=Paper=
{{Paper
|id=Vol-2258/paper19
|storemode=property
|title=Modeling of Communication Processes in Information Systems
|pdfUrl=https://ceur-ws.org/Vol-2258/paper19.pdf
|volume=Vol-2258
|authors=Gennady Vinogradov,Alexey Prokhorov
}}
==Modeling of Communication Processes in Information Systems==
https://ceur-ws.org/Vol-2258/paper19.pdf
Modeling of communication processes in information systems
G P Vinogradov1 and A A Prokhorov2
1
170023, Tver State Technical University, Marshal Konev St., 12, Tver, Russia, E-mail:
wgp272ng@mail.ru
2
170024, Research Institute “Centerprogramsystem”, 50 let Oktyabrya Ave., 5, Tver, Russia, E-mail:
info@cps.tver.ru
Abstract: The limitations imposed by the general logical and mathematical decision-making
abstraction of on decision-making procedures reduce the science of decisions to a set of
mechanical decision-making methods. In practice, system researchers consider people as objects
that mechanically react to input stimuli. This eliminates the possibility of choice management.
In this regard, the problem of the development of the theory of choice by ensuring the
compatibility of the theological approach and the approach based on causation is currently
relevant. In respect to the behavior theory, this approach requires the inclusion of the theory of
choice of psychological aspects in the subject area. Primarily, these are the problems of
perception, awareness, understanding the properties of the situation of choice and evaluation of
the results of choice, communication, conflict, and a number of others.
Key words: subjectively rational choice, communication processes, software agents,
information system
1. Introduction
In practical programming, an agent is a computer-generated system that is located in some environment
and is designed for autonomous actions in this environment in order to achieve specified goals. There
are some known limitations preventing the widespread use of MAC technologies that use the BDI
architecture of an agent. The desire to bring the concept of building agents in accordance with modern
requirements is currently associated with using biological principles and searching for analogs in
wildlife and human society. Thus, the papers [1–4] identify and describe components and observed
parameters of purposeful behavior using the formalism of fuzzy systems. In [5-10], the authors consider
the problem of a subjective rational choice related to satisfaction of needs, execution of obligations and
the system of values and norms. Such form of choice makes individual purposeful behavior possible. In
addition, it makes it possible to describe behavior as a process.
Observation over it allows identifying individual steps – subprocesses; their combination of
interactions forms a system. An important subprocess in such system is formation of vision of the
situation of choice. They should be considered as models. Their form of description can be different:
textual, mathematical, verbal, etc. [8]. They are the basis for the forecast of possible states from the
selected modes of action, as well as for estimating the probability of their obtaining and the degree of
desirability [10]. Formation of vision is based on the processes of obtaining information from the
environment. The main channels are: communication with other subjects, internal dialogue, observation.
Three main processes – perception, awareness and experience – together give the concept of a situation,
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generate new concepts, change and supplement the list of beliefs (knowledge base). This is the basis for
forming a model (vision) of the situation of choice. The driving factor of behavior is affective evaluation
of the realization of needs and capabilities. Assessments of the current situation cause a state of
satisfaction/dissatisfaction in a subject, who characterizes them by feelings and attitudes. The agent
selects from the known set of methods and opportunities those that allow him to reach the state of
satisfaction rationally.
The model of agent's subjectively rational choice of ways to meet the needs and fulfill obligations is
based on the methodology of gradual strengthening of agents' capabilities. The latter assumes that in the
process of searching for ways to meet the needs, the agent gradually builds up a variety of ways do this
[11]. Here he uses his experience and experience of other agents he interacts with, as well as the available
knowledge.
In most models of agents whose behavior is described by sociological and psychological concepts,
communication is defined in terms of protocols that do not have a direct connection with the first. It
creates problems in developing and modeling communication processes between agents, as well as
operators with agents. This is due to the fact that the description of agents’ autonomous behavior
contains high-level abstractness formalism, communication is defined in terms close to implementation.
The difference in description levels does not allow simulating communication between agents at the
same level as their autonomous behavior is described.
