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Adaptable Reasoning Agents: Case Study a Health Care Agent-Based
System
OKBA KAZAR1, SAHNOUN ZAIDI2, LOUIS FRÉCON3, SORAYA KOUADRI MOSTÉFAOUI4
Computer Sciences Department, University of Biskra, Biskra, 07000, ALGERIA
Computer Sciences Department, University of Constantine, Constantine, 25000, ALGERIA
Computer Sciences Department, INSA, FRANCE
Informatics Department, University of Fribourg, SWITZERLAND
Abstract: - Various advanced areas of Artificial Intelligence need cooperation of agents of different nature.
The idea of specialized agent necessitates a very efficient and rational intervention of an agent in order to solve
part of the problem. In fact, the efficiency and the ability of an agent would be appreciated if it has various
reasoning capacities. In other words, in a cooperative problem solving, an agent may use different and various
reasoning modes (analogy, temporal,). In some situation the reasoning type used by the human to solve a
difficult problem requires taking in a common knowledge posed by different specialists and sometimes on
collating of data and varied information [6]. The same problem can be considered under various aspects, that it
can be useful to take into account an efficient structure of an agent in order to better solve the problem. So, two
specialists can adopt different ways of a conceptualization of their common domain. The purpose of this paper
is to propose an agent with adaptive reasoning which take into account the various types of knowledge
representation ensuring a certain degree of adaptability.
Key-Words: - Multi-Agent Systems, Reasoning, Intelligent Agent, Agent paradigm, Competence
1 Introduction
Many advanced domains of artificial intelligence
require the co-operation of agents of very varied
nature. The constant increase of the human
knowledge volume induced the reduction of the
competency field of every agent. It drags the growth
of the necessary specialization for an agent to the
exercise of a professional high-level activity. This
evolution of sciences and techniques has the
tendency to suppress the interdisciplinary agent
notion, but also to give back very frequent and more
complex interactions between agent’s specialists
[30]. The notion of specialization of an agent
requires an efficient or rational and fruitful
intervening to find a solution to a part of a problem
[6][13]. The global solution is represented by the
sub-solutions obtained by the intervening of an
agent [22]. To this effect, the rationality and an
agent's efficiency cannot be obtained and
appreciated that if the agent does not arrange a
varied enough reasoning means. In other terms, for
the resolution of a given problem an agent requires
the utilization of several ways of reasoning [1]
(analogy, temporal,). For this reason, an agent must
have a tool, which permits it to adapt to various
problem definition [19].
2 Adaptable Agents
The effective design of multi-agent systems
requires interest to the question of “how an agent,
having some different knowledge, must act to
adjust in order to solve a given problem?” [18][5].
In other terms, data concerning a problem can be
represented differently (logical, rule of production,
frame, semantic network) and the agent must be
able to reason differently according to knowledge
representation [14]. So several types of reasoning
influence the behaviour of an agent. For this reason
and for the agent can change its reasoning it must
possess capacities permitting it: to take different
types of knowledge representation into account,
and to be able to reason differently. Therefore, it is
important that it uses, according to circumstances,
different modes of intelligent reasoning [20]. We
present adaptable reasoning agent as a set of
specialized reasoning agent called intelligent
paradigm, each one uses one type of intelligent
reasoning mode. Several reasoning mechanisms
[12] can influence the adaptable reasoning agent
behaviour:
logical
reasoning,
approximate
reasoning [33][34] and temporal reasoning [9]. The
adaptable reasoning agent must also consider other
types of reasoning, the reasoning by model and the
reasoning by analogy. In other types of problems,
the adaptable reasoning agent must plan a sequence
of actions to reach a goal. In other cases, the
adaptable reasoning agent uses the reasoning by
classification. This power of adaptability can be
obtained by the integration in the adaptable
reasoning agent architecture [28] [21] different
kinds of knowledge representation associated to
corresponding reasoning mode [32]. To every mode
of knowledge representation corresponds one or
several algorithms of utilization or reasoning [29].
After surveying different agent architectures like
abstracters architectures, concrete architectures
(logic, reactive, B.D.I, layered), we have chosen the
B.D.I architecture as a model of our adaptable
reasoning agent. The choice is justified by the
principle of BDI agents reasoning which consists in
gradually refining the options (desires) in
increasingly concrete intentions (planning). This
approach converges largely with our requirements
in reasoning. From the formalization of B.D.I
agent, we retained that:
The fact adaptation of the presented case under
natural form requires an analysis for the choice of
the representation structure. Thus, this component
lays out both on facts of the case to be solved and on
the paradigm library. The task of this analyzer and
selector of structure is to obtain a knowledge
representation adapted to a reasoning mode.
3.3. Paradigms Library
There are diverse forms of intelligent reasoning
techniques associated to knowledge representation;
this structure of generic knowledge also called
pragmatic library of the agent is a set of model
specification of paradigm "representation; reasoning
model; strategies ". For example:
Paradigm1 = the know-how represented by
(production rules + logical reasoning).
