The growth of intelligent agent technology allows new developments in the way learners obtain knowledge on the Internet. This paper proposes an agent-mediated online learning architecture, called AMOL, to automate online learning processes. Asynchronous Learning Networks (ALN) are networks of people who can learn anytime anywhere. ALN combines self-study with substantial, rapid, asynchronous interactivity with others. In ALN learners use computer and communications technologies to work with remote learning resources, including coaches and other learners, but without the requirement to be online at the same time. The most common ALN communication tool is the World Wide Web. By this definition, a web-based workshop that requires frequent online conferencing and collaboration with others is ALN. So is a text- or computer-based training course that requires learners to use email to discuss assignments with each other and with the coach. ALN also encompasses a proctored examination at a specified time and place, or occasional synchronous chat or lab sessions for near-campus learners, or an in-person kickoff meeting. By this definition, distance education based primarily on a synchronous audio or video presentation or conference is not ALN because these constantly require learners and instructors to be available at the same time. A videotaped course or mail-based correspondence course or computer-based training is not ALN because these do not
include substantial and rapid interactivity with others, even though the learner might mail in a paper or test and receive a reply days later.
There are many challenges facing ALN. In distributed learning environments where there is the potential for losing the cohesiveness and spontaneity of the classroom experience, it is essential to understand how to improve the online learning experience so that it approaches and perhaps even exceeds more traditional learning methods. The instant availability of a human tutor online would be ideal. However, providing this capability is no more realistic than continuously providing a human tutor for the traditional classroombased learning experience. Cost and availability are limiting factors in supplying continuously attentive human tutors. Often students simply want questions answered and would be happy with any type of effective immediate feedback – human or machine. An augmented anytime capability is particularly important in learning environments in which online tutors may not be available for extended periods (e.g., due to differences in time zones or to late-night student study habits).
ALN courses can be improved by the introduction of autonomous intelligent agents [
In contrast to the above Web-based intelligent agent system where “knowbots” sits on the
server side, resulting in increased latency in tutor response to student actions, the Adele
(Agent for Distance Education – Light Edition) [
Most of the existing intelligent agent online learning systems only focus on applying static intelligent agents that reside at Web servers or Web browsers in solving distance education problems. For instances, “knowbots” and Adele. System designers have ignored another key attribute of intelligent agents, that is, their mobility.
In this paper, we focus on the use of the mobility of intelligent agents in automating online learning process and proposed an agent-mediated online learning (AMOL) architecture. Under AMOL scenario, (1) online learning is able to choose most suitable study material or ongoing or coming conferencing systems with the aid of searching intelligent agent; (2) pedagogical agents residing at online learners’ PCs can automatically gather online teaching material with the aid of gathering intelligent agents; (3) once pedagogical agents cannot answer students’ questions, it will automatically try to obtain solutions with the aid of querying intelligent agents.
The following sections are arranged as follows: Section 2 introduces intelligent agent technology; Section 3 describes the agent-mediated online learning (AMOL) architecture; Conclusion is drawn in the last section.
Intelligent agents are one of the fastest growing areas of information technology. They
offer a new paradigm for developing software applications. More than this, agent-based
computing has been hailed as "the next significant break-through in software
development" [
An agent's role is of one action on behalf of others. In the field of artificial intelligence
this term is used to refer to an entity that functions autonomously in a particular
environment and provides service to its owner. This agent is autonomous in the sense that
its activities do not need human intervention. People have long dreamed of intelligent
system which can let them "input less and output more" to replace traditional ordinary
applications with little flexibility that depend on step by step learnings to be input.
Intelligent agent research is trying to narrow the gap between the dream and the reality.
The hope is that agents will provide services to people without requiring them to
explicitly indicate the procedures required, make appropriate decisions in unexpected or
novel environments, plan their action in advance and tackle problems independently.
The term "agent" has its background in the early artificial intelligent approach to the
humanoid entities. During the long period of development of artificial intelligence the
term was applied to a wide range of fields and attracted different definitions. Genesereth
[
Mobile agents owned by a user or another software element are capable of migrating
from one computer to another to execute a set of tasks on behalf of their owner. Such
agents would typically gather and analyze data from a multitude of nodes on the network,
and present a subset of this data as information to a user, agent or system. For example, a
company which needs to order additional paper supplies could have agents monitoring
the quantity and usage patterns of paper within the company and launching buying agents
when supplies are becoming low. Those buying agents automatically collect information
about vendors and products that may fit the needs of the company, evaluate the different
offerings, make a decision about which merchants and products to pursue, negotiate the
terms of transactions with these merchants and finally place orders and make automated
payments. Mobile agents can also act as brokers for users. For example, a single sign-on
agent can sign-on to many different systems relieving the user from typing in his/her
password for every system [
Mobile agents are said to be autonomous, in the sense that they can make their own decisions while away from their host. This implies that a mobile agent (agent, for short) is not just a piece of data being transferred between systems, but may also carry some logic (i.e. code) and state, which enables it to perform some part of its tasks in one system, migrate to another and continue its work there. In this paper we will mainly focus on a discussion of mobile agents.
