1997 icips intelligent tutoring system as multi agent system

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Intelligent Tutoring System as Multiagent SystemMihal Badjonski Mirjana Ivanovic Zora n B ud imac

Institute of Mathematics Institute of Mathematics Ins t i tu te of MathematicsFacul ty of Science Novi SadTrg Dositeja Obradovica 4.2 1000 Novi Sad

Yugos lavia Yugos lavia Yugos lavia

Faculty of Science Novi SadTrg Dositeja Obradovica 4,2 1000Novi Sad

Facul ty of Science Novi SadTrg Dositeja Obradovica 4.2 1000 Novi Sad

Abstract: Intelligent tutoring systems are importanteducational tools. They can be built using differentmethodologies and tools. In this paper a genericarchitecture for intelligent tutoring system as a multi-agent system i s presented. Th e agents in the architectureare programmed with AGLess - a specialized agent-oriented environment. This approach has been comparedto an object-oriented ap proa ch [l], [2 ] based on object-oriented lanbwage Less.

I. INTRODUCTIONToday, there are a lo t of different systems and tools (withor without intelligence) which are to be used in educationalprocesses. Also, a lot of new trends appear in dsigning andimplementing [4] cducational systems which brings a need touse and incorporate in them some intelligent behavior.Building intelligent tutoring system (ITS) from scratch isvery expensive, exhaustive and time-consuming process. In

order to facilitate ITS development, this paper presents ageneric ITS architecture that might be used for every ITS'organization.The architecture is proposed as a multi-agent system(MAS) [6], [SI. It consists of several agents thatcommunicate and cooperatively perform their tasks. As aresult, global system's behavior emerges. System acts liketutor and student is unaware of its decomposition into agents.Multi-agent approach to system organization provides atleast four advantages:

it is easier to program several simple entities than toprogram one complex entity (exploitation of divide-and-conquer strategy),

agent-oriented programming primitives are rathercomprehensive for any programmer and they makeprogramming easier,

MAS approach enables harmless modifications andgrowth of the ITS,

ITS can be distributed on several computers (there arethree possibilities. one computer - one agent. one computer -several agents and one computer - all agents).The research presented in this paper improves by new

approach the rcsearch described in [ l ] , [3] In [ I ] . [3] anobject-oriented way. based on the specialized Lesv language.to ITS development is givenThe agent-oriented approach is based on the environmentfor agent's programming called .1GLess mhich includes Lessas its lower levelThe rest of this paper is organized as follows ITSs aredescribed in the next section Specialized object-orientedlanguage Less is presented in the third section The fourthsection is aimed for AGLess an d a genenc architecture forITS Conclusion is gn n in the last section

I1 INTELLIGENT TUTORING SYSTEMSLesson to be presented is divided in to sm aller pieces called

topics which are someho\v connected in semantic entiretyTopics and links betveen them form a multi-directed graphaccording to a prescribed lesson planIntelligent tutoring systems are exclusively intended forlearning processes To test the knowledge, student hasaccepted during the process of learning, some problems(tasks) are attached to topics These tasks student tries tosolve at some check-points in the grap hITSs are usFally highly specialized systems and that is thereason why they successfully employ the methods of artificialintelligence Architecture and structure of IT S varies, butusually four elements can be recognized [7]

1. Expert (teaching) module is a data base which storesappropriate knowledge.2. Students ' module - is intended for modeling individualstudent's knowledge in the field where ITS is used. Thecontent of the module constantly chan ges during the processpf learning.3. Tutoring module specifies the manner and rhythm ofpresentation of stored knowledge.

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4 Diagnostic module constantly modifies the studentsmodule in accordance with answers that students give toposed questions.

111 LESS IN BUILDING ITSSpecialized object-oriented language Less [2 ] has beenenhanced to support building of ITS Kernel of Less consists

of different basic data structures for informationrcpresentation, and different statements and directives forinform ation presen tation Basic classes giT,e opportunity forstorage of multimedial information Less also possessesmechanisms for creating of new structures

As mentioned in [I], 131, the representation domain(lesson knowledge) can be observed as a complch set ofnodes are topics which

LOGICAL, and NUMBER Basic structured data types ar e0 ' L I S T O F T y p e - the list of arbitran. number of

elements of data type T y p e .T ( T y p e l , . . ., T y p e N ) - n-tuple of ekments ofarbitran. data types T y p e l , . . . , T y p e N

