Development of a Virtual Student Community to Communicate Via an E-learning Platform

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 9, September 2014)

Development of a Virtual Student Community to

Communicate Via an E-learning Platform

Mouhsine Rhaimi

1

, Rochdi Messoussi²

1,2 _{LASTID Laboratory, Department of physics, Faculty of Sciences, Ibn Tofail University, BP 133 Kénitra 14000, Morocco}

Abstract — To improve the performance of e-learning platforms (ELP), many evaluation studies have been performed, since learning provided by the platform is getting more customized, the evaluation becomes more complex, lengthy and costly, and also requires the mobilization of a large number of students and professors to simulate the richest educational exchange possible. Our approach is to create a Community of Virtual Students that will substitute a human group of students in the evaluation of a target platform.

Our Virtual Students Community (VSC) is composed of intelligent and conscious students, based on a cognitive and normative multi-agent system, applying the BDI model.

Virtual students will conduct exchanges with the platform, which significantly decrease the evaluation’s costs and time, and increase the efficiency of exchanges, while providing detailed reports and monitoring, allowing a better assessment via a third-party tool.

Keywords — Virtual Student Community, artificial intelligence, artificial consciousness, multi-agent system, BDI model, communication, learning, indexing.

I. INTRODUCTION

E-Learning Platforms (ELP) are nowadays in great expansion and their evolution is noticeable, still their role is very sensitive and requires a good evaluation before going on-line for student’s use. To overcome this problem, evaluation systems have emerged and offer a multitude of statistics to monitor and improve the performances of these platforms. However, the need for a group of students and professors to conduct exchanges with the ELP in order to extract these statistics makes it expensive and long-acting especially if you want good results. Thus was born the idea of a virtual student Community that will take on the role of the human students.

II.VIRTUAL STUDENT COMMUNITY

From our point of view, a virtual student is aware of himself and the world around him, he follows his goals by creating action plans that starts through tools such as surfing trough web pages, searching, playback semantics, comparison, communication and ends by indexing relevant results.

A virtual student (VS) focuses only on the exchange with the platform and can perform requests at a high frequency to minimize the treatment time.

The virtual student Community (VSC) is very flexible: the number of individuals in it is manageable as needed, it can be divided to subgroups with different profiles, and it is always operational (no absent or sick student...). Additionally, VSC allows easy detailed monitoring rapports of actions and exchanges made, and also virtual students’ goals status.

A. A modular system architecture:

The choice of a modular system is influenced by the human system, which is based on a decision module (brain) that orchestrates all the different activities of an individual through commands and instructions to organs and limbs.

However, to effectively replace a human student by a virtual one, “he” must demonstrate intelligence, awareness, attention, learning ability and flexibility vis-à-vis the changes encountered in his environment.

Creating such an artificial individual is a difficult task, our approach is to perform several studies in different fields in order to achieve the most complete VS: A study of artificial intelligence will enhance the VS smart side, a human consciousness (psychology) and artificial study will enrich the conscious side, a cognitive system study for the attention and learning, and the mixture between pedagogy, video games mechanics and a good model of multi-agent system will give the Virtual Student flexibility to its environment. The sum of all these contributions will allow him to evolve as a human within the community, providing diversity and personal aspect.

Among modular systems simulating human, Intelligent Tutoring System (ITS) are distinguished by their nature and area of use in direct relation to our subject.

III. INTELLIGENT TUTORING SYSTEMS

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Intelligent tutoring systems architecture (Beck, Stern, Haugsjaa [2])

IV.THE HUMAN COGNITIVE SYSTEM

Cognition in general (human, animal or artificial) can be defined by the various mental processes such as perceptual analysis of the environment, reasoning, language, motor control, memory, emotions, etc... . It combines the functions by which we build an operational representation of reality from our perceptions, most likely to feed our thinking and guide our actions in order to evolve in a reasonable way. The mechanism of decision-making is based primarily on data collected by the cognitive system and any lack of vital information generates an incorrect decision. Main cognitive functions are attention and memory.

