Published in Journal of Interactive Learning Research (1999), vol 10 number 3.4, pp. 243-262
Learning From Multiple Collaborating Intelligent Tutors: An Agent-Based Approach Konstantinos Solomos and Nikolaos Avouris Learning Technology Group Electrical and Computer Engineering Department, University of Patras, GR-265 00 Rio-Patras, Greece. Email:
[email protected]
Abstract This paper describes an open distributed multi-agent tutoring system (MATS) and discusses issues related with learning in such open environments. MATS is a prototype that models a "one student - many teachers" learning situation. Each MATS agent represents a tutor, capable of teaching a distinct subject. All MATS tutors are also capable of collaborating with each-other for solving learning difficulties that their students may have. In order to build this prototype, the following parts of the architecture had to be defined: an adequate agent architecture and multi-agent platform, a knowledge interchange language suitable for learning tasks and a general ontology of learning environments as a foundation for knowledge sharing. MATS can be used for supporting collaboration of heterogeneous learning objects and for this reason is an interesting paradigm of learning, in the rapidly expanding open distributed world of knowledge, surrounding us. The challenges that the learners face when participating in such environment are also discussed in the last part of the paper where the learners’ roles in the MATS context are described.
Introduction Learning from multiple tutors is a situation that occurs often in traditional education. Multiple complementary views of the same subject are provided often to students by many teachers. This can be the result of accidental or intentional partial overlap of the curriculum subjects or can be an intrinsic characteristic of the educational process as in the case of the complementary views offered by the theoretical and laboratory or practical parts of a taught subject. Modelling this process in a computer-based learning context is the subject of research reported here. In a typical situation, an intelligent tutoring module diagnoses difficulties in the learning process and seeks support by other intelligent tutors. If the support is provided and is in adequate form and level, the new knowledge is transformed and offered to the student in order to help him/her overcome the learning difficulty. There are many issues that have to be addressed during modelling of this process. A high level communication language to be used by the tutoring agents
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has to be defined. This language has to be rich enough to express concepts to be taught in adequate degree of detail, together with the concerned student's model and context of learning. An adequate ontology expressing learning objects has also to be used. This should be able to describe in a metalevel subject-knowledge, exchanged between the tutoring modules. Finally the structure and architecture of the agents involved and their collaboration platform capable of supporting the above tasks, have to be defined. These issues have been a subject of research by our group for a number of years. A prototype has been developed during this process, the Multi-Agent Tutoring System (MATS), based on a multiagent architecture, supporting web-based tutoring modules that can collaborate over a teaching task. The system architecture, the protocols and ontologies used as well as a scenario of collaborative teaching in the context of a Computer Science curriculum are provided in the paper. Main objective of the reported research has been the development of knowledge interchange mechanisms among tutoring agents with the purpose of supporting learning tasks. The agents have been defined as artificial teachers, distributed over the Internet, heterogeneous in terms of knowledge structure and content, supporting various interaction paradigms. The fact that these heterogeneous agents can communicate with each other is owed to the common ontology used for expressing their content and to the communication language that they can use. As a result of the collaborative nature of MATS tutoring-agents, they can improve their teaching capabilities and their effectiveness. An example of collaborative teaching is the case of a learner that has been following more than one course contemporarily or has completed some courses taught by other agents in the past. This fact, known to the agents concerned, is reflected in the corresponding agents Student Models. The previous knowledge of the learner is used so that explanatory analogies from already known subjects to be provided to the student, thus enhancing the learning process. Also collaboration of tutors can result in avoiding repetition of areas already covered by other agents if this is known to the tutoring agent. The computational foundations of the reported research lie within the area known as Distributed Artificial Intelligence (DAI), (Jennings 1996). Multi-agent systems are a known application of DAI (Huhns and Singh 1998). Multi-agent systems have already been applied successfully in many application areas, while some efforts have already been reported in interactive learning. The innovative nature of the reported research lies in the use of agents as mechanisms for knowledge exchange between tutors and the attempt to define a knowledge interchange mechanism rich enough to support multi-tutor collaboration. The process modelled is a very complex one, and for this reason, many simplifying assumptions had to be made during development of a prototype used for a full-scale multi-subject collaborative teaching experiment reported here. However many interesting features have been discovered during this process, while further development of the concepts and the prototypes is necessary and is under way.
