation
that is required for the achievement of the project goal(s).

Figure 9.11 indicates that a verification activity needs to be carried out in the transition from the
simulation model to the simulation program. This need arises because this transition typically
involves a variety of decisions relating to the execution of the simulation model and the
correctness of these decisions must be confirmed. Consider, for example, a simulation model
that incorporates a set of ordinary differential equations. Most modelling and simulation
programming environments offer a variety of solute on methods for such equations and each
has particular strengths and possibly weaknesses as well. If the equations in question have
distinctive properties, then there exists a possibility of an improper choice of solution method.
The verification process applied at this stage would uncover the existence of such a flaw when it
exists.

Operational Phases

Thus far our outline of the modelling and simulation process has focused on the evolution of a
series of interdependent representations of SUI. However, with the existence of the simulation
program, the stage is set for two operational phases of the process that we now examine. The
first of these is the validation phase whose purpose is to establish the credibility of each of the
model realisations, from the perspective of the project goals.
The second phase, which can begin only after the model’s credibility has been established, is the
experimentation phase, or more specifically, the simulation phase. This activity is presented in
Figure 9.11 as the task of ‘goal resolution’. This is achieved via a sequence of experiments with
the simulation program during which an ever-increasing body of data is collected and analysed
until it is apparent that a ‘goal resolution database’ is sufficiently complete and comprehensive
to permit conclusions relating to the goal(s) to be confidently formulated.

Concept of the Environment

Intuitively, the notion of “the environment’’ in AI and robotics refers to the relatively enduring
and stable set of circumstances that surround some given individual. My environment is probably
not the same as yours, though they may be similar. On the other hand, although my environment
starts where I leave off (at my skin, perhaps), it has no clear ending-point. Nor is it necessarily
defined in terms of metric space; if physically distant circumstances have consequences for my
life (via the telephone, say) then they are properly regarded as part of my environment as well.
The environment is where agents live, and it determines the effects of their actions. The
environment is thus a matter of importance in computational modeling; only if we know what
an agent’s environment is like can we determine if a given pattern of behavior is adaptive. In

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