A. Attention and Memory

William James says that «Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focus and concentration of consciousness is of its essence. It implies withdrawal from some things in order to deal effectively with others, and is a condition which has a real opposite in the confused, dazed, scatterbrained state. » [3]

The links between attention and memory are numerous and complex. Thus, an object on which our attention is focused will be better stored. It is generally believed that the attentional processes get involved between sensory memory and short term memory [4]

Memory is the cognitive function widely sought in most of our actions. It is constantly put to use on a voluntary/involuntary basis. It represents knowledge in a broad sense (souvenirs, cultural knowledge, motor procedures ...) of each human been. There are different types of memory depending on the stored knowledge’s types we wish to create. [4]

B. Indexing

At the heart of all the search engines is a mechanism for automatic indexing treating raw data and providing highly efficient multiple links, and accelerating the search process.

The concept is quite similar to a book index, which lets you quickly find the pages that deal with a given subject. If you need to cover a wide range of files or find a specific string in a file, you should not sequentially scan each file for the given sentence; Because the greater the number of files is, the longer the search will be. It is better to establish an index of texts in a format that allows quick search, which avoids the sequential method. This process is called indexing.

C. Research

Research is the act of looking up words in an index to find references to document. The quality of research is assessed by the position and relevance of the results. However, other factors come into play like speed, which is a key factor to treat a vast amount of information, as well as support of simple or complex queries, results positioning and sorting.

V. COMMUNICATION AND LANGUAGE

A. Communication

Communication is "the act of transmitting something to relate, in conjunction, contact, and things... to be shared with others, usually by language: verbal exchange between a speaker and another one from which he solicits a response." [15].

In the context of multi-agent system, communication or interaction is a dynamic linking of two or more agents through a set of reciprocal actions. Interactions are expressed from a series of actions whose consequences exert in return an influence on the future behavior of agents [16].

B. Aspects of communication

The communication system expands the perceptual capacities of agents by allowing them to benefit from the information and knowledge of other agents. To accomplish a given goal, an agent may cooperate with another one; the communication between these two individuals is a mandatory requirement.

C. Developed communication Interactions?

This type of interaction can take into account the mental states of agents, eventually through a model Belief-Desire-Intention (BDI). The relationships expressed between the different concerned entities with the act of communication include:

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International Journal of Emerging Technology and Advanced Engineering

Otherwise, the message is sent in a broadcast mode to all agents (this type of communication is widely used in dynamic systems in which agents can appear and disappear).

2) The nature of the medium: There are three kinds of message routing mechanisms: direct routing (the message is taken by the communication channel and brought directly to the recipient), the signal propagation routing (an agent sends a signal that spreads in the environment and the intensity decreases with distance) and routing by posters (an agent wishing to communicate, puts his message in a common area called bulletin board, visible to all agents (or those of a particular class.) This mode of transport combines the characteristics of direct messages and propagation messages modes).

3) Intend to communicate: is a graduated system that depends on the cognitive abilities of the issuer.

VI.CONSCIOUSNESS AND DECISION MAKING

A. Consciousness

« Consciousness is a concept that is just beginning to be really studied from a scientific point of view and there is currently no rigorous definition of it. But if consciousness is a physiological function, in other words, it relies on the brain and results of evolution, the problem is therefore to demonstrate that consciousness has properties that arise from the brain physiology» [6].

In the field of medicine, according to the founder of the Sophrology (the medical science of consciousness) Alfonso Caycedo, consciousness is « the force that allows the integration of all the psychological and physical elements of the human person, that is to say the force that animates it. Sophrology considers man as an indivisible, original and transcendent been (...) » [20]

Traditional medicine sees consciousness as its manifestation: It is a vital function, which ensures the survival of the person allowing him to behave in a suitable environment, the human being can be in one of three states: conscious, semi conscious or unconscious ... the Sophrology distinguishes three levels of consciousness (quantitative variations): standby, sleep and between the two: "sophroliminal level." In this "sophroliminal level", corporal sensations are best collected and integrated. It is at this level that the dynamic structure of consciousness is the most easy and efficient. It also specifies three states of consciousness (qualitative changes): disease (CP) studied by psychiatry, ordinary (CO) studied by psychology, and sophronic (CS) studied by Sophrology. [20]

B. The artificial consciousness

Artificial consciousness is the way to generate in computer systems what are actually human thoughts.

