Download Research on the Application of Distributed Artificial Intelligence in

Research on the Application of Distributed Artificial Intelligence in Network Teaching
Dong Song-ling
School of Journalism and Communication, Nanyang Normal University, Nanyang, Henan, 473061, China
Abstract. With the rapid development of modern education, the current school teaching model needs to be improved and perfected to meet
the needs of modern teaching. In order to improve the quality and efficiency of network teaching, it is an effective way to adopt the
distributed intelligent network teaching system. In this paper, based on the definition and classification of distributed artificial intelligence
(DAI), the network teaching system based on intelligent Agent technology was introduced, and the distributed artificial intelligence was
applied in intelligent network teaching platform of Distance Education and the corresponding fuzzy transform model combining with multiagent system (MAS) and mathematical theory was established, and a network teaching system based on Agent technology was constructed.
Finally, a university was took as a study case, the evaluation of the effectiveness of network teaching based on distributed artificial
intelligence technology was analyzed and studied, and by comparing the traditional teaching system with the effects of teaching evaluation
of the DAI network teaching system, it can be found that the network teaching system can be optimized by the distributed intelligent
technology, so DAI technology has an important role in the network teaching system.
Keywords: Network teaching; distributed artificial intelligence; multi agent system
1
Introduction
The researches of intelligent Agent theory and technology originated from distributed artificial intelligence (Distributed Artificial
Intelligence, DAI). Agent is a software entity that can study independently and adapt to the environment. Its characteristics include
autonomy, re-activity, cooperation, openness, communication, mobility and so on. Agent can take action to achieve a set of predefined goals
or tasks by sensing information in itself and in the environment [1]. Multi Agent system is composed of a number of independent Agent,
each Agent has its own responsibilities, and through the communications with other Agent it can obtain information to cooperate with each
other to completely solve the whole problem. Compared with a single Agent, multi Agent system is able to complete more complex and
extensive features. With the establishment of the nation's modernization and the lifelong learning system, the distance education in our
country are going into a new stage of rapid development [2]. However, in the face of many problems and difficulties in the practice of
distance education, how to use the new technology to raise the level of modern distance education is a historical and inevitable problem for
the distance education workers. The research and application areas of artificial intelligence and artificial intelligence science from the birth
are closely related to education [3]. AI in essence is to let the computer accept education and improve the science and technology of
intelligence, and its research results can be applied to the field of education to enhance the efficiency of education and produce a new
teaching model. In the background of emphasizing exploration and innovation, interdisciplinary integration, knowledge integration and
technology integration, how to introduce the latest technology of AI and use the latest achievements of AI to raise the level of education is a
significant and innovative engineering for distance education. From the point of view of the current development trend, the influence of
artificial intelligence technology in modern distance education is growing, and modern distance education already peeps into the new era of
intelligent clues [4]. Distributed artificial intelligence (DAI) mainly studies that how the distributed intelligent system to solve the problems
in a logical or physical way. The DAI system has the following characteristics: data, knowledge, and control in the system are distributed in
both logic and physics; Each solution mechanism is interconnected by a computer network, and the communication cost is much lower than
that in the process of solving the problems; Institutions in the system can cooperate with each other to solve the problems that a single
institution can solve difficultly or even cannot solve [5]. The implementation of DAI system can overcome the weakness of the original
expert system and learning system and greatly improve the performance of the knowledge system. The main advantages are as follows:
improving the ability and the efficiency of solving problems, expanding the scope of application, and reducing the complexity of the
software.
2
State of the ART
2.1 Application of DAI in the Intelligent Network Teaching Platform of Distance Education
The main roles of Agent in the distance education network teaching platform are: dynamical tracking process and real-time monitoring,
teaching and learning behavior analysis, information retrieval and filtering, collaborative learning and intelligent reasoning and so on.
Through the application of this technology application, it makes remote teaching platform use intelligent agent technology to realize
students' autonomous learning behaviors and behavioral counseling supervision, and according to the students' learning, progress and results,
they are given specific guidance, which provides intellectual support for the counseling of teachers. Intelligent agents can be teaching,
managers, learners, monitors, evaluators, guides, assistance and so on to help the remote learners to complete the study. The application of
intelligent agent technology in the distance education network teaching platform provides the quality assurance and technical support for the
distance education in a certain sense [6]. Figure 1 is the environment of network teaching based on artificial intelligence.
