E-learning Distributed Framework using Intelligent Agents Dan Gâlea1, Florin Leon2 and Mihai Horia Zaharia3 1
Dept. Comp. Eng., AI Laboratory, D. Mangeron 53A, Iaşi, 6600, Romania
[email protected] 2 Dept. Comp. Eng., AI Laboratory, D. Mangeron 53A, Iaşi, 6600, Romania
[email protected] 3 Dept. Comp. Eng., HPC Laboratory, D. Mangeron 53A, Iaşi, 6600, Romania
[email protected] Abstract: In this paper, we discuss the main issues concerning e-learning and its advantages over traditional instruction. We present a few possible implementation approaches and insist on the use of intelligent agents for e-learning. We propose a framework of intelligent agents with BDI architecture that can search server knowledge bases in order to investigate the knowledge sub-graph until all knowledge items are given perceptual explanations.
1. Introduction E-learning is generally defined as electronically based training or instruction. Whether user access is achieved through a browser (in the Internet or an intranet) or other media such as CD-ROM, the tendency is to enhance flexibility and availability by taking advantage of the World Wide Web continuous expansion. E-learning has been constantly improved, to such an extent that it now includes context based animations, simulations, and interactive tasks besides the classical audio-video methodology. E-learning content can easily be delivered alongside electronic discussion forums, streaming media lectures, online documents and quizzes; and it can augment actual classroom discussion mainly because of its cost-effectiveness. Among its main advantages, one should consider [2]: • • • •
Just-in-time training: Users can train as their schedules allow, at their own pace. This high availability helps your employees better manage their time; Browser-based: Content is presented in the familiar environment of a Web browser. Most employees are connected to a company's intranet or have access to the Internet; Easily updated: Changes can be made on the server that stores the content. The update can instantly be accessed, and access to courses can also be easily modified; Global availability: E-learning can be accessed by computers just about anywhere, which is an excellent way to save delivery costs when training employees or customers geographically scattered.
2. Intelligent Agents for E-learning Researchers studied the ways in which e-learning could be practically accomplished. Two technologies seem to have good chances of contributing to successful implementations of e-learning systems [4]: • Vortals (vertical or niche portals): They are specialized, dedicated portals that adapt specific learning collaborative strategies aimed at gaining performance and providing needed information. A learning portal should present different interfaces for different types of learners: one interface to a visual learner, another to an auditory learner, and another to a kinesthetic one. As the learner gains experience, frequently selected options should replace initial default choices. The more individualized the portal, the greater its impact; • Intelligent agents: They are tools that can manage the information overload, serve as academic experts, and create programming environments for the learners. In this way, the learning process is enhanced by having many agents collaborating and competing towards achieving the prescribed goals. Intelligent agents should be able to model the user in order to remember her knowledge, her skills and learning style. E-learning intelligent agents are sets of independent software tools that are linked with other applications and database software running within a computer environment. The primary function of an e-learning intelligent agent is to help a user interact with a computer application that presents a learning domain. Like intelligent agents in general, they should have four main characteristics [7]: • autonomy: the agent operates by itself, without direct human intervention and holds control over its actions and over its internal state; • reactivity: the agent perceives the environment in which it is situated, and answers in a timely manner to the changes in that environment; • pro-activeness: the agent is capable to show a goal-oriented behavior by taking initiatives; • social ability: the agent can interact with other agents or with humans by some communication language. Intelligent agents have three main educational potentials [1]: they can manage information overload, they can serve as pedagogical experts, and they can even create programming environments for the learner. While the distance-learning concept provides more convenient virtual access to learners around the world, it also introduces some limitations and shortcomings [5], mainly from communication, collaboration, pedagogy, and course administration perspectives. One of the most important limitations comes from the lack of direct interaction between the teacher and the student. Using intelligent agents in a learning environment, some of these restrictions can be overcome. Thus, an intelligent agent should be always available for course students, it should understand and interpret student problems (questions and requests), it should have or know a set of actions which may activate according to its recognition of the student needs, and its responses or decisions should depend on the course program, student advancement, student individual troubles and his special interests [3].