2. The statement of the research task
In existing information systems (which include software agents), communication is based on the so-
called command interface. It is implemented through dialog boxes, menu systems, etc. When artificial
entities communicate, the main problem is creation of message formats and ways of organizing
information channels. Complication of solved problems leads to the necessity of organizing an interface
by artificial entities between themselves and an operator based on some “professional language” that is
relevant to some subject area. This involves considering an interface as a sign system.
Let an interface language phrase describes some situation S, which has a meaning and a name. This
phrase might have the mode of action A from a set of possible ones. Its implementation puts the
environment into a new state. It is clear that a “correct” interpretation of environment perception results
will lead to a “correct” choice of a mode of action. This means that between S and A there should be an
information model of a domain that describes and allows determining relationships between stimuli and
system reactions. Consequently, autonomous behavior of an agent as an information system involves
development and integration of conceptual models of subject areas into its information systems. The
main purpose of conceptual modeling is the formalization of accumulated knowledge about a certain
subject domain in the form closest to understanding by participants in the communication process. In
practical terms, the most promising direction of conceptual modeling is ontological modeling.
Ontologies are explicit formal specifications of domain terms and relationships between them. Models
of ontologies used in the theory of agents are almost equivalent to the concept of language semantics in
a certain subject domain common in programming.
The rules of ontology specify statements that relate concepts and relationships between them.
However, these models have a number of limitations. For example, the relationship between concepts
may be concepts themselves. Overcoming this shortcoming is associated with using conceptual
modeling of a subject domain.
The approach supposes that association is also a concept rather than a named type of connection,
which sets a role or relation in a model. In addition, the language of the conceptual model does not
contain means for describing relations and roles, since the conceptual structure containes all the rules
necessary for derivation.
This allows increasing the level of model abstraction and developing a small number of general
algorithms with a small computational complexity that do not depend on the subject domain, since they
are formulated in the most general operations on concepts [12, 13].
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3. Solving the problem of communication based on ontologies of concepts
There are four types of concepts: single, simple, specific and abstract. Single concepts are the result of
a mental selection of unique entities in a subject domain and assigning names to them. This allows
replacing the entity with a sign that is identical to it in some sense. Simple concepts are formed by
combining entities similar in some sense. They are assigned a unique name, considered as a single
concept, and acceptable manifestations (values) range is defined, considered as a set of single concepts.
Specific concepts are based on a union of entities with the same characteristics, which allows defining
a set of entities that correspond to a notion. The features to be distinguished are considered as simple
concepts, and the names of concepts are considered as single concepts. A particular concept is
represented by a set of entities that form its extensional or volume. The name of a concept is a sign
expression of a concept with an attributed meaning. The scheme or structure of the concept is set by a
set of characteristics that characterize the concept. The intensional or content of the concept is
considered as a set of values of interrelated characteristics that allow recognizing the entities belonging
to the concept. At the same time, entities should be considered as some fragments of a subject domain
represented by signs, symbols, images, etc. In order to manipulate entities in an information system,
they must be called and treated as single concepts.
Formation (determination) of abstract concepts includes using complex forms of abstraction based
on the establishment of relations of independence, differentiation and integration of features between
the concepts. To form abstract concepts, there four abstractions: generalization-specialization, typing-
concretization, aggregation-decomposition, and association-individualization.
The process of studying a subject domain supposes formation of new concepts or identification of
existing ones. In this case, from the perspective of solving a certain problem, there are entities that have
or are assigned with certain names. Further, the set of identified entities is analyzed in order to define
their similarities and differences. Similar entities are grouped and form concepts or fill existing concepts
with problematic content.
Well-known formalisms define a lot of relationships of different nature on concepts. Unlike them,
here there is another formalism – a conceptual structure that is defined by a set of concepts with four
kinds of mappings, the only purpose of which is to show ways of concept formation, ways of abstracting
[12, 13].
Definition 1. A conceptual framework S = (N, T, G, C, A) is a finite set of concepts N with four finite
sets of mappings in the form N N: type checking T, generalization G, aggregation C and association
A.