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/5/19
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Fig.1. Network teaching environment based on artificial intelligence
DAI can be divided into two basic research areas: Distributed Problem Solving (DPS: Distributed Problems Solving) and Multi-Agent
system (MAS: Mulit-Agent System). DPS studies a given problem, and how to assign the task to a group of modules or nodes so that each
node can share the knowledge of the problem and the answer is also its duty [7]. Distributed problem solving is based on the cooperation of
distributed and loosely coupled knowledge source. These knowledge sources are distributed on different processing nodes. If the single
source of knowledge cannot solve the problem, it is necessary to make decisions on the cooperation of knowledge sources and the sharing of
information. Decentralization refers to that control and data are distributed without global control and global data [8]. MAS mainly studies
the coordination of the behaviors of agent and how to coordinate their knowledge, goals, skills, and mutual planning in a group of
autonomous Agents and take actions to solve problems. Agent in MAS can have a global goal or have an independent goal related to their
own. Like the module in the DSP system, the Agent in MAS must share the knowledge of the problem and the answer and follow the
coordination process between the Agent behaviors.
The basic characteristics of the DAI system are shown in figure 2:
Fig.2. Basic characteristics of DAI system
Adaptability: logical, semantic, temporal and spatial distribution of the DAI system can provide a variety of choices for different
environments and it has a greater ability to adapt to different environments. Low cost: DAI system can contain many simple computer
systems with low cost. Extended development and management: each unit in the DAI system can be independently developed by a specific
domain expert, and the DAI system can be expanded or integrated with existing computer systems. High efficiency: parallel processing can
improve the speed of calculation and reasoning. Autonomy: for the purpose of local control and protection, the unit in the DAI system is
isolated. Reliability: The DAI system is more reliable than the centralized system because of the redundancy and mutual checking [9]. The
basic problems of DAI research include: how to describe, decompose and assign tasks and how to solve the integrated problem between
groups of agents. The second one is about how to make intelligent communication and interaction and use all kinds of language and
communication protocols, communication contents and time. Next is about how to ensure consistency in the interactions of decision, action,
and the adjustment of local decisions and the impact of the overall situation. Fourth one is to coordinate between single agents, how to
express and derive the behaviors, planning and knowledge of other agents and push the state of the coordination process are involved. The
fifth one is how to identify and coordinate the differences of views and intentions in the cooperation between the agents and how to
synthesize a unified view and consistent results.
2.2
MAS Technology
MAS (Multi-Agent System) is an important research direction that has been developing rapidly in recent years and it has been applied in the
field of scientific research and engineering. MAS mainly studies how to coordinate the intelligent behaviors of multiple Agent in the logical
or physical way, so as to realize the complex problem solving. There are a number of components, and it is a loosely coupled network of
complex problem solving, which often is used to solve those problems that cannot solve or can be solved difficultly. Each one of the MAS is
a physical or abstract entity, which has part of information or ability of solving the problems, and it can act on itself or the environment,
communicate with others, coordinate with each other and work together to achieve a predetermined goals. Relationships can be cooperative,
but there are also competitions. This fully reveals many common characteristics of human society and the potential social significance of the
study. In human society, there is a certain connection between individuals, and it is the connection that makes the individual form a human
society, so that the individual becomes a social and attributed person. Also, several persons stacked together, which will always be a few
independent individuals, only through mutual coordination and cooperation, can they form the system of a certain function of operation. As
an integral part of the environment, they must be active in dealing with their own internal affairs and the distribution of the business
environment. The relationships between them and the environment are the same with people in a society, they not only solve their own
affairs, but also assume the responsibility of society as a member of society [10]. Therefore, the application of Agent is mainly in the form
of multiple collaboration. Just like the general intelligence of the society is better than any individual, the solution of the cooperative system
is far more than a single system. Agent's intelligence is reflected in the ability of effective cooperation of achieving the common goal, so the
multi coordination and cooperation are the core problems of researches.