A mobile agent framework naturally draws upon existing components of networks such as name servers, directories, routers and so forth, and adds several new facilities:
• •
These may be expressed in various procedural languages and may transport knowledge expressed in various forms. They must be structured so as to engage in a progressive dialogue with the Agent Meeting Places (AMPs) until they are able to execute or are rejected.
Two kinds of languages are involved. One is the language in which the programmatic content of the agent is written; this is usually (though not necessarily) a script language. The second is a language for knowledge representation, which provides the means to express goals, tasks, preferences, and vocabularies appropriate to various domains. •
Agent Meeting Places These have various subcomponents, and they are the principal means by which a server becomes part of the agent framework. We think of the AMP as a broker between the agents that are making requests for resources and services and the applications that implement these resources and services. •
Public Security Services These security services, which are trusted by the servers taking part in the agent framework, provide certificates of authenticity and other security services to the mobile agents.
In many cases, the mobile agent is launched from a client device such as a laptop or desktop PC by an otherwise conventional application. We anticipate that end-users will not in general write their own agents (though that is certainly possible), but that various classes of agents will be distributed by services for use by their subscribers or will be packaged with client applications. The agent is initialized with the user's task and transmitted by a message channel. The sending client may specify a destination service directly, but more than likely it sends the agent initially to, for example, a Yellow Pages server, which can propose servers to be visited that are likely to be able to fulfill the user's task.
When the agent reaches a server, it is delivered to an Agent Meeting Point (AMP). Upon arrival at an AMP, the Agent Passport is inspected for its authentication credentials. Once validated, the AMP examines the agent's Table of Contents (TOC). Ontologically named service requests are resolved to determine if the desired services are available at the AMP. If sufficient resources are available at the AMP and are permitted to the agent, the constituent parts of the agent are passed to the services.
The executable portions of the agent are then started. In some cases, the mobile agent will be interacting directly with server resources (via proxy objects, which enable access control to be enforced). In the other cases, the mobile agent will interact with a static agent at the AMP. A static agent is an agent that is resident at that AMP. The static agent may have been installed by the server operator. Static agents enable the server's function to be personalized by the server's owner or by users. When the agent has successfully completed its task at this server, it may ask to be transported to a new host. Alternatively, it may launch a smaller agent to deliver the acquired information to the sending client or to another server and then it may terminate. This ability to acquire knowledge and transport it from place to place is a key attribute of a mobile agent. The new knowledge may simply a new destination or it may be a security token or a transaction. This ability means that the agent is not merely a program that is executed on a remote host and then returned to its origin, but it is a dynamic process which progressively accomplishes a task by moving from place to place.
If the agent proves to be unauthorized, or if the meeting point is unable to provide the agent with the resources it has requested, the AMP will take action based on the agent's header. It may discard the agent; send the indicated party a description of the failure; or if it is capable, propose one or more AMPs that may be able to satisfy the request.
Our agent-mediated online learning (AMOL) architecture aims at automating online learning process by use of mobile agents. AMOL is apply to the following scenario: Learner Group
Learner Learner Learner Learner
Education Center Group
Education Center 1 Education Center 2 Education Center i Education Center n
Teacher Group
Teacher Teacher Teacher Teacher As shown in Figure 1, AMOL scenario consists on three parties of participants: 1. Learner Group: the set of learner who cannot attend conventional classes due to distance or time constraints, but have personal computers connecting to the Internet and try to learn some online material. 2. Education Center Group: the set of education center servers which provide online study material, assignment repository, “knowbots”, knowledge databases, various pedagogical agents, and etc. 3. Teacher Group: the set of teachers who can answer students’ questions and check the assignments for students, and possess personal computers connecting to the Internet.
In order for pedagogical agents to run, learners have to install necessary software on their
computers to support mobile agent language. In AMOL, the Aglets Software
Development Kit [
The above process can be regarded as establishing “tutorial rooms” for online learners.
Assume that an online learner want to study some online material. He creates a searching intelligent agent and tells it his requirements for the pedagogical agents or conferencing systems. These requirements encompasses:
3. Level of study material (for example, primary, secondary or high levels)
In case of searching conferencing systems, once suitable ongoing or coming conferencing systems matching the requirements of the online learner are found, the searching intelligent agent reports its findings to the online learner and then automatically lapses. In case of searching online material with pedagogical agent, once suitable online material with pedagogical agent matching the requirements of the online learner is found, the searching intelligent agent invites the pedagogical agent to the online learner’s PC and then automatically lapses.
As shown in Figure 1, the searching intelligent agent finds what it wants at Education Center i and then lapses there.
As the searching intelligent agent indicates, a pedagogical agent at Education Center i brings necessary teaching material to the online learner and resides on the PC of the online learner. In AMOL, the pedagogical agents are supposed to be animated pedagogical agents.
AMOL architecture on the PC of an online learner can be depicted as:
Simulation Engine and GUI Animated Persona
Reasoning Engine Text-to-Speech
Engine
Material & Knowledge
Store
Agent Factory
Task Planner
Assessor Client Server
Learner's PC As shown in Figure 2, AMOL architecture in a learner’s side consists of four main components: the pedagogical agent, the simulation, the client-server, and the Material & Knowledge Store. The pedagogical agent consists further of two sub-components, the animated persona and the reasoning engine. Text-to-speech engine and agent factory are installed on the PC of an online learner during software installation.