It is understood as the artificial production of views, opinions, assessments, impressions, desires, and the generation of very sensible representations of the reality in which the system works [7].

Artificial intelligence is an existing technology for several decades but which tends to designate more or less complex algorithms performed by computers, always under control and administration of man. Based on this assumption, we can consider that the computer system becomes aware from the moment it is no longer fully controlled. In addition, the system must also be equipped with decision making capability. One challenge is to define in digital systems, which might identify the emergence of a form of artificial consciousness.

C. decision making

Decision making can be seen as the mental processes (cognitive process) resulting in the selection of a course of action among several alternative scenarios. Every decision making process produces a final choice. [8] The output can be an action or an opinion of choice.

There are four steps involved in decision-making: data gathering, information processing, developing a sense and decision making, and three possible outcomes: a reaction, a response or orientation resulting in a process of reflection.

Richard Barrett [9] describes six modes of decision making: based on instinct, on the subconscious beliefs, on conscious beliefs, on values, on intuition, and on inspiration.

D. Problem Analysis and Decision Making

It is important to differentiate between problem analysis and decision making. The concepts are completely separate from one another. Problem analysis must be done first, then the information gathered in that process may be used towards decision making. [23]

1) Problem Analysis: The problem analysis is based on:

• Analyze performance, what should the results be against what they actually are

• Problems are merely deviations from performance standards

• Problem must be precisely identified and described • Problems are caused by some change from a

distinctive feature

• Something can always be used to distinguish between what has and hasn't been effected by a cause

• Causes to problems can be deducted from relevant changes found in analyzing the problem

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 9, September 2014) 2)Decision making:

• Objectives must first be established

• Objectives must be classified and placed in order of importance

• Alternative actions must be developed

• The alternative must be evaluated against all the objectives

• The alternative that is able to achieve all the objectives is the tentative decision

• The tentative decision is evaluated for more possible consequences

• The decisive actions are taken, and additional actions are taken to prevent any adverse consequences from becoming problems and starting both systems (problem analysis and decision making) all over again

• There are steps that are generally followed that result in a decision model that can be used to determine an optimal production plan [24]

3) Common techniques: Some of the decision making techniques people use in everyday life include:

• Pros and Cons: Listing the advantages and disadvantages of each option, popularized by Plato and Benjamin Franklin

• Simple priority: Choosing the alternative with the highest probability-weighted utility for each alternative

• Satisfaction: using the first acceptable option found • Acquiesce to a person in authority or an "expert",

just following orders

• Flipism: Flipping a coin, cutting a deck of playing cards, and other random or coincidence methods • Prayer, tarot cards, astrology, augurs, revelation, or

other forms of divination

• Taking the most opposite action compared to the advice of mistrusted authorities (parents, police officers, partners ...)

VII. CONSTRAINTS

A. Constraints of the human cognitive system

According to Haikonen « The logical rule based principles of programming that have made computers so powerful are actually preventing them from attaining human-like cognitive powers, preventing them from becoming true thinking machines.» [21]

Such systems dismiss the meaning and significance of the information in their treatment, merely just provide a correct logical result whereas the purpose of a cognitive evaluation of a given situation is to learn the meaning of this logical result, memorize the feeling of being confronted with this situation and the knowledge gained after facing it, in order to notice it before a similar future event, and use this knowledge to good aim.

Another weakness is the combinatorial explosion, which makes a small change in the number of data to be considered in a trivial problem sufficient to make the solution very difficult, if not impossible in some cases with current computers.