A conceptual structure is designed to reflect the results of conceptual analysis of the subject domain
in a formalized form, and expresses displaying some concepts into others. The abstractions used here
are considered as mental operations that are necessary and sufficient for the mental isolation and
transformation into separate concepts of representations that are accumulated relative to a formalized
domain.
The definition of the concept assumes that each concept has a schema, which is a set of features or
simple concepts to define this concept. However, when defining a conceptual structure, only displaying
some concepts into others is to be defined. The problem arises of defining schemata of concepts.
A scheme can be found for any concept from the conceptual structure S according to the following
recurrent procedure:
a scheme of a single concept is empty;
a scheme of a simple concept consists of the name of this concept;
a scheme of a differentiation concept is equal to the marked intersection of schemes of
differentiated concepts;
a scheme of an integration concept is equal to the marked integration of schemes of integrated
concepts;
a scheme of the concept obtained after integration and differentiation is equal to the marked
unification of schemes of integrated concepts belonging to the marked intersection of schemes
of differentiable concepts.
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The ability to define schemes of abstract concepts allows displaying them as certain concepts as a
list of entities that are defined through primary notions of the subject domain.
To verify a conceptual structure, it is required to check the computability of the schemes of all its
concepts. The requirement of computability of concept schemes is the extension of a set regularity
property to concepts. In this case, the computability of concept schemes guarantees the absence of
concept definitions through themselves, which is inadmissible in any formal or substantial theory that
claims to be adequate.
Thus, a conceptual analysis of the subject domain provides its ontological description given by a
conceptual structure. The principal differences of the conceptual structure from other conceptual
schemes are the following:
terms are not divided into meanings, signs, concepts, connections and roles; instead, there is one
term – a concept with particular manifestations such are meanings, signs, concepts, connections
and roles;
the possibility of representing associations as independent concepts, which allows, for example,
expressing the generalization of associative links;
the definition of concepts that can be both generalization and the association of other concepts;
semantic description invariance, which does not require subject knowledge for its interpretation.
A conceptual structure of a subject area assumes assigning concepts and their abstraction methods.
A concept scheme can be also computed. It is a set of simple concepts (attribute concepts) that allows
considering abstract concepts as specific ones. There is a concept intension to determine specific sets of
simple concept meanings that define a particular essence of specific and abstract concepts. It sets the
rules for correlating the sets of simple concept meanings with a particular entity from the extensional of
the concept described.
Definition 2. A conceptual model M of the subject domain is its conceptual structure S that is
supplemented by a description of intensionals D of all concepts in it, M = (S, D).
From the above reasoning, it follows that the universal form of assigning an intensional is
enumeration of attribute sets of each entity belonging to the concept. In practice, it causes difficulties
when the scope of the concept is sufficiently large. Another form of describing intensionals is also
possible – the use of procedures (formulas, functions) that resolve the extensionality of the defined
concept.
This means that a low-level representation of the conceptual model of the subject domain can be
solved by means built into the database management system. To set simple concepts (attribute concepts),
we might use integrity constraints that narrow the ranges of simple data values, or special tables
containing valid values of a simple concept. Specific concepts are represented by tables with columns
corresponding to simple concepts from its scheme.
Representation of an abstract type concept is also complicated by using a query to a database that
processes records from several tables with the same set of fields simultaneously. The implementation of
an abstract generalization concept requires a separate table with intensions of entities that are absent in
the extensions of generalized concepts. Therefore, that the abstract generalization is represented by a
query to the database, in which all records from the generalization concept table and unique entries from
tables of generalized concepts with a set of fields belonging to the generalization concept scheme are a
subject to processing.
Aggregation of concepts is represented by a query that executes the product of two or more tables.
However, the implementation of the abstract association concept cannot be reduced to a query to a
database and requires allocating a separate table that connects records from two or more associated
concept tables.