3. Proposed Framework Our proposed framework consists of a number of agent servers that have the capability to host and transport agents in the network. Also, servers have local knowledge bases, which the agents can consult. The agents have a BDI (beliefdesires-intentions) architecture. In order to explain how they search for knowledge items in the servers’ local knowledge bases, we will first describe the information type they can handle. We studied the possibility that the agents search for distributed knowledge for an e-learning system. We presumed that every item of knowledge for such a purpose could have two types: • perceptual: knowledge the learner can directly understand, without prior information (e.g.: the concept of natural number or color); • extended: knowledge that requires prior understanding of other concepts (e.g. a programming language cannot be understood without knowing what a computer is). It was assumed that extended knowledge items are based on a limited number of perceptual knowledge items. The system was designed to be very general and flexible, so we didn’t memorize specific information, but references to it. For example, if a certain concept requires a movie and some text for being learned, the agent will find the reference to the movie on one agent server and then will look further for the concepts in the text or the movie that require other concepts in order to be understood. The agent will in fact discover a sub-graph in the knowledge graph, such that all the corresponding knowledge items will be directly or indirectly (through their children) based on perception. It is not practical for the mobile agent to carry the movie across the network, but it’s important to keep the information about its location.
Figure 1. Knowledge graph
Figure 1 shows an example of 4 perceptual knowledge items and 16 extended knowledge items. For simplicity, we imposed that an extended knowledge item could extend between two and four other knowledge items (either perceptual or extended). The knowledge base will contain information such as: KI_1 perceptual KI_5 extends KI_3 KI_2 KI_14 extends KI_13 KI_8 KI_12 This knowledge base is distributed on the agent servers. We permitted an information overlapping of 20%, i.e. the same piece of information has a probability of 20% to be in more than one local knowledge base. Agents’ desires lists are randomly initialized. When an agent arrives on a server, it asks the server the definition for the unknown concepts. The agents and the servers communicate by KQML messages. After the server answers with the definitions from its local knowledge base, the agent adds them to its beliefs list and updates its desires. The concepts that are now defined are removed from the desire list. However, if the concept extends other concepts that are not in the beliefs list yet (i.e. concepts the agent has not encountered before), those concepts should be added to the desires list as well. When the server has no more answers in its local knowledge base, the agent randomly chooses an unvisited neighbor to dispatch to. After the agent has visited all the servers in the network, it may still have unfulfilled desires. In this case, a new visiting cycle begins. The process continues until all the items in the desires list are given definitions. If the agent must find the sub-graph for a concept that is high in the concept hierarchy, the agent should come across a great subset of the set of concepts. In order to achieve that goal, more visiting cycles may be necessary. Let S be the number of servers and Ag the number of agents in the system, D the number of dispatches, Q the number of questions the agent askes, A the number of answers received by the agent. Let Kp be the number of perceptual knowledge items, and Kt the total number of knowledge items. Let M be the total number of messages passed between agents and servers is displayed. Table 1 shows the behavior of the framework under different initial conditions.
S 2 5 20
Ag 3 10 200
Kp 4 20 20
Kt 20 170 500
Min. dispatches D Q A 2 27 14 10 79 19 29 237 23
Max. dispatches D Q 2 37 29 234 148 1573
M A 19 51 94
157 3505 370460
Table 1. Framework performance for different initial settings One can easily notice that the number of questions is greater than the number of answers, because the local knowledge bases of the servers do not contain all the definitions, and therefore some of the agent’s questions will unavoidably remain unanswered. The speed with witch an agent finishes is not given by the number of dispatches, but by the number of questions answered by servers.
It must be mentioned that these figures have a mainly qualitative purpose. The performance of the system can vary even when the number of agents and servers is the same. First, the distribution of knowledge among servers is random, and the desires of the agent are randomly initialized. Second, the servers that agents dispatch to are also chosen is a random fashion, in order not to bias any specific traveling direction. Finally, when its goal is achieved, the agent returns to its origin and reports the results. The agent then goes into a suspended state. It no longer acts on the environment. It can only be restarted from exterior for a new task, or terminated if its presence is no longer required. 4. Conclusions Our framework showed a way to search for e-learning concepts in a distributed environment. As future directions, we intend to develop the cognitive structure of the agents, based on schemata learning. Schemata are similar in structure to type definition. A concept type may have at most one definition, but arbitrarily many schemata. Schemata are thought to store generic, abstract, or prototypical knowledge, i.e. they encapsulate perceived regularities in experiences of which there have been many particular instances. Once formed, they are used to guide further encoding, organization and retrieval of information [6]. In this way, the agents should be able to learn models of servers and other agents. We intend to implement an agent interaction mechanism, so that one agent on a server should not only benefit from the local server knowledge base, but also from the knowledge bases of other agents on that server. 5. References 1. 2. 3. 4. 5. 6. 7.
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