A combinatorial explosion example is the Ackermann function, a total computable function that is not primitive recursive. All primitive recursive functions are total and computable, but the Ackermann function illustrates that not all functions are. Found in 1926 by Wilhelm Ackermann. One common version, the two-argument Ackermann–Péter function, is defined as follows for nonnegative integers m and n:

Its value grows rapidly, even for small inputs. For example A(4,2) is an integer of 19,729 decimal digits[25]. This gives us an idea about the problems of profound and multiple searches, meaningless search take a huge amount of time and may not lead to effective solutions. « Without any actual sense of meaning and significance it is hard to do it, no matter how the knowledge is grouped » [21]

B. Constraints of Communication and Language

Assuming that the agent is aware of his situation and understands the meaning of his purpose, he is faced with the fact of transmitting and communicating his intentions to other agents, involving the meaning of his message; Professor Albert Mehrabian has established the rule of 7% -38% -55%, based on two studies [18] [19], widely used in communication, also known as the rule of 3V:

• 7% of communication is verbal (meaning of words) • 38% of communication is oral (tone and voice) • 55% of communication is visual (facial expressions and body language).

Thus 93% of communication is non-verbal and relies on emotions and attitudes, two human characteristics that robots and artificial agents do not have.

Also, developing a computer system that attempts to learn the semantics in the manner of a human being, cumulatively, and over the long term, requires a machine that calculated 24/7, connected to an information network of graduated education (as school system), and years of learning, which is very expensive for any project.

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 9, September 2014) C. Constraints of Consciousness

There isn't a precise definition on what we may rely on to establish a concept of artificial consciousness in order to implement it. One challenge is to define in digital systems, which might identify the emergence of a form of artificial consciousness.

It is also necessary to determine what level of complexity a digital system must reach to be able to achieve this awareness and get its free will, which makes things more complex.

« Baars uses an operational definition of consciousness that considers people to be conscious of an event if:

- They can say immediately afterwards that they were conscious of it.

- We can independently verify the accuracy of their report. » [19]

« As Baars readily acknowledges, his definition is less than perfect in that it presupposes the abilities to use language (in telling of the conscious experience), to have volition (the ability and inclination to do the telling), and to use meta-cognition (to become conscious of the conscious experience of X, in order to tell of the experience), and thus doesn't isolate consciousness in its essence as would an ideal definition. »[19]

It is imperative to note that consciousness is based on the knowledge database that we possess, being in a dark room and aware that we have a tube-shaped object in our hand is a first form of consciousness of the world, but being aware that this is a flashlight – which we can switch on to illuminate the room and see exactly where we are - is a developed form of consciousness that is based on the knowledge already acquired.

D. Constraints of decision making

First we have the difficulty of prevision that blocks classic IA since it preprograms every relevant rule for future events without anticipation and adaptation. « The problem with AI and artificial neural network approaches has been that the programs and computations do not really understand what they are doing. Meaning and significance are not really involved in the process. » [26]

Also adds the difficulty of designing an artificial system applying the six modes of decision established by Barrett [9], based on studies in human psychology: Decision making based on instinct: DNA emanating actions are based on learned responses in adulthood, this type of decision making comes into play to help us survive and avoid dangerous situations, or put our lives in danger to save others. The action always precedes thought; there is no reflection.

Decision making based on subconscious beliefs: we react to what is happening in our environment without reflection, based on personal memories. The action precedes thought and is often accompanied by the release of an emotional charge, others are not consulted to help us improve our sense decisions and provide support in our decision making. This type goes against our goal of cooperation agents to improve trade with the learning platform.

Decision making based on conscious beliefs: we take the time to think in order to use logic to understand what is happening, use information from the past (beliefs about what we think we know), and make a choice on how to react. We also have time to discuss the situation with others and get advice on how best to meet our needs.

Decision making based on values: Thought precedes action. Decisions that are taken are not based on past experiences. They are based on the future we want to create. We are in control of our actions and behaviors. We can consult others to support and improve our decision making.

Decision making based on intuition: intuition arises from the deepening of your connection to your soul... It allows us to access our own deeper intelligence, and the collective intelligence of wider group awareness is expanded through a change in our identity / sense knowledge. Judgment is suspended: no meaning decisions takes place, either consciously or unconsciously. The mind is empty from thoughts, beliefs and agendas. The thoughts that arise reflect wisdom and are in alignment with your most deeply held values. There is no conscious or subconscious attempt at making meaning, and there is no focus on the past or the future as part of our thesis, we do not intend to implement the intuition in the VSC.