Multi-agent System Architecture The main computational entity of the MATS system is that of an agent. Agents in our context are active persistent software components that perceive, reason, act and communicate with their
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environment and other agents. Our agents are fairly complex, since additionally to the capabilities of a normal tutoring system, they posses the ability to communicate over the internet with other agents and collaborate with them. Usually an act of collaboration is initiated by an agent requesting support during an instructional activity. The agents respond to the collaboration-request message in an asynchronous way, if they judge that they are capable of supporting the requester, while they perform their normal tutoring activities. The inter-agent collaboration is facilitated by a special type of agent, called Broker Agent (BA). These BA contain models of a number of Tutoring Agents (TA). The models within BA, contain adequate information in order to serve collaboration requests of TAs. Tutoring Agent-1 Userclient
User-server module
Broker Agent
Main course Domain Knowledge
Agent interaction module
Tutoring Agent-2 Tutoring Agent-3
Other courses
Figure 1. MATS Architecture Inter-agent communication is based on a KQML-like language, which has been defined in the frame of this research. This is called Educational Agents Cooperation Language (EACL). A full description of EACL can be found in (Avouris 1999). As described in (Genesereth 1992 and Finin 1993), KQML is a high-level agent communication language based on speech-acts theory (Austin 1962 and Searle 1969). The performatives (commands) of KQML have been defined as a library of communication acts. EACL uses the KQML performatives in order to model learning actions like knowledge transfer, knowledge request, inform about agents capacity, registration and unregistration of agents etc. A representative set of EACL commands is included in the next section. There are EACL commands supporting Tutoring agent-Broker agent communication, concerning registration of a tutoring agent (Announce_course, Announce_change, Announce_removal, Announce_busy), initiation of a knowledge request (Ask_for_relevant_course) and possible answers of the Broker agent (Service_done, Reject, Contract_request). As far as Tutoring Agents peer-to-peer communication, there are functions dealing with the initiation and fulfillment of a contract process (Bid_offer, Sign_contract, Accept_contract, Reject_contract etc.). The organisational structure of the MATS system can be described as a non-mediated open agent society, where the facilitators (BA) provide services informing agents about other members of the community and their availability, without interfering in the inter-agent relations or making any attempt to balance the workload. The open architecture of the system, which does not necessitate point-to-point connections between the agents or establishment of permanent links, means that
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Tutors can enter or leave freely the community, without affecting the performance of the system. In principle, any TA can communicate with all available fellow TAs requesting their assistance for learning difficulties of her learners. For practical reasons however, the members of an acquaintances-model (called ‘Trusted agents’) which is built by each agent, are used in order to focus search for interaction partners. The "trusted agents" are used in combination with an open request for bids mechanism, similar to the contract net (Smith 1988) for selecting the most appropriate offer for collaboration and establishing a "Collaboration thread". A bid from a trusted agent receives higher priority and is preferred to one of not-known agent. The main functionality and structure of the MATS agents are described in the following section.
The Broker Agent (BA) The Broker Agent is an asynchronous, autonomous agent. Its main functionality is to provide directory services to the Tutoring Agents. There can be more than one ΒΑs, exchanging information among them. A BA manages a neighbourhood of agents, maintaining a model of each one of them, describing information about their capabilities, areas of expertise, level of course material, form etc. In the future there is provision for including information about charging mechanisms as well. The BA also collects information and statistics of the agents status and availability. The BA receives requests for collaboration, which it distributes to the available relevant agents. However the subsequent stages of the bidding process take place directly between the requesting agent and the bidders, as discussed in the next chapter.