Paradigm2 = the same know-how represented by
(semantic nets + associated model reasoning)
The modelisation of adaptable reasoning agent is
also B.D.I where:
Their exploitation during agent intervention for the
problem resolution may be sequential, parallel or
combined. Several selection criteria of a paradigm
are used by the agent: time (reasoning requiring a
temporal constraint), already encountered problems,
if the problem requires an intervention of other
agents (decomposition, delegation...), reasoning
mode retained for a solution hoped (on qualities or
criteria) by other agents.
B is the problem facts,
3.4 Intelligent paradigm
D is agent conditions to choose a paradigm,
Intelligent paradigm contains three essential parts
specifying:
B (Beliefs): the set of all the possible beliefs,
D (Desires): the set of all the possible desires,
I (intentions): the set of all the possible intentions.
I represents the selected paradigm.
3 Components Functioning
3.1. Interface
The Interface is an important tool as intermediate
part in the man/machine communication. In our
system of adaptable reasoning agent, the interface
must lay out a mechanism for the problem
acquisition, which is represented in various forms of
representation (production rule, semantic net, and
frame...). If the problem is simple the user can
choose only one interface of acquisition according
to the model of representation, but if the problem is
complex the user can choose an interface combining
one or more models representations.
3.2. Pragmatic analyzer
1. the why of its utilization: in which case it is
recommended and inversely,
2. the manner in which it can be used in
considering representation and reasoning,
3. the agent capacities on the paradigm that defines
the know-how oriented approach of agent.
In other words, it represents acquired experience on
the paradigm by the adaptable reasoning agent.
Two elements in the paradigm represent an
evolutionary aspect of the adaptable reasoning
agent: the «why » part: after a repeated use of a
paradigm, an adaptable reasoning agent can have
an idea of its use (degree of adaptation to certain
type of problem...), and the «capacities» part: by
applying certain strategies with a given reasoning
mode for resolution of problem, an agent can
deduce the effectiveness from this combination.
Thus, the adaptable reasoning agent can in other
similar circumstances change strategies with the
reasoning. The reasoning in this systems relief of a
social design of intelligence consisting in making a
set of agents in interaction, each of them
responsible for solving part of a problem with its
type of reasoning [23].
4
Adaptable
Functioning
Reasoning
Agent
The behaviour of an agent is independent of its
applicability. This behaviour can be described like
the uses of the intelligent reasoning existing in the
paradigms. In the first step, the user select the
suitable interface for problem acquisition, which is
represented in a showing form (production rule of,
semantic net, frames...), the agent perceives the
problem and generates new beliefs (facts of
problem). In the second step the pragmatic analyzer
use the context (intentions) in which the problem is
defined (simple or complex) and starting from the
paradigms library, determines the most adequate
paradigms, they represent the desires agent using
the why part of the paradigm to know if it should be
or not used in this case. This first stage represents
the meta-modelling problem. In the third step, and
after having determined the problem facts, the agent
models its data set; this stage represents modelling.
The paradigms are selected, the agent generates a
plan of execution, and then it passes to the execution
stage.
5 Proof of Concept Implementation: A
Health Care Agent-Based System
5.1 Paradigms library
In this paradigm the agent knowledge is represented
with semantic net form which has the recordings
table structure, such as each recording represents a
medical rule and has the following fields:
Summit_D[ ]: the list of the starting summits.
Name_Summit_F: arrival summit (Conclusion),
Nb_Summit_D: a number of starting summits,
Nb_Summit_V: a number of summits check. Each
starting summit is a record with three fields:
Name_ Summit : summit name,
Name_Arc: the Name arc, which connects this
summit with the entering summit,
State_Summit: equals false if the summit not
verified.
b. The reasoner (Execute1 ())
The reasoner represents the heart of each paradigm;
it is a program designed to solve a problem starting
from the initial data. The reasoner uses two
reasoning types, one is deductive and the other is
regressive (tries to find the summits initial starting
from the final summits).
5.1.2. Paradigm2
a. The representation model
It represents the production rules with a list that
facilitates modification (insertion, suppression of the
rules). Each element of this list is a set of fields:
State: state of the rule (to block or not).
Name_Rule: action code (Conclusion).
C++ is a good candidate for agent's implementation.
The implementation of the paradigm library consists
to realize the separate implementation of each
paradigm. Each paradigm is composed by a given
structure which represents the agent knowledge and
program which is the reasoner exploiting the agent
knowledge.
For application in medical domain, we implement
three paradigms, which are:
Fact[ ]: List facts (Conditions).
Nb_Fat: Condition in the rule
Nb-Fact_V: numbers Condition checked.