The decision on inspiration: Inspiration is the way we receive soul-based promptings into our mind. It is always very personal and directive. It is about what you need to do. Thought seems to come from nowhere. It is persistent, linked to the actions you need to take, and there are consequences not following your inspiration.

It is still impossible today to design a program of general decision making; the only option is to model a specific system by maximizing the input specifications to ensure an outcome that meets best to our expectations.

VIII. SOLUTIONS

A. Research and Content Indexing

These two functions are implemented using two mechanisms: indexing and search. We opted for using

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Our primary objective is to provide the VS with the possibility of collecting and indexing the contents of the platform using a web crawler teamed with a search engine (Lucene) for storing, and returning an index containing the URL and the content of a platform page, corresponding to the retrieved data.

In principle, students surf through the pages in search of the desired data, this scenario is difficult to implement given the unknown number of pages to visit and dig in, following a predetermined path in order to highlight the level of the platform's organization. Respect of the robots.txt website and a maximum depth will be put in place to improve the performance of this action.

B. Communication Language

Any act of communication is based on language, in a network protocol perspective, we have the Agent Communication Language (ACL), which manages proposals, rules, and actions instead of simple objects without associated semantics. Message written using ACL describe a desire rather than a procedure call or method condition.

The « FIPA Agent Communication Language (ACL) » is based on the speech act theory: messages are actions or communicative acts, because they are intended to perform an action under its dispatch. The specification consists of a set of message types and a description of their pragmatic, i.e. the effects on the mental attitudes of the sender and receiver agents. Every act of communication is described both in narrative form and a formal semantics based on modal logic. The first questions that arise are: How to edit the contents of a message? And what is its size? The ACL implementation in JADE provided a good example.

Below is a list of all the attributes of a message JADE ACL. As described in the API documentation, JADE provides get and set methods for accessing attributes [5].

Performative - FIPA message type (INFORM, QUERY, PROPOSE...)

Address - Receiver/ Sender

Content – message body

ConversationID – link between same conversation messages.

Language – content Language

Ontology - content Ontology

Protocol – Specify the protocol

ReplyWith –

InReplyTo –

ReplyBy – a « time limit » for responses

C. The partial artificial consciousness

As part of our subject, we will focus on how to implement a notion of "partial artificial consciousness", in a manner similar to medicine that sees consciousness through its manifestations (world consciousness) and decompose it into 3 levels (conscious, semi conscious, and unconscious). This way we can differentiate consciousness built for VS devoid of feelings (to avoid the definition of senses and the possibilities for theirs implementations ...), and human consciousness which largely encompass it.

The definition of Baars [20] is based on three capacities: use of language, willing, and meta-cognition (have a mental activity on its own mental processes, that is to say «thinking about his own thoughts»), it can be integrated in the design of an agent and make it «partially conscious»

The partial artificial consciousness of a virtual student is also based on the development of a decision support system, which allows among other things, to give greater autonomy to the VS.

D. Decision making

If we want to make rational decisions, we choose the decision based on conscious beliefs, which allows us to have time to think so that we can use logic to understand what is happening and make a choice about how to respond. We also have time to discuss the situation with others and get advice on how best to meet our needs. The main characteristics of this mode are:

• Thought precedes action.

• Decisions are based on what we think we know. • We are in control of our actions and behaviors. • We can consult others to support and improve our

decision making.

Decision making based on conscious beliefs uses information from the past (beliefs about what we think we know) to make decisions about the future.

VII. DESIGN AND IMPLEMENTATION

A. Multi agents systems (MAS)

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 9, September 2014) B. MAS Architectures

1) The horizontal modular architecture: Consists of an assembly of modules, each in charge of a particular function (perception, communication, database maintenance beliefs, commitment management, goals management, decision making, planning, etc.). Communication channels between the components of this architecture are static and determined by the architect. One advantage of this architecture is that it is very simple to implement. [10]

2) The production system architecture: This architecture is based on a rule base, whose validity is checked regularly. When the conditions that validate a rule are met, it activates and performs a given action (this system is very similar to an inference engine).

3) The classifier architecture: Brings evolutionism to the previous architecture. When a rule is activated, its effect is evaluated by the classifier and its relevance to the problem being recorded. Effective rule will be better noticed as a very effective rule. Subsequently, the Top rated rules are more likely to be activated and will carry more weight in the evolution of the agent.