Tutoring Agents (TA) The Tutoring Agents (TA) are asynchronous, autonomous components that represent interactive learning software units within a society of collaborating tutors. They cover different subjects, which are defined as the areas of expertise that they can teach. While the interactive learning software contained in different agents can vary, their interface to the society of collaborating agents is standard. They all contain a description of their capabilities, a detailed model of their knowledge, expressed according to an ontology of tutoring systems. They maintain models of other agents (trusted agents mechanism) and they communicate with other agents and the BA in EACL. In the frame of our research, we have pursued two distinct complementary goals in terms of the TA architecture. One relates with definition of a generic TA architecture for agents built from scratch and inserted in MATS, while the second is the definition of necessary mechanisms for transforming an existing tutoring system in a TA. The second objective is more interesting and hard to achieve, since it depends on the specific characteristics of the individual components, their architecture, their APIs, their interaction model and their knowledge structure and representation. Given the difficulties of the latter task, we have decided to develop a generic TA for the purpose of our experimentation, giving it a modular structure. So the Collaboration Component (CC) of the agent built, is used for all MATS agents, while the Interactive Learning Component (ILC) having a well defined interface to the CC, in the future will be substituted by existing interactive learning systems of new agents. The user interaction aspects of the generic agent need also to be considered. While the interaction style of the specific component of the ILC can vary, there are some performatives that the CC can direct to the user, as discussed in the scenario described later in this
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paper. The CC needs to explain to the user that a new interaction thread is about to start with Agent Ai and to request at the end of the session a judgement on the relevance and the usefulness of the provided material, in order to update the trusted agents model. This interaction has to be managed by the CC and thus adequate constructs and primitives have to be defined. The TAs developed can interact with their user from a distance since the agent is built according to a client/server architecture, as shown in figure 1. The client component handles user-generated events and presents information send by the TA-server. The user responses are codified accordingly and forwarded to the server. The developed client/ server system is based on a web server / Java servlets technique as described in more detail bellow, presenting the information to the user through a web browser. The architecture of the developed generic TA is shown in figure 2. The main part of the agent is the Agent Controller that manages the resources of the agent and is the main locus of control determining agent behaviour. The controller maintains data structures that contain the status of interaction threads with other agents, manages the interaction input and output queues, issues queries to the domain component. The Communication Component is responsible for generating and receiving messages in EACL. It operates in an asynchronous way with respect to the Agent Controller and maintains the input and output queues. The Student models and the Trusted agents models are used for managing interaction with the learners and other agents accordingly. The student models are based on an overlay model (Carbonell, 1970), and represent the learners progress with the taught subject. While some form of Student Model might already exist in the Interactive Learning Component, it has been considered necessary to maintain a form of SM in the Agent, in order to be able to answer requests of other agents on the specific learner and adapt the agent collaboration to the specific learner characteristics. The Domain Model is a map of the Interactive learning Component, expressing in a well-defined multi-layered ontology, the domain. The queries addressed to the agent are sent to the Domain Model by the Controller. The objects of the DM can address the specific parts of the Learning Component. While the Interactive Learning Component can be different from agent to agent, the Domain Model has a common structure. It is through this mechanism, that it is made possible to incorporate heterogeneous components in the
learner
Interactive learning component
Domain model
Student models
Agent Controller
Trusted agents
Communication Component
EACL messages to other agents
MATS system. Figure 2. Structure of the generic TA
Agent Interaction During typical MATS operation, the BA waits for bidding requests from TAs. Each TA during registration has been assigned to the nearest BA. So each TA knows the BA address for communication. Each TA during registration informs the corresponding BA about its