The reasoning engine performs all monitoring and decision making. Its decisions are based on a learner model, a case task plan, and an initial state, which are determined at the Education Center i when the searching intelligent agent interacts with the Education Center i, and on the agent's current mental state, which is updated as a learner works through a case. The record of the learner's actions is saved in the Material & Knowledge Store where it is used to assess the level of the learner's expertise and determine how the pedagogical agent will interact with the learner in future cases.
The animated persona is simply an Aglet that can be used alone with a Web page-based JavaScript interface or incorporated into larger applications, such as the simulation-based exercises. The persona applet allows animation frames to be easily added and exchanged to support a choice of personas.
The simulation can be authored using the language or authoring tool of one's choice. All simulations communicate with the agent via a common application programming interface (API) that supports event and state change notifications.
The integrated system is downloaded to and run on the client's side for execution
efficiency. This is in contrast to the architecture of most other Web-based Intelligent
Tutoring Systems where the intelligent tutor sits on the server side, resulting in increased
latency in tutor response to learner actions [
After the pedagogical agent resides on the PC of the online learner, it performs tutoring on basis of task plan and feedback.
Depending on the context, a task can be a simple sequence of steps or it can be a complex
and non-linear partial ordering on a set of steps. As Adele, AMOL represents all
procedural tasks using a standard hierarchical plan [
Preconditions and end conditions are represented by Boolean expressions in conjunctive normal form. Steps are supported as goals by sub-classing the expression tree to include a new type of Boolean, a step expression, which, like Boolean constants, logical expressions, and comparative expressions, evaluates to true or false. Evaluating a step expression is equivalent to evaluating a step's end conditions, which is done recursively in the case of complex plans. This extension makes it possible to support the creation of both goal- and task-based plans.
The plan hierarchy is evaluated at each step to account for the dynamic nature of a simulation and the unpredictability of a learner's actions. Actions whose goals become 'undone' are automatically re-executed while those whose goals are implicitly satisfied are skipped. In this way, AMOL's task plan is dynamically updated.
The reasoning engine runs in three modes. In its most restrictive mode, it will simply block actions whose preconditions are unsatisfied. Same as Adele, AMOL uses this opportunity to provide unsolicited feedback about what should be done to satisfy the desired step's preconditions. The persona displays a Hint button so that a learner may also ask for hints directly, before guessing or taking an incorrect action. (Similarly, the persona has a Why? button, that allows a learner to ask for a rationale.) In practice mode, the engine does not block - the learner can make mistakes - and does not provide unsolicited feedback, but still allows a learner to ask for hints.
Once the teaching material available at the Material & Knowledge Store is not enough, the pedagogical agent will generate and launch a gathering intelligent agent which roams among the education centers, gathers related teaching material and brings them back.
Learning process is interactive. With the limited knowledge store at the learner’s side, the pedagogical agent may not be able to answer various questions and judge various answers from learners. In this case, we can consider making use of those “knowbots” residing at distributed education centers. For this purpose, the pedagogical agent creates and launches a querying intelligent agent from the online learner’s PC. The querying automatically roams in a series of online education centers and interacts with those “knowbots” to solve problems as shown in Figure 3.
Learner
Knowbots
Knowledge
Base
Education Center If the querying intelligent agent cannot solve problems with the aid of knowledge bases of online education centers, it visits a series of online teachers’ PCs as shown in Figure 1. Why do we need the querying intelligent agent to visit online teachers’ PCs one by one instead of broadcasting queries to them? The reason is to avoid repetition of teachers’ works.
Once the querying intelligent agent gets solutions, it immediately sends them to the pedagogical agent in the learner’s side and then automatically lapses.
Once the pedagogical agent accomplished its teaching mission at the PC of the online learner, it erases all data in the Material and Knowledge Store and brings the study record of the online learner back to the Education Center i. Then Education Center i save the study record and study period into a database for future use.
Most of the existing agent-based online learning systems only focus on using static agent to solve only learning problems and ignore the mobility attribute of intelligent agent. Distinguishing from the traditional online learning systems with single education center, an agent-mediated online learning (AMOL) architecture under which multi-systems for online learning collaborate to automate online learning processes has been proposed above. AMOL made full use of the mobility of intelligent agent. Mobile agents in AMOL can be classified into: pedagogical agent, searching agent and querying agent. The advantages of AMOL lie at: (1) online learning is able to choose most suitable study material or ongoing or coming conferencing systems with the aid of searching intelligent agent; (2) pedagogical agents residing at online learners’ PCs can automatically gather online teaching material with the aid of gathering intelligent agents; (3) once pedagogical agents cannot answer students’ questions, it will automatically try to obtain solutions with the aid of querying intelligent agents.
In our future work, we will proceed to implement AMOL architecture by using Aglets Software Development Kit. Interested parties are welcome to comment on AMOL and offer valuable advices about implementation.