The design of the virtual student will be based primarily on horizontal and modular architecture, and will evolve into the architecture of the classifier to approach the human reasoning.

C. The BDI model

1) Beliefs: The beliefs of an agent are the information that the agent has on the environment with all its entities. They may be incorrect, incomplete or uncertain and because of this they are different from the knowledge of the agent, which is always true data. Beliefs can change over time as the agent evolves, interacts and collects more data.

2) Desire: Desires of an agent represent what he would like to see achieved. An agent may have conflicting desires, in this case, he must choose between his desires a subset that is consistent. This consistent subset of desires is sometimes identified with the goals of the agent.

3) Intentions: are the desires that the agent has decided to accomplish and actions that he decided to perform. Even if all the desires of an agent are consistent, the agent may not be able to accomplish all his desires at once.

BDI theory of rational action is a theory of practical reasoning that tries to explain how people reason in everyday's life, deciding at each moment, what they have to do. Intentions play a fundamental role in practical reasoning, because they limit the choices a human (or an artificial agent) can have to a certain point.

4) BDI functions: These functions are an adaptation of the basic functions of the BDI model [11].

Maj(cr,p) is the function updating the agent’s beliefs when it receives new perceptions (p) on the environment, where (cr) represents the set of agent’s beliefs. It is performed by the beliefs management module.

Opt(D,I) represents the decision-making process , taking into account the desires and current intentions. This function is performed by the decision module.

chgD(B,D,I) allows changing desires of an agent if his beliefs or intentions change, to maintain the consistency of the agent's desires. This function is also performed by the decision module.

filtre(B,D,I) decides intentions to follow; it is produced by the action plans creation module, which has the responsibility to build and validate partial plans after checking the plans database to carry out the intentions of the agent, taking into account new opportunities.

AnalyseOpp(cr,I) detects new opportunities towards the agent's intentions realization or may even prevent their achievement. This analysis is done by the action plans creation module. A resulting plan is a sequence of actions to be executed in time.

D. BDI strategies

But what happens if the realization of an intention becomes impossible when the agent choose to execute it, due to a change in environmental conditions? And other intentions become more viable to achieve, and how long the agent remains committed to the intention? The answer to these questions is given by the commitment strategy selected. We can identify three main commitment strategies:

1) Blind commitment (or fanatic commitment): the agent will keep his intentions until he believes they are made. This strategy is not the best if the environment changes between when he selected (filtered) his intentions, and the time these intentions must be completed.

2) Limited commitment: the agent will maintain its intentions until they are completed, or until he believes they are no longer achievable.

3) Open commitment: the agent maintains its intentions as long as they are also his desires, and once the agent has concluded that his intentions are no longer feasible, it no longer considers them as part of his desires. It is the strategy chosen in our design.

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1 B =B0

2 D =D0 3 I = I0

4 repeat

4.1 get new perceptions p 4.2 B = Maj(B, p) 4.3 I = Opt (D, I) 4.4 D = chgD (B, D ,I) 4.5 I = filtre(B, D, I) 4.6 Pl = AnalyseOpp(B, I)

4.7 while (PE<>{} and unaccomplished(I, B) and possible(I, B))

repeat

- x = first(PE); execute(x);

- PE = left(PE)

- get new perceptions p

- B = Maj(B, p) - D = chgD (B, D ,I) - I = filtre(B, D, I) - Pl = AnalyseOpp(B, I) end while

until agent stops

end

E. Normative multi agent systems

Our goal is the simulation of a student community (a specific sample of people, a social group) to use their interactions through an E-learning. A social group is a group of people with common characteristics or goals. But how to implement the spirit of organization and rules and standards in this community ?

1) Social Standards: are set of rules of conduct in a society or a social group, including ways of acting; they define the field of social action specifying what the individual can or cannot do, and gather rules of conduct and behavior patterns prescribed by society.

Social Standards (norms) come from customs, traditions, value systems gradually developed within the society, resist it can lead to marginalization [13].