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characteristics. Part of its description is the higher levels of the Domain Model of the agent, containing the concepts about which the agent can provide instructional support. In a typical learning scenario, the student interacts with a specific tutor. In the case of our generic TA, this takes place through the client-component of the Interactive Learning module, which is a web browser. Let us assume that from the user interaction an event is generated that cannot be handled by the ILC ( a request for a more explicit example, or a simpler exercise, etc). The request in this case is transferred to the CC. The Controller receives the request through the ICL-goals structure. A message for a collaboration bid needs to be sent to the BA. The message contains information about the specific learner and context of learning, as well as urgency information. The BA upon receiving the collaboration request looks into the models of the TAs and their current availability. After selecting the most appropriate agents, the BA generates a bid-request to them and informs the requesting TA about the receivers of the collaboration bid-request. The BA has completed its mission at this stage. More cycles of bidding requests might be generated for the same task if the offers generated during the process are not considered satisfactory by the requesting TA. In this case a second bid request might be issued to the BA with relaxed constraints to be send to other agents or peer BAs. The TAs receiving the collaboration bid-requests, assess their domain model and their current workload and accordingly they compile their bids. The offers of the biding TAs are received and accessed by the Requesting TA. The offers contain information describing the form and position of the offered knowledge in the corresponding domain model. The attributes of the supplied concepts are: concept-description, synonyms, course-level, level-of-difficulty, prerequisites, form-ofinteraction, learners model details, responsiveness-level etc. The requesting TA has to decide on which one of the biding agents to select for collaboration. For this purpose it uses a bid selection algorithm that takes into consideration the following parameters: relevance of the offers to the request, trusted-agents, learner's familiarity with the offered subject. An example of the algorithm in use is included in the last part of the paper. For instance from the offer of TAi can be deduced that the learner has been well acquainted with the subject, but the offer of Taj, teaching the same subject, might be preferred to that of TAi despite of the fact that the TAi has not been familiar to the user. Following the collaborating agent selection, a bid-accept message is sent directly to the selected agent, while bid-reject messages are sent to the rest of the bidders. A collaboration thread is established in this case between the two agents which might include a long exchange of messages and instructional material. The establishment of this interaction thread is notified to the BA. Upon completion of the interaction, information is requested from the learner on the level of relevance and usefulness of the supplied information. The user judgement is used for updating the Trusted-agent model for the particular interaction. The described interaction sequence is the most common and widely used in the MATS community. However there is an alternative direct request mechanism, supported by the developed protocol and the EACL language. This is used in the case of a request done by a learner, with very similar characteristics with a previous successful request. In such case the agent who served the previous request in the past, is contacted first directly, bypassing thus the long bidding process.
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TAi
BA
TAj
7
TAk
query bid request bid request
bid requests sent
bid offer bid offer
Bid accept (contract signing) collaboration query knowledge content reply
Figure 3. Agent interaction sequence diagram
The Domain Model As already described in the previous section, a major effort has been put in expressing the heterogeneous interactive learning systems in a common way. This takes the form of the Agent Domain Model. This component of the agent facilitates interaction and exchange of information with the other agents. The Domain Model has to be based on a language rich and generic enough to express the complexity of the learning process and material. Such a language should be based on an abstract ontology of the educational domain. There has been considerable effort by the research community to define and build such ontologies during the last years. Examples are the work of (Mizoguchi 1996, IEEE 1998, Ikeda 1995) on Ontologies, ITS generators (Pintelas1996), reusability of courseware components (Vassileva 1995), genericity of instructional knowledge independent of domain (Marcke 1996), and research for a standard for educational sharing (IEEE P1484). These ideas have influenced our work, which lead to instructional material multi-layer ontology used in the MATS agent Domain Model. The four layers of the ontology that maintain a hierarchical structure are the courses layer, concepts layer (structured as a conceptual network), instructional microfunctions layer and digital instructional units layer.
Concepts layer A concept is defined as the self-contained knowledge fragment contained in instructional material in digital or paper form. It usually corresponds to a Chapter section in linear textbooks. For
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example "use of while command for implementing loops" can be a concept of the Course: ‘Introduction to Programming languages’. Concepts are connected to a conceptual graph containing Concepts as nodes and links connecting them. In our ontology there are four types of concept links: Ordering Links { Next-And, Next-Or, Previous-And, Previous-Or}, Dependency Links { Prerequisite, result }, Structuring Links { Partof , Is-consisted-of }, Replacement Links { Analogue-to }.