3
Methodology
3.1 Fuzzy Transformation Model in Intelligent Network Teaching
A can be represented as a fuzzy set, which is set：
membership degree
µ A ( xi ), i = 1, 2, , n .
transformation. Matrix
A = (a1 , a2 , , an ) , among the U = ( x1 , x2 , , xn ) , ai is corresponding
Fuzzy vector can be regarded as a fuzzy matrix with one line, which can define fuzzy
A = (aij ) m×n and column vector X, so Y = AX . And Y is a column vector，and elements of Y are calculated
by this way:
=
yi
n
=
a x (i
∑
k =1
ik
k
1, 2, , m) .
(1)
So
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/5/19
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 a11 a12  a1n 


( y1 y2  yn ) =  a21 a22  a2 n   ( x1 x2  xn )
 a a a 
nn 
 n1 n 2
(2)
Assuming a fuzzy matrix R and a fuzzy vector X:
X = ( x1 x2  xn ) , 0 ≤ xi ≤ 1(i =
1, 2, , n)
=
R (rij ) m×n (0 ≤ rij ≤ 1)
Y, in fact, is the synthesis of fuzzy vector X and fuzzy relation R matrix, formula
If:
X R = Y
(3)
(4)
is called fuzzy transformation.
X = (0.2, 0.5, 0.3)
(5)
 0.2 0.5 0.1 0.2 


R =  0.3 0 0.4 0.3 
 0.5 0.1 0.2 0.2 


(6)
The synthesis result Y is
 0.2 0.5 0.1 0.2 


=
Y X=
R (0.2, 0.5, 0.3) 0.3 0 0.4 0.3
=
 (0.3, 0.1, 0.4, 0.2)
 0.5 0.1 0.2 0.2 


(7)
In the construction of the complex student model, the introduction of the principle of fuzzy transformation makes the evaluation of
students' cognitive level and learning strategies more scientific [11].
3.2 Standardization Process of Network Education Platform
With the continuous maturity and progress of the standards, the operability and the implementation of the standards have been improved
significantly. Moreover, the international standardization organizations also strengthen the contact and communication, such as ISO, IMS,
AICC, they have a close contact. Many studies have also begun to focus on how to coordinate the differences between these different
standards. And at the same time, with the gradual popularization of E-Learning in the global scope, the voice and demands of letting ELearning system share learning resources and interoperability are growing. In this kind of background, the educational technology standard
also gradually develops from the theory to the practice [12]. In the different development stages of the network teaching platform, the
network teaching platform focuses on different E-Learning requirements. Therefore, different types of educational technology standards and
norms are needed to meet the needs of different applications. Figure 3 describes the standardization process of the network teaching
platform: In the early stage of the network teaching platform, its main function is to provide the management of digital learning contents
(CMS stage). Correspondingly, the formulation and application of standards are mainly focused on the description and organization of
learning resources and contents. The meta-data standard, which provides a unified data format for describing the resources of different
systems, is to describe the attribute naming and value of resources. Only uniting the data format, can the system understand each other
information and it can share the resource. The meta-data standards we know are LOM Dublin, Core IEEE of Dublin, etc. This is the earliest
stage of the development of learning standards, and through the implementation of these standards, a variety of teaching resources can be
convenient to import and export in different teaching platforms, so as to achieve the most primary resource sharing.
Fig. 3. The standardization process of network teaching platform
3.3 Network Teaching System Based on Agent
The introduction of Agent technology in network teaching can play a very important role in the following areas: It can establish a
collaborative learning environment of network learning by using the social nature of agent, and learners complete collaborative learning
through agent collaboration mechanism and teachers and students can exchange information, at the same time, teachers can also make
collaborative teaching; The distribution of agent can unify the teaching resources and enhance coordination and reusability, in order to
achieve different software, data exchanges and collaborative work between software in the heterogeneous network, operating system and
machine environment; Using the autonomy of Agent, it can dynamically track learning behaviors, and according to the learner's learning
situation, it puts forward suggestions to the learners and automatically organizes learning contents. According to different functions, the
main Agent of the network teaching system are management Agent, teacher Agent, student Agent, resource Agent, question and answer
Agent, examination Agent and other categories [13].