We represent the facts using recording form such as
the fields:
State_F: Established fact checked or not Index
5.1.1. Paradigm1
a. The representation model
Index: Facts code in the fact table
Buffer: The text or facts significance (character
string).
If Nb_Facet_V = Nb_Facet Then Show
b. The reasoner (Execute2 ())
endif;
This paradigm is restricted to front chaining. As in
expert systems the reasoner carries out basic cycles,
where each cycle comprises three essential stages:
filtering, resolution of conflict, execution.
5.1.3. Paradigm3
this Frame
End
If no Frame such as all the facets are symptoms in
the interface, then calculate Demon "If need be" to
determine the frame which has more checked facets
and show the heritage of this frame.
b. Inductive Reasoning
a. The representation model
In this paradigm Frames represent the agent
knowledge. We uses a recordings table each record
comprises the following fields:
In this case the user inserts a disease (Frame Name)
and the reasoner seeks the frame such as the name
of this frame is the same name with the name of the
selected disease. Inductive algorithm is:
Name_Fram: Frame name
Begin
For each Frame Do
Facet[ ]: Frame facet list.
If the selected disease = Name_Frame
Nb_Facet: facet Number.
Then Show all the facet; End;
Pourc: frame Demon (if needs).
End.
Heritage: frame family
5.2 Knowledge Base
The facet structure is:
We choose the field of medical diagnosis to validate
our system. Each paradigm represents the
knowledge base (to know of the agent) in the form
of a model of representation, which relates to O.R.L
(Oto-Rhino-Laryngologie), and dental problems.
State_Facet: facet state (verified or not).
Symp: facet Name.
Deductive Reasoning
This deductive
algorithms:
algorithm
contains
two
Algorithm 1
For each Frame do
If at least exists facet in interface Then
Modify the following fields:
State_Facet = active.
Nb_Facet_V++
sub
O.R.L Problem
Rule 1
If headaches then suspicion of respirator problem
Rule 2
If cough then suspicion of respirator problem
Rule 3
If suspicion of respirator problem And pain is on
the root of nose And Fever Then respirator problem
Endif
End
Rule 4
Algorithm 2
If suspicion of respirator problem And irritation of
throat then ignition of the throat
Begin
Rule 5
For each Frame Do
If fever And ignition of the throat And red throat
Then angina
Rule 6
If ignition of the gorger And raucous cough Then
laryngitis
The principal window contain principal menu witch
offers the following functions (fig.2):
Rule 7
If nasal pain And blood flow of the nose Then
episomites
DENTAL Problem
Rule 1
Fig.2. Principal menu of the application
If presence of dental plaques And gingivorragies
Then gingivitis
Rule 2
If decayed tooth And exploration with the probe are
painful Then dentine deep
Rule 3
The Knowledge base menu is protected by a
password (fig.3), which makes it possible to enter to
the agent knowledge for the update, consultation or
new knowledge creation. The insertion of new rules
uses the following window (fig.4). The new rule is
inserted in the three knowledge base of three
paradigms used.
If gingivitis inflammatory And dental mobility And
gingivorragies Then dentite
Rule 4
If Tartar And gingivorragies Then parodentopathy
Rule 5
If decayed tooth and oedema then narcoses pulpar
Rule 6
If decayed tooth and throbbing pains and long tooth
feeling then tooth ache
Fig.3 Password check
5.3 Problem Acquisition
The principal interface system is given by the figure
as follow (fig.1):
5.3.1 Simple problems
It allows to choice the type of problem (simple or
complex Problem) and the model of representation
(fig.6). This dialog window (fig.6) is applied to the
three representation models (semantic net,
production rules, Frame), which permit the selection
of reasoning method, associated. For complex
problems the user can select the representation
mode of each sub problem and let the execution,
which shows sub results after an analysis of these
sub problems.
Fig.1. System interface
that it gives him all the diseases which are
generated by the introduced causes.
a. Semantic net

Deductive Reasoning (using symptoms):
starting from the symptoms drawn by the
user on the following window, deduced the
diseases and to show them by semantic net
form (fig.5).
5.3.2
Complex problems
In the complexes problem the user can select the
representation mode corresponding to each sub
problem and begin the execution which gives sub
results after an analysis of these sub problems.
6 Conclusions
This paper presents a. In this paper we have
presented a new simple and coherent architecture of
an adaptable agent based on B.D.I structure, which
is defined, as a set of specific paradigm based on
one model of knowledge representation, and the
reasoning of the agent is Multi-paradigm. In
perspective of our work we are looking for the
module integration, which will allow the addition
of new intelligent paradigms, such defined in our
paper.
Fig.4. Rule acquisition
Fig.5. Reasoning mode

Inductive reasoning (diseases): starting
from the diseases selected by the user on the
window below, it deduces the symptoms
Fig.6. Illustration of a complex problem
acquisition
and shows them in the semantic net form.
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