. . . , V a l N ) -enumerated hp ees ar e TEXT. PICTURE

ANIMATION an d SOUND, They have the same structure- an instance variable for representation of specific kind of

information,- a method for presThe four primitiveclass I n f 0, entral eI n f o = C L A S S {i d e n t : S T R I N G ;

( * o b j e c t i d e n t i f i c a t i o n * )k e y : L I S T OF S T R I N G( * l i s t o f k e y w o r d s * )t e x : C LA SS T e x t( * r e p r e s e n t e d i n f o r m a t i oP l c : C LA SS P i c t u r e ;a n i : CLASS A n i m a t i o n ; 6

L T o p i c is th e heart of a niult~-digrap h structure forstoring information information to be presented andinformation based on which a decision o n further learningdirection can be made

L T o p i c = C L A S S i n h e r i t s I n f o (q u e r y :L I S T O F CLASSES ( L T a s k P , L T a s k T ,

L T a s k R , L T a s k B , L T a s k E ) 1Expert module is realized by list of L T o p i c ' s objectsThere are five types of queries The one where student'sanswer should be explicitly typed-in ( L T a s k P ) the queries

when one of several alternatives has to be chosen ( L T a s kT).the queries where correct pairs of items In tno independentlists should be recognized ( L T a s k R ), an d the tasks wherestudent has to execute longer procedure ( L T a s k E ) All ofthese task classes inherit, the common COL T a s k

L T a s k = C L A S S i n h e r i t s I n f o {a l d S l v d : LOGICAL;( * I s t a s k a l r e a d y s o l v e d ? * )d i s p : LOGICAL;( * T z d i s p l a y or n o t t h e c o r r e c ta n s w e r a f t e r t h e w r o n g r e s p od i s p L T : PUBLIC METHOD( * a r r a n g e s a l l t h e i n f o r m a t i o n o n

t h e s c r e e n a n d a c c e p t s s t u d e n t ' sr e s p o n s e * ) }Student's model is maintained in the class S t u d e n t Th e

object of the S t u d e n t class contains a list of topics thatstudent has learnedFinally, the class L e s s o n is defined in [11 as follows

L e s s o n = C L A S S {i d e n t : S T R I N G ;l s n : L I S T O F C L A S S L T o p i c ;( * l i s t o f l e s s o n ' s t o p i c s * )g e n : L I S T O F C L A S S L T o p i c ;( * t o p i c s w i t h e x t r a m a t e r i a l * )s t a r t : L I S T O F I D E N T L T o p i c ;( * s t a r t i n g t o p i c s * )s t o p : L I S T O F I D E N T L T o p i c ;( * e n d i n g t o p i c s * )c u r r T p c : I DE N T L T o p i c ;( * c u r r e n t t o p i c * )c u r r T s k : I D E N T L T a s k ;( * c u r r e n t t a s k * )i n t e r p r : PUBLIC METHOD;s t u d : L I S T O F S t u d e n t }

s o u : CLASS Sound;i n t e r A l l : METHOD( * f o r i n f o r m a t i o n p r e s

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Tutoring module is supported by defined Lesson classi e by making connections betuccn topics and tasks indetermining paths through topicsDiagnostic module is a \\e& point of suggested system Itcan be realized in Less. i n rclativcly rudimentary, butsatisfactory wayAll the classes enlisted in this section, more or lessmodified are also uscd in the agent-oriented approach

IV. ITS ARCHITECTURE AS A MASA . AGLess Environment

AGLess is an environment for agent-orientedprogramming, devclopcd for ITS' construction Design ofAGLess is strongly influenced b? the research described in[j], b ] In HOMAG E-en\ ironiiicvt [j] n agent specificationconsists of two parts On the lower lex1 a mental state of theagent is defined as a set of objects An object could be a C++,Common Lisp or Java object At tlic hig her level. tliere is alanguage ALL (Agent Level Language) vh ic h is used for thedefinition of the agent's initial mental state, mental changerules and for the embedding of objects defined in the lonerlevel into the agent's mental stateLower level of ACLess is object-onented and serves for adefinition of classes in object-oriented language Less At thehigher level, AGLess offers constructs for a n agentspecification, which consists of mental cliange rules, mentalstate definition and m ental state initialization Meatal state ISdefined using classes defined at lower level Methods inobjects correspond to actions of an agent while the object'svariable are used for the agcnt's beliefs Also, every agent isable to perform communicative actions Mental change rulesare tlie main part of an agent's specification and theydetermine the behavior of the agent when it receives ainessage in some mental stateEvery agent in AGLess is able to perform four types ofcommunicative actions