Management Agent: Agent is responsible for the management of teaching issues related to the decision-making, teaching and the Agent
control and supervision of other issues. Management Agent is composed of the controller, the teaching inference engine, the knowledge base,
the student model base and the communication module. The controller has the function of dispatching, directing and coordinating of the
whole teaching task. Teaching inference engine comprehensively use relevant information from the knowledge of knowledge base and the
student model base, and according to the control strategy on the various problems encountered in the teaching process of reasoning, it
arranges the teaching contents and adjust the decisions of teaching procedures and methods to provide the optimal solution. There are
control knowledge, problem solving knowledge and communication knowledge, these knowledge is not static, and it can be added and
updated with the problem. The database of student model records the student's information and the scope of the student's knowledge.
Communication module is responsible for communicating with other Agent, reflecting the interaction and cooperation between the Agents.
Intelligent tutoring system stores the domain knowledge and the corresponding teaching methods, which can teach students individually, and
according to the understanding level of knowledge of students to automatically adjust the teaching methods and the teaching speed, which in
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/5/19
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a certain extent simulates the software system of human experts in teaching the teaching activities [14]. Intelligent teaching system usually
consists of three main parts: the domain knowledge base, the student module and the inference engine. The overall structure of the system is
shown in figure 4.
Fig.4. Overall structure of intelligent teaching system
4.Results Analysis and Disccussion
Researches and evaluations of the effectiveness of the network teaching in a school are made, which mainly emphasizes to give priority to
the quantitative researches and supple the feedback and suggestions. In "curriculum design and development course of network", we made
use of the investigation and statistics of the network learning platform to make a survey, and the first batch of 90 students conducted a
questionnaire survey, 47 the number of valid questionnaires were recovered, the recovery rate is 52%. Table 9 to table 11 is the students'
satisfaction, teaching effect and learning achievement of this course [15]. To compare the evaluation results of face-to-face teaching, we
adopt the statistical evaluation method that has been used in professional education in college classroom teaching of Hong Kong University,
calculating the average percentage of each evaluation problem. The formula is: [the number of people (very satisfied/agreed very
much)×100 + the number of people(satisfied/agreed)×75 +the number of people (OK)×50 +the number of people (Not satisfied /
disagree)×25 +the number of people(very dissatisfied / extremely dissatisfied)×0]/ the total effective number(does not include the number of
people who choose not to apply). The average percentage of the evaluation results was interpreted as: 0-39.9 % means "fail", 40-49.9%
means “OK”；50-59.9% means “satisfaction”; 60-69.9% means “good”; 70-74.9% means “very good”; 75-100% means “excellent”.
Table 9. The degree of satisfaction of the students (%)
Very
Satisfie
Extremely
Commonly
Dissatisfied
Not applicable
Average
satisfied
d
dissatisfied
Website design
53
34
9
4
0
0
84.0
Teacher
57
39
2
2
0
0
87.8
lectures(video)
Teachers'
68
26
4
0
2
0
89.5
guidance
Instructional
57
35
4
2
0
0
85.8
design
Network
independent
47
43
10
0
0
0
84.2
learning
materials
Network
learning
51
36
9
2
2
0
83.0
environment
Quality of the
58
36
4
0
2
0
87.0
course
Table 10. Evaluation of the teaching effect of the students on the course (%)
Very
Satisfie
Commonl
Extremely
Not
Dissatisfied
satisfied
d
y
dissatisfied
applicable
The teacher's
lecture rhythm is
moderate
(video)
The teacher has
a good
knowledge of
the curriculum
Tutors have
abundant
knowledge of
curriculum
Tutors answer
clearly
Guidance
Average
68
30
2
0
0
0
91.5
79
19
2
0
0
0
94.3
66
28
6
0
0
0
90.0
62
32
6
0
0
0
89.0
63
24
13
0
0
0
87.5
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
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teachers
stimulate
students' online
discussion
Guidance
teachers
stimulate
students interest
in learning
Tutors can help
students learn
Teaching effect
of this course is
good
53
36
9
2
0
0
85.0
53
36
9
0
2
0
84.5
51
40
9
0
0
0
84.0
From table 9 to table 11 we can know that the satisfaction, teaching effectiveness and the effectiveness of students based on distributed
artificial intelligent network courses get "excellent" in the classroom teaching evaluation. In which the satisfaction rate of the quality gets 87
points and teaching effects reach 84 points, and the effects of learning reach 82.8 points, and evaluation of "this course is worth learning"
achieves a high score of 92. This performance is much higher than the average score of the college classroom teaching.