Instructional Microfunctions layer Instructional microfunctions are defined as self-contained instructional units that fulfil a fundamental instructional operation. Teaching a concept is done through a structured set of instructional microfunctions. A sequence of instructional microfunctions is an instruction plan fulfilling an instructional goal, activated by the pedagogical component of a tutoring system. Examples of instructional microfunctions are a clarification, a general example, applied example etc. Microfunctions in printed form correspond to a short paragraph of a textbook. Concept A
Example #1
Example #2
Exersise #1
Figure 4. An example of Concept-Microfunction relation We can distinguish three categories of microfunctions: presentation microfunctions, testing microfunctions, corrective microfunctions. Some examples of Presentation Microfunctions are: Introduction, detailed-description, example, alternative presentation, analogy, conclusion, nextpresentation-selection etc.
Digital instructional units (DIU) layer The digital instructional units (DIU) are units of a instructional software that implement the instructional microfunctions. The units can be found in tutoring systems and their form is specific to a particular computation environment and interaction style. The DIUs are responsible for interaction of the system with the learner. There is a correspondence of the DIU and instructional microfunctions. While the previous layers of the ontology are implementation independent, this layer is the interface to the specific interactive learning environment. For example, a DIU can correspond to an HTML page, an exercise-solving interaction unit etc.
MATS Implementation issues The MATS platform is a layered one. The lower layer deals with safe and reliable data transport. At this level we used the sockets mechanism, Java support and TCP/IP. The environment in which the agents have been developed is that of the Java Web Server1. This makes development of the agents independent of the underlying operating system. The BA and TA agents implementation has been based on the Servlets development construct. This is a Java-based programming technique for developing client/server applications on the server
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side. The servlets are objects residing in the web server environment. Each agent is made of many such servlets implementing the agent components described earlier: There is an Agent-controller servlet, agent communication servlet, agent-domain-model servlet. The latter can contain many more servlets corresponding to concepts (concept-servlets) or instructional microfunctions (microfunction servlets), through which the Interactive Learning Component of the agent is implemented. The user client component is based on web browser who is connected to the web server in whom the agent (agent servlets) resides. The user interface of the generic TA developed has been designed in a standard form shown in figure 5. The client manager manages four frames which allow the student to navigate through microfunctions, concepts, and interact with the digital instructional unit that he currently sees. There is also one hidden frame responsible for the communication with the rest part of the tutoring agent. Some typical messages exchanged between the user-client component and the TA-based interaction server include presentation events (complete presentation, not complete presentation), test events (exercise mark, misconception found), presentation queries derived from the user (generalisation, explanation, presentation from other agent), diagnosis events (misconception solved, new exercise is needed), test queries (hint, or providing partial solution, simpler exercise, harder exercise), etc The inter-servlet communication is effected through the sockets mechanism, making the agent architecture highly modular. The same mechanism has been used for implementing the inter-agent communication, seen as socket-based communication between the interacting agents’ communication-servlets.
Figure 5. User Interface of the User Client Component
1
JAVA Web Server of Sun Microsystems
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The Collaboration Scenario Let us consider a learner interacting with the tutoring agent A (“Introduction to C programming language”). We also assume that the learner has also recently completed the course of tutoring agent B, in our example "Introduction to Programming through Fortran". In the context of interaction with Agent A, the learner is currently studying concept ‘if –else command’, in particular viewing the microfunction ‘example #2' (type: example , dependence : specificity-high, difficulty level : easy, type of coverage: short). Previously the student has seen three more presentation type microfunctions, that is definition#1, Example#1, and Description#1 all of them having easy difficulty level. The user breaks the learning process and asks for a relevant presentation in a simpler form.
Attempt for internal goal satisfaction The User component constructs a message to the Communication Module of agent A. Then it forms a query which is added to the Agent A’s goals. Processing of this query results in no answer since there is no microfunction that can fulfil the query specifications. The content of this internal message is: Student name: ‘user #1’,Request: ‘easier difficulty level.’ Agent A knows from the Domain Model that ‘user #1’ is currently learning .