There are formal standards (written laws, different codes and regulations) and also informal norms which are actually customs (politeness, meals rhythm). A non-compliance with these standards usually leads to penalties.

“Since the use of norms is a key element of human social intelligence, norms may be essential tool for artificial agents that collaborate with humans, or that are to display behavior comparable to human intelligent behavior.

By integrating norms and individual intelligence normative multi-agent systems provide a promising model for human and artificial agent cooperation and co-ordination, group decision making, multi-agent organizations, regulated societies, electronic institutions, secure multi-agent systems, and so on.” [27].

2) The normative approach: The normative approach can be achieved by normative multi-agent systems that combine social norms and multi-agent systems. These systems offer the possibility of integrating the social and individual factors, and provide increased fidelity to modeling social phenomena such as cooperation, coordination, collective decision-making, organization levels…, in human and artificial agent systems.

Normative multi-agent systems are based on the BDI model upgraded with the inclusion of commitment and standards. Commitments are the individual desires of each agent, whereas standards are the general desire of the society as a whole.

Normative agents must have the ability to:

 represent the standards in a format that allows them to change during the lifetime of the agent (knowledge representation).

 recognize and deduct the standards of other agents based on observations and interactions, while separating the standards of individual rules and constraints (learning theory).

 transmit the standards, both active and passive way, to other agents (communication and theory of networks).

3) Standards in software engineering: Here, the focus is usually on action rules that can be used to influence the behavior of an agent and thus reduce the size of the search space. These constraints can be either rigid, in this case, the standard must be met, and is considered a global constraint, or flexible, when the obedience of the standard depends on the agent’s decision-making mechanism.

To use standards in multi-agent systems, they must be specified in a way that allows them to be treated by artificial agents. Research in this field is still in its beginning due to a limited scope on the practical application of normative agents.

There are several systems of representation used in recent studies such as condition/action in rules-based systems, and game theory.

4) La The condition/action in rules based systems:

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This representation format is commonly used by systems that operate offline, where standards are hard-coded directly in the agent's decision-making module.

5) Game theory: Each agent is capable of making a simple choice that yields a corresponding payoff. At each round of the game, the agents attempt to maximize their payoff by choosing an action based on what they anticipate their opponent to choose.

Norms are represented by the strategies that an agent uses to make these decisions. A norm emerges when the number of agents in the population playing by the same strategy exceeds some tolerance value. Like the condition/action pairs of rule-based systems, this scheme is commonly used in systems where the norms are designed and encoded offline. [14]

6) BDI normative MAS architecture: Our normative MAS architecture is based on the ITS architecture model by Beck, and the normative BDI agent structure we described before.

Virtual Student global architecture based on a BDI normative MAS

F. Implementation

The previous architecture was implemented in a JADE environment, using the Lucene software and a custom web crawler specific for a virtual student.

Virtual Student architecture based on a BDI normative MAS

The VSC is composed of several VS that will interact with an e-learning platform, to facilitate exchanges with various modules (tutorials, forums ...)

Use case example: a VSC with an e-learning platform

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The purpose of this approach is to reach both the VSC which animate best the evaluated platform, and to push further research in the field of intelligent and conscious agents, that are aware of their own existence and the world around them, applying the standards ruling their societies, as well as being independent and self evolving.

REFERENCES

[1] METHODES D’APPRENTISSAGE INSPIREES DE L’HUMAIN POUR UN TUTEUR COGNITIF ARTIFICIEL, faghihi usef, université du québec à montréal, 2008, (Baars B. J., 1983, 1988),(Sloman, A and Chrisley, R., 2003)

[2] APPLICATIONS OF AI IN EDUCATION, by Joseph Beck, Mia Stern, and Erik Haugsjaa, mars 2005.

[3] THE PRINCIPLES OF PSYCHOLOGY, William James, (1890). New York: Henry Holt, Vol. 1, pp. 403-404.

[4] ETUDE DU COMPORTEMENT HUMAIN GRACE A LA SIMULATION MULTI-AGENTS ET AUX METHODES DE FOUILLE DE DONNEES TEMPORELLES, mémoire de thèse Stéphane Daviet (mars 2009)

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