=Paper=
{{Paper
|id=Vol-1989/paper7
|storemode=property
|title=Intelligent software environment for integrated expert system designing and development
|pdfUrl=https://ceur-ws.org/Vol-1989/paper7.pdf
|volume=Vol-1989
|authors=Galina Rybina,Yury Blokhin
}}
==Intelligent software environment for integrated expert system designing and development ==
https://ceur-ws.org/Vol-1989/paper7.pdf
Intelligent software environment for integrated expert
system designing and development
Galina V. Rybina,
doctor of technical science, prof.,
Yury M. Blokhin
National Research Nuclear University MEPhI
(Moscow Engineering Physics Institute),
Kashiskoe sh. 31, Moscow, 115409, Russian Federation,
galina@ailab.mephi.ru
1 Introduction
Today dynamic intelligent systems (DIS) are the most complex class of applied intel-
ligent systems, including real time (RT) DIS. This is one of the most and currently
one of the most relevant and in-demand classes of DIS are dynamic integrated expert
systems (IES) that use dynamic domain and solving dynamic problems [10]. Analysis
of the foreign and domestic level of research and development in the field of DIS, in
particular, dynamic IES, has shown that when creating dynamic IES and relevant
tools (IS), a large number of scientific and technological problems arise related to the
specific features of building both separate components of the IES. And the organiza-
tion of interaction of these components among themselves in the RV. As a whole,
despite the lack of semantic unification of the terminology base, integrated DIS classi-
fications, and their separate classes as well as the circle of general scientific and tech-
nological problems that hinder the wide application of DIS applications in strategical-
ly important subject domains, where the highest effect of using the temporal DIS
occurs, have already accumulated [11-13].
Listed above problems substantially determine the high complexity of DIS devel-
opment as dynamic integrated expert systems that are the most widespread and need-
ed DIS class. Moreover, there is no universal complex method for solving the de-
scribed problems (or a part of them) that implies the development of an integrated
integral methodology and technology for creating such complicated systems at all
lifecycle stages. Modern commercial software to support the construction of most
DISs (G2, Rtworks, RTXPS, etc.) despite its power and versatility, is not able to solve
the above problems in terms of integrated methodology fully [15] and others.
А new stage of developing the theory and technology of IES construction based on
the problem oriented methodology is a valuable step towards IES development auto-
mation. Its main properties are stated in a few monographies [11-13]. Today, this is
the basis that is used to create the intelligent applications and automated workstation
of a knowledge engineer, namely, the AT TECHNOLOGY tool complex, on whose
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basis several tens of applied IESs have been created, where a wide spectrum of mod-
els and methods of solving different unformalized and formalized problems is used in
terms of integrated IES architecture.
A few generations of the AT-TECHNOLOGY workbench have proved efficiency
in the development of more than 20 applied intelligent systems, including dynamic
IES, which, in the context of a problem-oriented methodology, represent the further
improvement of static IES related to working in dynamic environments and domains
and solving dynamic problems (monitoring, diagnostics, planning, control, etc.). Thus
in the architectures of dynamic IES there are components modeling the external envi-
ronment and allowing to interact with hardware in the RT, and also to perform reason-
ing in the changing data and temporal knowledge. In general, this corresponds to spe-
cific results obtained by solving the scientific problems listed above (published in
[10-13], etc.).
The modern version of the AT-TECHNOLOGY workbench enables automated
support of IES development, using methods of intelligent planning and control of
development processes [16,17] etc. As the conceptual basis of intellectual software
technology is the concept of intelligent program environment model. Its complete
formal description and methods for implementing individual components is given in
[10].
The work is focused on the further development of methods and tools for the au-
tomated construction of dynamic IES using components of the intelligent software
environment and taking into account the modern requirements of software engineer-
ing, described in detail in [5] and other works.
2 Intelligent planning methods and their usage for integrated
expert systems development automation
The detailed analysis of modern methods, approaches and software tools used in
the field of intelligent planning is given in [17], so here we consider only the most
important theoretical and methodological aspects of this problem in the context of the
goals and objectives of this paper.
The planning problem formulation described in modern works is usually based on
the basic set of axioms for Labeled Transition System Σ, as [6,3]: finiteness; full ob-
servability; determinism; static; limited goals; plans linearity; implicit time; offline
planning. These axioms impose significant restrictions on the formulation of the plan-
ning problem, and violation of some of these axioms leads to a complication of the
planning task. The formal formulation of the planning problem is given in [3,6]. The
most known approaches to planning were analyzed - graph planning, state space plan-
ning, transition to other problems, etc. [3,6,7].