8 ASK VALUE(from-agent, to-agent,belief )

INFORM(from-agent, to-agent,bel name, value 'REQUEST-ACTION(from-agent, to-agent,action)DONE (f om-agent, to-agent, action)REFUSED (from-agent, o-agent, action)

Enlisted coinniuiiicative actions belong to higher levelconstructs in A G h . 5 , as well as beliefs about facts andprivate actions Agent uses coiiiinunication in order to ask orinforin another agcnt about bcliefs It can also request theexecution of private action from another agent Receiver ofsuch inessage wil l try to perforin specified private action a ndreport to sender what happenedAgents do not use objects in their coiiimunicatioii Objectsare lower level constructs used for the specification of privateactions and beliefs (seeTable 1)Mental-change rules arc the iliain part of every AGLessprogram. Syntax for mental-change rule is< r u l e > : = , ' - > ' ' ( ' ' ) ' . is logical e\pression that describes

messages able to activate tlic rule If issatisfied, is ehainined slogical expression describing the internal statc of agent (itsbeliefs) that need to be satisficd for the ru le to fireFinally, if both conditions ar c satisfied, sequence of actions s e\ecutcd (sequence> consists of privateand/or communicative actions as ne11 as of conditional,looping and assignment statement When agent usesREQUEST-ACTION or ASK-VALUE communicative action,it stops the e'iecution of tlie sequence until it receives aresponse and executcs tlic rulc for that respon se After that itcontinues nit h started actions' sequence

Mental-change rules can contain variables Like inAGENT0 [ 6 ] ,he! arc e\istcntiall) quantifiedProgram in .4GLcts is aimed for programming of exactlyone agent It consists of

- mental statc definition (dcfinition of objects),- declaration of beiicfs and private actions (they are dcclaredwith their name and corresponding loner level construct -see Table 1),- part for mental state initialization,- part for mental change rulesB. ITS Architecture

Table 1higher level 1 lowcr level 1private action 1 method in objectbelief about fact I variable in object0

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Intelligent tutoring system can be seen as ai1 agency (seeFig 1 ) consisting ofStudent-Model agent,Tutor agent,Inter face agent

A mental state of the To pic

Topicl agent, . , Topic. agent

is represented using thelhe class L T a s k , the class is also used, but it ismodified In 111, every task has associated the nest actionthat should be performed after finishing current task In theITS represented as MAS, these actions are only local oneThe> cannot activate another topic, but only to activateanother task inside the curren t topic The n est topic selectionis controlled b> the Tu to r agent In the presenting of theirinformation, the Topic agents use the information fromStudent-Model agent (tasks that student has already solvedand the appropriate level of task presentation) Inte rfac eagent supports communication betrvcen student anMAS

Objects of the class L T o p i c the class L T o p i c uses

Topic agents have four private actions available1) activate.3 ) get-17ext-task,All private actions uses appropriate methods from classesalined fo r tasks ( L T a s kP, T a s k T , L T a s kR. L T a s kB nd

L T a s kE)Eve? Topic agent has two mental chan ge rules

Th e rule for handling messages of opeR E Q U E S T - A C T I O N ( t u t o r , t o p i c l , a c t i v a t e ) ,

*Fig 1 ITS as an agency

The rule induces the presentation of unsolved tasks fromagent's topic At the end of tasks presentation,Student-Model agent is informed about the results andTuto r agent is informed about th e finish of the Topic agent'swork

The rule for messages of typeINFORM ( s t u d e n t m o d e l , t o p i c l , ? n a m e , ? v a l u e )-where student's history or current level of expertise isreceived from Student-Modcl agent

Mental space of Topic, agent includes beliefs about factsFOUND. HISTORY, LEVELA multi-digraph which reprcsents the learning process is

part of the Tutor agcnt's mental state The mental state ofthe Tutor agent is implemented xith a new class calledL e s s o n This class slightly differs from the class nith thesame name that is defined in [ l ] , [3 ] This agent uses itsknowledge and the information received from theStudents-Model agent to govern the process of learningAfter it receives a list of alread! learne d topics, Tuto r agentdetermines the next topic using its private action next-topicL e s s o n is defined as folloms

L e s s o n = CLASS{g r a p h : L I S T O F T ( 1 D E N T L T o p i c ,I DE NT L T o p i c )( * l i s t o f d i r e c t e d edges * )s t a r t : L I S T O F I DE NT L T o p i c ;( * s t a r t i n g t o p i c s * )s t o p : LIST O F I DE NT L T o p i c ;( * e n d i n g t o p i c s * )c u r r T p c : I DE N T L T o p i c ;( * c u r r e n t t o p i c * )n e x t T p c : METHOD; 1