Request for collaboration In order to solve the request, Agent A attempts to seek support from other agents. Assuming that there is no similar query satisfied in the trusted agents history of interaction, Agent A has to contact the BA and send its problem query, in order to receive bid offers from other agents (EACL ask_about_relevant command). In our example, there are only two tutoring agents making up our agent society. The content of the previous command is as follows: Tutoring agent: #A (‘Programming language C’) Type: (‘Computer science’, ‘Software’, ‘Programming languages’, ‘C ‘, Difficulty level: Academic level) Query data: (concept name:’if else command’, concept synonyms: ‘if command’, ’branching commands’, microfunction type: example, dependence: specificity-high, difficulty level: easy, type of coverage: short, problem type: easier explanation) Student: (Student name:’user1’). Urgency: (Urgency type: very urgent)
The Broker agent performs a query in its database trying to identify all agents that cover the concept requested in the query, filtering out agents incompatible with the requesting agent ( e.g. different language agents). The use of concept synonyms is used if the first attempt does not provide a satisfactory solution. The result is the construction of an agent contact list whose members seem to have common knowledge with Agent A. The next step involves a bid request by BA to each member of the contact list, and ends with informing Agent A about the results of the search providing the contact list.(EACL service_done command).
Contract_request Sender: ‘programming language C’ Mediator:’Broker 1’ Receiver: ‘programming language Fortran’ Content:
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Student: (Student name: ‘ user #1’) Urgency: (Urgency type: very urgent) Query data: (concept name:’if else command’, concept synonyms: ‘if command’, ’branching commands’, microfunction type: example, dependence: specificity-low, difficulty level: easy , type of coverage: short, problem type: easier explanation).
Agent B receives the request message, asserting it to its goal list. The external query processing procedure is similar to the local query processing. One difference is the use of concept synonyms together with concept itself. In different domains, it is common to have discrepancy between local terms and external terms with similar semantics. The use of concept synonyms can solve partially the problem, especially if there is a universal dictionary available, containing all synonym mappings. This is the case with our architecture. The Broker agent maintains such dictionary derived from the agents’ concept lists and synonyms. The query issued to the Domain Model has a hierarchical structure. At the highest level, there is a search for the closest match at the concept level, followed by a search for the corresponding microfunctions which have the same type as the query requests, and at the end a search is performed for those microfunctions that fulfil the difficulty level as well as the coverage type attribute. In our example, the concepts that fulfil the query are: Arithmetic if, Logical if, Combined logical if. The microfunctions that answer the query are and Logicalif, Example #2. The contents of these two microfunctions are shown in Table 1. Concept name: Arithmetic if Microfunction name: Example #3 Microfunction type: example Microfunction content :
Concept name: Logical if Microfunction name: Example #2 Microfunction type: example Microfunction content :
‘ ….If (e) N1,N2,N3 Whereas : e an arithmetic expression N1, N2, N3: command numbers and integer values Meaning: If e0 then go to command with the number tag N3....’
‘…. The command syntax is: IF (L) S Whereas: L : a logical expression S: command Meaning : if the logical expression L is true then execute command S if the logical expression L is false then do not execute command S ….’
Table 1. Contents of selected microfunctions (low specificity query)
Learner-dependent query refinement A special case is when agent B has tutored the user in the past, and therefore there is a student model registry concerning him/her. If from this entry, one can derive that the user has an adequate knowledge on the subject, and if there are no low specificity answers found in the original query, the constraint can be relaxed and a new query can be formulated and processed by B, with the following attributes: Microfunction type= ‘example’, dependence=‘specificity-high’ OR ‘specificity-low’, coverage= ‘short coverage’ .This query permits inclusion of more specific microfunctions, since the user has previous knowledge on the topic. The result of this new query is a new list of microfunctions, which contain examples specific to Programming Language FORTRAN. That is and .
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All agents that can reply to the bid message send their bids to the offering agent A (biddingoffering phase in the contract net protocol). In our case, two bids are generated by agent B which have the following structure: Bid_offer Sender: TAj Mediator:Broker A Receiver:TAi Content: (Concept: (Concept name:’concept 1’), Microfunction: (microfunction name:’mf1’, microfunction type: example, coverage: short, dependence: specificity-low’))
Bid selection Process Agent A initiates an evaluation process, after receiving the bids. A voting algorithm is used for bid selection. Trusted agents bids receive extra points. Also extra credit is given to low specificity offers, since that instructional material is more general and easier to use out of context. Short instructional material is also given preference to longer one, since it interrupts less the main instructional activity. In our example the two bids receive the same vote, since they both are (trusted-agent, low-specific, short coverage) bids, so A selects arbitrarily the first one and sends a contract-signing message to B referring to the selected bid. This message is shown bellow: Sign_contract Sender: ‘Programming language C’ Receiver: ‘ Programming language Fortran’ Contract_ID: #123 Content: (Concept: (Concept name=’logical if ‘), Microfunction (Microfunction name=’Example #2’))
The reply of B contains adequate information for A to be able to present the instructional material of its learner. In our case the reply contains the URL of the corresponding unit.