The problem of planning of prototyping IES is quite well described in terms of
states and transitions. It is necessary to point out that application of intelligent plan-
ning for the automated support processes of building intelligent systems is a poorly
investigated area, and it is possible here to refer mainly to the experience gained in
the creation of applied dynamic IES based on the problem-oriented methodology and
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AT-TECHNOLOGY workbench, in particular, the development and use of educa-
tional and dynamic IES [14]. Let us consider the basic concepts of intellectual AT-
TECHNOLOGY workbench software in more detail. So far, the main applications of
intelligent planning are [1,3,4,6,17,18]: autonomous robots control; logistics and the
resolution of extraordinary events; semantic web; automated tutoring; calibration of
equipment; control of conveyor machines; resource-scheduling; resource allocation in
computer systems.
The classical algorithm A* was chosen as the base for developing algorithms for
generating global and detailed plans [17] used in the prototyping of PECs, was cho-
sen, which is the simplest and sufficiently well-researched, which corresponds to
modern world trends in the development of planners [9]. To reduce the search space,
usually effective heuristic functions is used, which development in modern works on
intellectual planning is given significant attention.
During the researches comparative analysis of universal heuristic functions used in
planners implementations was carried out within the framework of these studies and
using heuristics (relaxed heuristics, heuristics of the critical path, abstract heuristics,
landmark) [2,3,8]: Blind; Relaxation-based maximum; Merge-and-shrink; Admissible
Landmark; Relaxation-based additive; Relaxed plan heuristic; Casual graph heuristic;
Context-enhanced additive heuristic; The Landmark Heuristic et al.
The comparison between different heuristic functions showed that even though the
most powerful heuristic functions (for example, from the landmark class) show rather
high efficiency, they do not give a fundamentally qualitative leap in the issues of
computational complexity of solving the planning problem. Therefore, for specialized
domains such as dynamic IES, it is preferable to use problem-oriented heuristic func-
tions instead of universal ones. They can reduce search space size up to several or-
ders, so in this work, the specialized heuristic function have been developed.
3 Implementation details of basic intelligent software
environment components
Intelligent software environment takes significant place in the framework of the prob-
lem-oriented IES constructing methodology (basic points are reflected in [10]) and
implements intelligent software support for the IES development processes. It is gen-
eral concept of "intelligent environment". Complete formal description of the intellec-
tual environment model and methods of the individual components implementation is
presented in [10], so here only a brief description of the model in the form of quater-
nion is presented: MAT = , where KB is a technological knowledge
base (KB) on the composition of the project, and typical design solutions used in de-
velopment of IES. K - set of current contexts Ki, consisting of a set of objects from the
KB, editing or implementing on the current control step. P – a special program - an
intelligent planner that manages the development and IES testing process. TI - many
tools TIi, applied at various stages of IES development.
Intelligent planner is the main procedural (operational) component. It is defined as
P = , SK here is the state of the current context, in which the
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scheduler was activated. AF is a set of functional modules, a part of planner. Pa is a
selection procedure for the current target based on the global development plan. Pb is
a selection procedure for the best executive function module from the list of possible
candidates. I - procedures to ensure the interface with the corresponding components
of the AT-TECHNOLOGY workbench; GP - operating procedures for the IES global
development plan.
The main technological knowledge unit is a standard design procedure (SDP),
which can be represented as tuple SDPi = where C - is the set of conditions
under which the SDP can be implemented; L - script implementation described in the
describing internal language actions of the SDP; T - set of parameters initialized by
intelligent planner at SDP inclusion in the development plan of a IES prototype. De-
scription of other intelligent software environment can be found, for example in [17].
Now let us state methods and approaches used in the implementation of the intel-
lectual supportive environment for the development of IES model. The main compo-
nents of this IES are the technological KB on the composition of IES project, SDP
and RUC, and the intelligent planner managing the process of plans construction and
implementation for the development of IES prototypes. These are the main purposes
why it is necessary to use different types of knowledge in the process of developing a
IES prototype: checking referential integrity of the project on the development of IES;
automated construction of components diagrams; layout synthesis of IES prototype
architecture; planning a series of steps to create a prototype of IES-specific features
and tasks; determining a set of the most relevant sub-tasks for each of the stages
(steps) in the development of IES prototype and others.