Therefore, when using database management systems, the main tool for describing concept
intensionals is the enumeration of sets of interrelated characteristics in the form of a physical or virtual
table. A physical table is stored in memory. A virtual table is generated dynamically due to executing a
query to a database. In this case, a table row (record) corresponds to the described entity from a concept
extension, and the column (field) corresponds to a concept sign.
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The use of a conceptual structure that specified in the form of a tree, as well as mapping of entities
from concept extensionals in the form of lists are sufficient in the context of functioning of an
information system designed to automate decisions of a specified class of applied problems.
If we abstract from a specific content of actions and procedures in the algorithms for solving applied
problems, we can conclude that all such actions might be reduced to three abstract operations on
concepts: concept creation, change and removal. Thus, a description that consists of three elementary
operations [12, 13] is a semantically invariant form of describing a solution of applied problems in a
modeled subject domain.
Concept creation. The operation of concept creation arises when a conceptual model of a subject
domain is complicated. It includes determining a name of a new concept and setting a method for its
abstraction. Calculation of a new concept schema follows automatically after its creation. Depending on
the concept type, either a table (for simple and specific concepts, as well as for abstract generalization
concepts and association concepts) or a representation/query (for abstract typing concepts and
aggregation concepts) that can enumerate entities belonging to a created concept is created in a database.
Removing concepts. The operation of removing a concept arises in the case of changing visions of
a subject domain. It consists in changing a description of all concepts in definitions of which include it.
Concept change. The operation of concept change is used when it necessary to fill concepts with a
specific subject content. Change operations affect specific and simple concepts. In this case, three
actions are possible: editing an existing entity (record), deleting an existing entity (record), and adding
a new entity (record). The same actions are also performed on abstract generalization concepts and
association concepts that have their own tables.
To implement the operations of creating, deleting and changing concepts in a database management
system, there might be special stored procedures that ensure integrity and consistency of a conceptual
model of a subject domain.
Inferences. We might defined any reasoning as a transition from one or more judgments that are a
premise of an inference to a statement that is the consequence of an inference. Rules for constructing
inferences are based on the inference rules generally valid in a subject domain, i.e. generating true
statements under all possible assumptions. The rules for constructing inferences in the case under
consideration are based on inference rules that are formalized in the conceptual structure of the subject
domain. The conceptual structure itself is considered as a formal theory that preserves the truth of all
consequences in it.
The following logical statements specify inference rules on knowledge [13]:
Ni ( E ) N ( E ) Ni ( E ) N ( E )
(Ni N (Ni N
(1)
N (E) Ni (E) N (E) Ni (E)
(Ni N (Ni | N
(2)
where () is a logic connective OR (AND); > ( , , | ) is a mapping generalization sign
(association, typing, aggregation) of concepts; is a generality quantifier; is a logic connective of
following.
The inverse proposition of the first inference rule from (1) is true only for the abstraction of
specification typing (second rule), since the specialization generalization abstraction describes a larger
number of entities than in the union of extensions of generalized concepts.
In turn, the inverse proposition of the inference first rule from (2) is true only for the abstraction of
decomposition aggregation (second rule), since the abstraction of the individualization association
describes a smaller number of entities than in the Cartesian product of associated concepts extensions.
Queries. To turn an information system into a complete knowledge base, it is necessary to implement
queries for extracting facts (assertions) and outputting meaningful statements about the modeled
domain.
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Facts (assertions) and statements are propositions with logical connectives AND, OR, NOT with two
types of predicates [13]:
one-place membership predicates of the entity E to the concept N of the type N(E);
relations P[ E] V , where P[E] is a functor that returns the value of the attribute concept P of
the entity E, is a relation sign (=, , , ,, , > etc.), V is some value concept.
After setting restrictions on one or more characteristic values, the information system searches for
entities that satisfy the specified conditions and then displays them as child nodes of the search node.
The search for concept essences by the key is performed in a similar way after entering the key or its
part in the name of the search node. If the key has several signs, then they are separated by a special
sign.