Tutor agent uses n e x t T p c in its private actionnext-topic, until it finds the appropriate topic that has not yetbeen learned according to lesson's scenario It ha s two rules

The first rule always executes whenever Tutor agent isactive, It produces getting the current student model fromStudent-Model agent an d activation of appropriate TopicagentThe second rule executes wh en message of the type

I N F O R M ( s t u d e n t - m o d e l , t u t o r ,t o p i c - h i s t o r y , ? v a l u e )

is received Received informatio n is used in appropriate topicselection

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Tut or agcnt has its bclicfs about facts such as SOLV ED.NEXT-TOPIC-NAME, HISTORY V . CONCLUSIONStudent-Model agent uscs tlic class Student dcfincd inLess [ 11 Its most important action is get-level It is used forthe determination of studcnt's level or hnoivledge or thclesson It has two rulcs1. I N F O R M ( ? t o p i c , student-model,

task-history, ? v a l u e ) , TRUE(update-mode.1 ?value)->

2. ASK-VALUE(?anybody, student-model,?name), TRIJE->(INFORM(student-model, ?anybody,?name, VAL (?name) )

Studen t-mod el agent has belicfs about facts such asTASK-HISTORY, TOP-HISTORY. LEVEL, STUDENTS,CURRENT-STUDENT

Interface agent has only on e simple rule that cause tliecxecution of its private action present-task Aftcr task'sprcsentation. it proceeds student's rcsults to appropriateTopic agent

The mental change rules in all agents arc defincd i n theway that implements the following algorithm:step 1. Tutor agent rcccives current studcnt's modcl fromStudent-Model agentstep 2. Tutor agcnt sends activation message to appropriateTopic agent.step 3. Th e selected Top ic agent presents (through Int erfa ceagent) its tasks to the student. using the student's modelobtained from Student-Model agentstep 4. Aftcr presenting all tasks in the topic, tlie Topic agentsends its report to Studen t-Mo del agent to update thestudent's model. It also sends a message to Tutor agent withinform ation about the e nd of its activities.step 5. Go to step I

In this paper, a multi-agcnt generic ITS architccturc usingagent-oriented environmcnt , GLess is prcscntcdAGLess is clioscn a5 a more suitablc option than an! otlicrAOP language. environment or softwarc tool

Appropriateness of AGLess for I T S de\ elopinent stcms fromth e Less' facilities for ITS' program ming [1 AOP languagcssuch as AGENT0 or an environment such as HOMAGE arcnot specialized for tlic dcvelopment of ITS and are lesssuitable than AGLess for this particular domainOn the otlicr hand the most important advantage of tlicIT S as M AS in comparison with the ITS in [ l ] is in thecasicr devclopincnt and modifications of tlic s stein ThcMA S approach es ploit s divide and conquer stratcg! and thusfacilitates a development and modifications of the s stein

VI REFERENCESM IvanoviC, "Tona rds Intelligent Tutoring S stemsUsing Less", (to appear)M Ivanovid.. .4 Contribution to the Developrnent ofProgramming Languages using Object-0riented~Methodologv, h D Thesis, Unir crsity of No! 1 Sad (i nSerbian), 1992M Ivanovid. and Z Budimac. "Intelligent TutoringSystems Using Less", in proceedings of th e 1993qmopsis of 1st IEIC BALX4\' Conference on .CISOh rid, M acedonia, pp 2 13-2 14D A Norman and J C Spohrer. "Lcarncr-centerededucation", Comniunications of the A GW, vol 39 no 4.Apr 1996,pp 24-27A Poggi and G Adorni, "A multi languageenvironment to develop multi agcnt applications", inProceedings of the 1996 Working iVotes of the ThirdInternational JVorkshop on Agent Theories,Architectures and Languages, Budapest. Hungaq , ppY Shoh am, "Agent-oriented programming", ArtificialIntelligence, vol 60 no 1, M ar 1993, pp 51-92P H Wood, "Intelligcnt Tutoring Systems AnAnnotated Bibliography", SIGART Bulletin, no 1. JanWooldridge and N R Jennings, "Agent Theories,Architectures, and Languages A Survey", LNAI,Springer-Verlag, vol 890, Intelligent Agents, 1995, pp

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1997 icips intelligent tutoring system as multi agent system

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