The main task of intelligent planner is a dynamic support knowledge engineer op-
erations at all life cycle stages of building. Dynamic support is done by generating
IES development plans for the current IES prototypes and allowing the specific plans
execution (made either automatically or interactively). It should be noted that detailed
plans and global IES prototyping generation and architecture model synthesis is based
on the integration of IES with planning methods.
The proposed method is based on state-space planning which generates plan ap-
plied IES architecture model and the SDP set. The architecture model IES contains a
set of elements that need to be realized and depending on the type of element and its
content, different RUCs can be used for implementation, resulting in various compo-
nents of the IES prototype project. Moreover, the use of knowledge from the techno-
logical KB (containing SDP and RUC) allows the implementation of several architec-
ture mode components together with the application of the appropriate RUC. Four
special algorithms have been developed to implement the proposed method. With the
help of the first one, the model of the prototype architecture of the IES [10] is prepro-
cessed by converting architecture model in the form of a hierarchy of extended data
flow diagrams (RDPD) into one generalized diagram by recursive detailing of com-
plex operations (example is shown in Fig.1. as a uncovered graph).
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E2
SDP1 Op4 S1
NF2
Op3
SDP2
Op2 E1 Op1 NF1
NF3 E3
SDP1 S2
Fig. 1. Detailed coverage
The aim of the second algorithm is to search the coarse coverage of the generalized
diagram (Fig.1, solid bound). In this paper, a multiple of SDP fragments is meant to
cover a diagram, between elements of which a one-to-one correspondence with ele-
ments of a generalized diagram is established. Under a coarse coverage is meant a
cover only by necessary fragments of a SDP.
It is performed in the following manner. At the first step, all the vertices of the dia-
gram are marked with a number 0, and an empty set is also initialized, into which the
activated SDPs will be added. Next, for each SDP from the technological KB, the
possibility of covering this necessary SDP fragment on a fragment of a generalized
diagram consisting of elements marked with zeros is checked. For the SDP in the set
of activated SDPs, an instance of the corresponding SDP is added, and the covered
elements are marked with the identifier of the corresponding instance. The process of
generating coverage continues as long as there are SDPs in the technological KB,
which can be covered with zeros of the generalized diagram. In this case, the same
SDP can be activated several times in the form of different instances.
The third algorithm allows to generate detailed coverage (example is shown in
Fig.1, where optional fragments bounded with dashed lines), which means covering
all available SDP fragments from the set of activated SDPs. To do this an instance of
the activated SDP is selected with attempt to cover the diagram elements marked with
0 using each of the optional SDP fragments, taking into account their links (data
flows) with the required SDP fragment. The covered elements of the diagram are also
marked with the identification number of the SDP instance. The algorithm is termi-
nated when there are no optional SDP fragments that can extend the existing cover-
age.
The coverage generating with second and third algorithms are implemented with
using heuristic state space search, based on the classical algorithm A*. Generating of
new states is associated with the coverage of the diagram by each new fragment (nec-
essary and optional respectively). The fourth algorithm is designed to convert the
325
detailed coverage into a plan, with a set of planned tasks associated with the use of
certain operational RUCs from each coverage fragment.
The reason for development of specialized algorithms is due to the violation of the
axiom associated with setting constraints on the complexity of describing the goal
state of the system. The generating of a global plan involves special search metric. To
construct a coarse and accurate coverage, two specialized heuristic functions are used,
which were developed on the basis of the experience of prototyping IES of different
architectural typologies.
4 Conclusion
Now various methods of intelligent planning, algorithms and heuristic functions have
been experimentally evaluated for the further development of the intelligent planner
efficiency of AT-TECHNOLOGY workbench. As a result the following were com-
pleted: model of IES prototype development plan (decomposed to the global and de-
tailed plans), as well as concretization of individual components of SDP model (sce-
nario and conditions); the original method for IES prototype development plan gener-
ating based on heuristic search is developed as well as algorithms for its implementa-
tion, including specialized heuristic function.
On the example of development of two prototypes of dynamic IES for the Russian
Center for Disaster Medicine "Zaschita" ("Management of medical forces and means
for major road accidents" and "Management and monitoring of resources of the satel-
lite communication system between regional centers"), software simulation of meth-
ods for development of particular components of dynamic IES prototypes of various
architectural typology. Their design and development stages are characterized by high
laboriousness and intellectual load on knowledge engineers.
The work was supported by the Russian Foundation for Basic Research support
(project № 15-01-04696) and the MEPhI Academic Excellence Project (agreement
with the Ministry of Education and Science of the Russian Federation of August 27,
2013, project no. 02.a03.21.0005).
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