User Interaction issues The material offered by B is presented to the user through a new, clearly marked, thread of interaction. There is no attempt to hide the fact that there is a new tutor that has intervened, in response to her request, breaking temporarily the established instructional process. This is reflected at the interface level with a new window, in which the source of interaction is shown. It is believed that this interaction model can accommodate for the loss of continuity in style and content, which is inevitable in this case. The user mental model of the system should be based on the metaphor of the "invited professor" rather than the "knowing everything own tutor". The judgement of the student at the end of the invited outsider's session, is likely to be concentrated more on the main issue, i.e. relevance and value of the offered material, rather than be influenced by the break of continuity. Our first findings confirm the observation that today's users, accustomed to hypertextline interaction, are more likely to accept this collaborative teaching metaphor, according to which, their tutoring system, is viewed as an intelligent hypertext-browser, offering links to other tutoring systems with the right content and at the right time.
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Conclusions The presented work proposes an innovative environment in which collaborative tutoring agents can operate. The MATS prototype contains an open number of tutoring systems that can register for collaboration, withdraw from the society temporarily or permanently without affecting other agents tasks. Each instructional node includes a meta-level description of its material according to a commonly used ontology, hierarchically structured in a four-level model. A specifically designed agent communication language (EACL) has been used, supporting the instructional material exchange, based on the widely used KQML language performatives, implementing an adaptation of the contract net protocol for the educational domain. According to the architecture, the knowledge and other instructional material exchanged, is not interpreted by the receiving agent, at the semantic or lexical level. The acceptance of the material is based on receiving agent's trust on the offering agent's self-description, which is the basis upon which, selection of collaboration offers is made. This description, expressed in the common ontology with clear semantics, guides the agents’ interaction. An agents-credibility validation mechanism has been devised, based on user's judgement, the trusted-agents model. This innovative way of building tutors acquaintances-models is based and is promoting synergy of the tutoring system with its learners. Finally an implementation of the proposed architecture has been described, based on Internet technology. On this prototype, the first experiments have been made, using collaboration examples from Computer Science curriculum courses. Through these examples the effectiveness of the architecture and the communication language has been demonstrated. Special attention has been given to the learner’s interaction with the system. As in other cases of user-interaction with distributed multi-agent systems, the transparency issue (Hall 1992), that is the users' view of the distinct agents is an important one. The metaphor reflected at the user interface is that of clear identification of the separate tutors to the user. This seems to remedy and justify the lack of continuity observed at the level of the interaction. The effort so far has been limited in experimenting with agents built with the intention to be integrated in the system. More emphasis is going to be put in the future in experiments with incorporating existing tutoring components with diverse architecture and knowledge structures like intelligent tutoring systems, simulation systems, expert based educational environments etc. This requirement has led the design of MATS during our research and is reflected in our modular agent design, however much more work is needed in this direction.
References Avouris N.M, Solomos, K. (1999), Distributed Intelligent Tutoring Systems in Higher Education, PENED #601, Final Report, University of Patras, ECE Dept, Patras. Austin J. L (1962). How to do thing with words, Oxford University Press. Carbonell J. R. (1970). “AI in CAI: An Artificial intelligence approach to computer assisted instruction”, IEEE Trans Man Machine Systems vol. 11 no 4 pp190-202.
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Acknowledgements Funding for the reported work has been received by the GSRT/PENED Programme, grand number #601/4.1, in the frame of the Research Project "Distributed Intelligent Tutoring Systems in Higher Education".