A more complex search requires using queries to the knowledge base, so there is a corresponding
query language and a knowledge output machine supporting it, which implements inference rules (1)
and (2). It should be noted that for many application problems it is sufficient to use a client application
subject line to find concepts in their conceptual structure by their names with the subsequent search for
the required entities in the extensions of the found concepts.
4. Knowledge representation in a communication process
In addition to knowledge presentation, extraction and actualization, another important task must be
solved – knowledge representation. It consists in changing the form of knowledge presentation. It is
based on construction of conceptual submodels with their subsequent visualization by special programs.
Definition 3. A submodel M ' of the concept model M = (S, D) is a conceptual model M ' = ( S ' , D' )
with the following relations: S ' S , D' D , where S ' is some substructure (fragment) of the concept
structure S, D' is a description of concept intensionals in the conceptual structure S ' [13].
A conceptual submodel is constructed according to the following procedure. First, a number of basic
concepts is identified, which must be included in the submodel according to the conditions of the
problem being solved. Then the conceptual substructure is iteratively constructed. It includes all
concepts having connections with the initial and then with the current set of them. Iterations finish when
a current set of concepts ceases to replenish itself. At the end of the procedure, a description of
intensionals of the concepts from the conceptual substructure is created.
The construction of conceptual submodels is necessary to create data that is required for visualizing
subject domain fragments by third-party programs. To display such submodel, the forms implemented
by the corresponding application programs might be used: Gantt charts, resource lists, resource
scheduling, figures, slides, videos, etc. For this purpose, an information system includes a module which
performs their visualization based on their conceptual models.
Submodels might be used to automatically create various kinds of documents (files). In this case, the
submodel has expression rules (representation) of concepts in a document body. The expressive means
for such representation will depend on the required display form (text, graphics, sound, animation, etc.).
To display a submodel in text form, representation rules might be made in the form of a document
template. Template creation involves using a special markup language that allows specifying the forms
of concept expression in the text.
The extraction operator allows retrieving and inserting into its location a formatted value retrieved
from the conceptual model according to the specified path. The calculation operator is used to represent
a formatted value of some calculated expression in the text form. The syntax and semantics of
expressions are like those of high-level languages. In the implemented information system, interpretive
languages such as VBScript and JScript might be used as languages for specifying expressions.
The setup operator serves to change values in a conceptual model and additionally might be used to
create temporary simple concepts (variables). The selection operator is necessary for implementation of
text branching in the model representation process. The iteration operator represents composite
concepts. Operators can be interleaved, since all their parts are a plain text.
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Submodels of other stable fragments of subject domains and their corresponding visualization forms
have a similar way of creation. For example, charts and diagrams; infographic (graphical representation
of charts, maps, figures, formulas, etc.); technical graphics (graphical representation of schemes,
drawings, axonometry); dynamic business process models in various notations (graphical representation
of processes and their current states).
5. Knowledge base
Any information system for processing knowledge is based on a formal apparatus for knowledge
representation and manipulation in order to simulate human reasoning to solve applied problems. In
turn, a knowledge base is a database that contains facts about a certain subject domain, as well as
inference rules that allow making conclusions automatically and obtaining new statements about
available or newly introduced facts [13].
This allows us to consider an information system with a conceptual model of the subject domain M
= (S, D) as a knowledge base. In this case, the concept structure S (concepts and abstraction methods)
sets the inference rules on knowledge, and the concept intensionals D sets facts (assertions) about the
subject domain.
Assertions. The facts (assertions) are statements about the belonging of subject domain entities to
concept extensionals. According to the formula (1), the entity E belongs to the concept extensional N if
and only if the set of characteristic values of the entity E, which is ordered according to the shm N
concept scheme, belongs to the concept intensional int N:
Pi [ N ] int N N ( E ), (3)
PishmN
where is a marked (ordered) union performed with the repetition of elements; Pi[E] is a functor
that returns the attribute concept value Pi oа the entity E; is a bipolar logic connective; N(E) is a
one-place membership predicate of the entity E to the concept extensional N, N(E) E ext N [13].
According to the formula (3), the feasibility of the one-place predicate N(E) is determined by the
current state of the intensionalals D, i.e. the information system implements the open world model.