Table 11. Evaluation of students' learning performance in the course (%)
Very
Extremely
Averag
Satisfied
Commonly
Dissatisfied
Not applicable
satisfied
dissatisfied
e
This course
has a good
45
43
10
2
0
0
82.8
learning
effect
This course
is worth
66
32
2
0
0
0
92.0
learning
5 Conclusion
With the extensive application of artificial intelligence technology and the rapid development of the network teaching system, the network
teaching system based on the distributed artificial intelligence technology has become a hot research topic. The study is conducive to the
promotion of the efficient and sustainable development of modern teaching. This paper establishes a network teaching system based on the
distributed artificial intelligence technology, which not only provides theoretical basis for solving the distributed problem, but also provides
a technical platform and model for network teaching. Through the experimental analysis of the teaching achievement of a school that based
on the traditional teaching mode, the advantages of distributed artificial intelligent network teaching system are obvious, and in the aspects
of teaching, it has good effects, so the distributed artificial intelligence technology has a greater role in promoting the modern education. But
the relative researches on the further integration and the future development direction of artificial intelligence and network teaching are not
enough, and on the basis of more practice and researches, the system model of modern teaching should be optimized and perfected and the
high efficiency and sustainable development of network teaching will be realized.
References
[1].
[2].
[3].
[4].
[5].
[6].
[7].
[8].
[9].
[10].
[11].
[12].
[13].
Singh M, Panigrahi B K, Abhyankar A R. Optimal coordination of directional over-current relays using Teaching LearningBased Optimization (TLBO) algorithm. International Journal of Electrical Power & Energy Systems, 50, 33--41 (2013).
Jian T, Lijian F, Tao G. Cloud computing-based Design of Network Teaching System. Journal of TaiYuan Urban Vocational
college, 159--160 (2010).
Jiangbo, Pan, and Deng Jiangao. "Application of simulation software in computer network teaching." Experimental Technology
and Management 7, 32 (2011).
Weining, Wu. "The Status and Developing Strategy of Network Teaching Platform in Universities." Value Engineering, 31, 171
(2010).
Tanimoto J, Brede M, Yamauchi A. Network reciprocity by coexisting learning and teaching strategies. Physical Review E, 85(3),
032101 (2012).
Galbraith C S, Merrill G B, Kline D M. Are student evaluations of teaching effectiveness valid for measuring student learning
outcomes in business related classes? A neural network and Bayesian analyses. Research in Higher Education, 53(3), 353--374
(2012).
Carnmarata S, McArthur D, Steeb R. STRATEGIES OF COOPERATION IN DISTRIBUTED PROBLEM SOLVING!.
Readings in Distributed Artificial Intelligence, 102 (2014).
Seibt J, Hakli R, Nørskov M. Frontiers in Artificial Intelligence and Applications. (2014).
Rogers A, Farinelli A, Stranders R, et al. Bounded approximate decentralised coordination via the max-sum algorithm. Artificial
Intelligence, 175(2), 730--759 (2011).
Ramchurn S D, Vytelingum P, Rogers A, et al. Putting the'smarts' into the smart grid: a grand challenge for artificial intelligence.
Communications of the ACM, 55(4), 86--97 (2012).
Wang X, Hong Y, Huang J, et al. A distributed control approach to a robust output regulation problem for multi-agent linear
systems. IEEE Transactions on Automatic Control, 55(12), 2891--2895 (2010).
Yu W, Chen G, Cao M. Some necessary and sufficient conditions for second-order consensus in multi-agent dynamical systems.
Automatica, 46(6), 1089--1095 (2010).
Arel I, Liu C, Urbanik T, et al. Reinforcement learning-based multi-agent system for network traffic signal control. Intelligent
Transport Systems, IET, 4(2), 128--135 (2010).
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/5/19
19.5
[14]. Ma C Q, Zhang J F. Necessary and sufficient conditions for consensusability of linear multi-agent systems. Automatic Control,
IEEE Transactions on, 55(5), 1263--1268 (2010).
[15]. Jie, F. E. N. G. "The Problems and Countermeasures of Experimental Teaching in Innovation-Oriented Talents Training."
Research and Exploration in Laboratory 4, 23--25 (2008).
Journal of Residuals Science & Technology, Vol. 13, No. 5, 2016
© 2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/5/19
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