constructing NL interfaces for expert systems. 
While interacting with the user the IDEAL system 
provides the sufficient linguistic universe for 
comprehending, interpreting and translating NL 
phrases to a knowledge representation. The 
environment is capable of mapping a domain 
knowledge to a transformation language, such as, 
a Geometric Transformation Language (as used in 
CAD systems, Computer Graphics, etc), a Digital 
Image Processing Language as in Batchelor (1986), 
a Digital Signal Processing Language for digital 
filters sinthesis and/or design as in Nie et ali 
(1991), etc. 
152 Generalities 
The solution to a domain problem always involve a 
great quantity of knowledge which sometimes is 
not well organized to provide the best solution, 
That happens in all of the application domains 
and so it is not different in the Digital Image 
Processing Domain, specially when dealing with 
radar or multiespectral satellite images that 
have much information, but yet are not well 
interpreted. One of the problems arises from the 
fact that when using Digital Image Processing 
Software, the domain experts dedicate much of the 
time to learn how to operate the systems to 
choose procedures and provide parameters, 
Corr et ali (1989) present a system for automatic 
knowledge-based segmentation of remotely sensed 
images. Time sequences of remotely-sensed data 
information are used together with cartographic 
map data and domain expertise in modelling the 
scene in terms of segments and their possible 
classes. Srinivasan and Richards (1990) use 
knowledge-based procedures to provide a new 
scheme for incorporating several knowledge 
sources in the classification process, Silva and 
Bittencourt (1991) considers the use of several 
knowledge representation techniques to represent 
the knowledge involved in interpretation of 
meteorological radar images. The knowledge 
Sources are represented using frames, semantic 
networks, and/or production rules representation 
schemes within a blackboard architecture that 
permits different knowledge sources share the 
same data, simultaneously or sequentially. In 
this system the weather is monitored by the radar 
and the images are numerically processed before 
the representation, In critical cases the system 
activates an alarm calling for experts 
interference. 
There exist many knowledge-based systems that try 
to solve specific Digital Image Processing 
problems specially those related to automatic 
Image Interpretation. In all of them, however, 
the MMI is not efficiently achieved, once the 
user (domain expert) is required to participate 
through out the whole process. And then, the 
tasks are not automatically performed from the 
user's point of view. The decisions are always 
taken by the user that must be an expert in the 
application domain and the systems used.The aim 
of this work is to provide an environment through 
which the systems may be well used to solve 
problems in a specific domain, Using NL to 
communicate with the system the user may specify 
his intention, avoiding unnecessary computation, 
and the tasks are only performed after the 
correct procedures and parameters specifications, 
Therefore, the tasks are automatically performed 
from the user's point of view. 
380 
The proposed environment translates the user's 
query to a tranformation language, that is, the 
environment maps the query to the procedures 
specification to be adopted for task execution, 
The parameters are still provide by the user. But 
the environment friendly requests them, sometimes 
explaining the use of the parameters and giving 
examples, This characterizes a user training 
process through the system that can detect any 
specification fault before the execution process. 
That happens because the domain knowledge is also 
represented and so environments like this may be 
used in education tasks in remote sensing. 
The greatest advantage of using NL in intelligent 
environment is that instead of dedicating time to 
learn how to specify procedures and parameters to 
efficiently use software systems, the user can 
dedicate his time searching for new problem 
solution estrategies and methods, thus enhancing 
the solution space, 
2. SYSTEM DEVELOPMENT 
2.1 Environment Architecture 
  
This work does not focus on the knowledge 
representation of remotely sensed images. This 
has been studied by several researchers as 
mentioned in section 1,2 and can be adapted in 
the case of the digital image processing domain 
or any other domain. The objective of the work is 
to improve MMI in software usage. That is, turn 
parametric and difficult to use systems into 
friendly environment, With this kind of system 
the user may specify his queries using his own 
language within the restricted domain. 
The environment developed is composed of modules 
for linguistic interpretation, translation to the 
set of actions, task execution, and graphic 
representation. The linguistic and domain 
knowledges are represented using Minsk's frame 
representation conception as in Winston (1975), 
that permits stereotype representation such as 
the description of an object (e.g., a chair) or a 
situation  (e.g., going into a classroom). These 
knowledge sources are used within a blackboard 
approach, so that, knowledge sources responsible 
for inference rules may share the same data, 
searching for the solution to the specified 
problem, see figure l. 
24.2 Environment-Description 
  
The application domain considered is restricted 
to 2D computer graphics where it is possible to 
perform some tasks on regular geometric objects 
such as rectangles, triangles, squares, circles 
and lines, Examples of tasks are translation, 
scaling, color alteration and rotation. To 
implement the frame representation of this domain 
a frame language was used based on the one 
proposed by Arariboia (1988), 
The prototype was developed in the C language for 
graphic presentation and PROLOG for knowledge 
representation and inference rules 
implementation. Both languages share a knowledge 
source where the graphic status is represented. 
As the application domain changes the inference 
rules and the knowledge necessary may also 
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