6. Implementation
A multi-layered structure of an information system with a conceptual model of a subject domain contains
a number of subsystems.
The subsystem providing consumer operation is implemented as a client application that functions
according to the browser principle. It means that the application receives data from the knowledge
subsystem to display the conceptual structure in the form of a tree, and concept intensionals and
extensions in the form of tabs and a list.
The subsystem for supporting work with the knowledge base serves to form data in formats used by
a client application for displaying and manipulating the concept model. Data exchange is carried out via
two interfaces. One interface is to display a tree node and provides its name, icon, description of the
context menu and tabs, as well as interpreted program texts for changing a node name, processing
interface events, displaying child nodes. The second interface is to display the list and provides a
description of the list header (columns), a number of rows consisting of cells by the number of columns,
as well as interpreted program texts for changing the contents of cells and processing interface events.
In order to increase the effectiveness of the information system and to shorten the time for the
implementation of new procedures, classes of specific concepts are used in a uniform canvas, for
example:
a class of concepts represented by standard questionnaires;
a class of concepts that express all kinds of reports for analyzing the current state of the modeled
domain;
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other classes of concepts with a typical representation and the same type of processing.
The point to note is that an information system itself also has some conceptual model. Working with
this model is also through the subsystem of working with a consumer. The model can include such
concepts as:
a module loaded in the process of working client application; it serves to implement a specific
function of mapping a conceptual model or solving a specific domain problem;
an event registered in the information system; it allows specifying a processor for operations of
concept creation, removal, or change;
a form for implementation of various scenarios for data inputting and processing by a user;
other concepts necessary to implement the requirements for a particular domain model.
The subsystem of inference and logic formation is responsible for performing operations on concepts.
This layer can be implemented through the procedures on the dedicated server (logic layer), as well as
through the stored procedures of the database management system (data layer).
To limit operations on concepts, as well as to form individual conceptual models, the information
system in the logic layer implements a developed mechanism for determining and inheriting rights.
A database management system represents the data storage and processing subsystem. In the case of
deploying large information systems, each layer can be implemented not on one but on a server group,
and contain means for dynamic distribution of the load on next layer servers. In this case, the data layer
is implemented as a distributed database.
Conclusion
The presented approach to the construction of an information system with a conceptual domain model
have been used in development of integrated simulators. They are designed to test interaction and
teamwork of operators when managing complex special purpose software and hardware facilities. The
main requirement for such facilities is the simplicity and naturalness of communication between
operators during operational commitment. Communication is based on a “professional language”, which
makes it possible to use conceptual models of a subject domain. They exclude the use of concepts and
all sorts of relationships between them, which are a part of model semantic load. This fact significantly
improves the universality of communication models and ensures invariance regarding the subject
domain.
This effect is due to the fact that the links between concepts in conceptual models are concepts
themselves, and the model is built on the basis of identifying and describing the abstractions that formed
(defined) concepts.
Concept intensionals define subject semantics completely. The abstraction of concepts formalized in
a conceptual structure of the subject domain determines only structuring of intensionals. In this case, it
is not necessary to specify logical statements (formulas, functions) that characterize concepts and are
inference rules. All that is necessary for a knowledge-based inference is in the conceptual structure of
the subject domain and concept intensionals.
Refusal of describing associations as links with different semantic markup makes the conceptual
structure of the subject domain representable as a tree.
Since the cooperative training of operators is associated with the implementation of known structure
management scenarios, the use of conceptual models of the domain allows implementing voice control.
It implies the ability to manage the script using a set of commands consisting of specific words. Thus,
the operator has the opportunity to enter information using his voice. After pronouncing certain words,
the device starts to recognize speech (converting audio signal into digital information). After the entered
information is correctly recognized, the program proceeds to the specified algorithm. It performs a
function that attached to a particular command. A virtual assistant continuously collects all data about
the entered requests and operator's order to build his profile. It tries to adapt to each user as much as
possible and allows evaluating the quality of the model of choice of each particular operator.
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