A Multi-Agent Architecture
for an ITS with Multiple Strategies
Thierry Mengelle, Claude Frasson
Université de Montréal, Département d'informatique et de recherche opérationnelle
2920 Chemin de la Tour, Montréal, H3T 1J4, Québec, Canada
E-mail: {mengelle, frasson}@iro.umontreal.ca
Abstract. This research aims to implement an intelligent tutoring system with
multiple co-operative strategies involving several pedagogical agents (for
instance: tutor, co-tutor, companion, ...). Such agents are able to play different
pedagogical roles, consequently, we called them actors. We describe a general
architecture for actors, and illustrate it in the context of a new strategy :
learning by disturbing. A prototype of an environment for building actors has
been implemented using an object oriented language; it allows to develop new
co-operative pedagogical strategies.
1. Introduction
Initially, Intelligent Tutoring Systems (ITS) considered only two partners: the
computer which tried to simulate a human tutor and the real learner. The resulting
systems were generally difficult to control and not always efficient from a
pedagogical point of view. Since the mid-eighties, various alternatives to this one to
one tutoring strategy have been proposed: learning with a co-learner [5], learning
by teaching [10], or, more recently, learning by disturbing [1]. All of these new
approaches suggest that the computer can simulate various pedagogical agents
which can co-operate with the learner in a constructive process. Because the
efficiency of these co-operative strategies depends on the learner’s characteristics,
our objective is to implement an ITS with multiple strategies.
In this paper, we describe a distributed architecture for this kind of ITS. We first
show how ITS architecture can be structured to distribute pedagogical expertise
among various agents. Each agent is considered to be an actor which can assume
various pedagogical roles according to the current strategy. Concerning the different
kinds of intelligent agents, we define the main properties that are required for these
actors. We then describe the architecture of an actor and illustrate it in the context
of the learning by disturbing strategy [1]. To conclude, we briefly describe a
prototype of an authoring environment allowing the implementation of similar cooperative strategies involving several actors.
2. Global Architecture of an ITS with Multiple Strategies
Traditional ITS consider only two partners (tutor and human learner); their
architecture generally include a curriculum, a learner model and a pedagogical
module. However, as mentioned above, we need a more flexible communication
between the learner and the system allowing various interactions between several
partners, for instance a companion which simulates the behavior of another learner
[3], a co-tutor that helps the tutor [1], ...
Session
Manager
Curriculum
Troublemaker
Tutor
Supervisor
Learner
Model
Co-Tutor
Companion
Non-Didactic
Resource
Artificial
Learner
Human Learner
Fig. 1. Architecture of an ITS with multiple strategies.
Figure 1 presents an architecture for an ITS with multiple strategies. The session
manager uses the curriculum and the learner model to choose an activity allowing to
reach a specific objective (a non-didactic resource1) and the pedagogy to manage
this activity. In this paper, we focus on the modelization of pedagogical strategies
that supervise the interaction between the learner and a non-didactic resource (for
instance: a problem, a multiple choice questionnaire, ...). Each pedagogical strategy
involves some specific pedagogical agents. For instance, the one-to-one tutoring
strategy involves only two agents: the tutor and the artificial learner. (which is
intended to synchronize the human learner’s activity with the different agents); the
learning with a companion strategy [3] calls a third agent: the companion. A same
agent can act in several strategies; alone with the learner, the previous companion
will play the role of the co-learner as suggested in [5]. Since the same agent can
1 In the context of the SAFARI project, which supports our work, we consider two
kinds of resources : didactic resources that are self-managed (for instance, a CBT
module), and non-didactic resources. In this paper, we are interested in the
exploitation of this second kind of resources by the pedagogical agents.
play different roles according to the chosen strategy (the tutor acts differently
whether it is alone with the learner or whether it is also with a third agent) leads us
to define pedagogical agents as actors. In the next section will also distinguish
actors from other intelligent agents.
To illustrate the different paradigms, we will take the example of the learning by
disturbing strategy [1]. This strategy aims to strengthen the learner’s selfconfidence; it involves three partners (Figure 1): the tutor, the troublemaker and the
human learner (by way of the artificial learner). The learner works on a specific task
and the troublemaker can give him advice. But, the troublemaker is not a reliable
assistant: it sometimes gives correct solutions and sometimes tries to mislead the
learner in order to check and improve his self-confidence. Though the companion
shares with the troublemaker the characteristic of giving right or wrong solutions,
their motivation and functioning are quite different. The companion tries to model a
human student; so when it does a mistake, it is only because it supposes that,
according to the current knowledge state, a real student would act like this. The
troublemaker does not give wrong solutions accidentally; it ’voluntarily’ decides to
mislead the learner. This difference of motivation has an impact on the knowledge
of these actors: the troublemaker needs pedagogical knowledge to decide when it is
suitable to mislead the learner, while for the companion only the domain knowledge
is absolutely required (one way of implementing the companion is to use a machine
learning approach [3]). Unlike the companion, the knowledge of the troublemaker is
quite superior to that of the real student. A first experiment (learning of highway
code) has shown that the learning by disturbing strategy becomes efficient for
advanced students [2]. Some experiments on the other pedagogical strategies are
under way in order to find rules that will allow the session manager to select the best
strategy according to learner’s characteristics.
3. Intelligent Agents for ITS: Actors
3.1. A new kind of Intelligent Agent: Actors
The architecture of an ITS with multiple strategies described in figure 1 requires
that the different pedagogical agents have some specific properties. In presenting
these properties we follow the evolution of research in the field of intelligent agents.
The first point concerns the autonomy of the pedagogical agent. An agent can
operate without human control in various situations involving different other agents
(for instance, in the one-to-one tutoring strategy, the tutor interacts directly with the
student; in the learning by disturbing strategy, it co-operates with the troublemaker).
To ensure this social ability, each agent needs some perception capabilities.
In our context, pedagogical agent must show ability to react according to the
activity of the other agents. It seems suitable to consider two kinds of reactions:
immediate responses to stimuli without reasoning (like in reactive agents), and
controlled reactions that require planning, prediction and diagnosis capabilities [8].
Because an agent can act in different learning strategies, we need to allow it to
evolve, to be adjusted to new learning situations. To ensure this adaptation, the
designer can simulate the system and modify the agents’ knowledge. This is the
main characteristic of instructable agents [6]. These can receive instructions or new
algorithms. To help the designer, we want to allow agents to dynamically improve
their behavior. In particular, like adaptive agents, they have to adapt their
perception and their decisions (reasoning methods, control strategy, ...) according to
current objectives, available information, and performance criteria.
Beyond this adaptation ability, for an ITS with multiple strategies, it may be
suitable to design cognitive agents which model human behavior in learning
situations. They should be able to learn from experience.
This extended notion of intelligent agent is what we consider an actor. An actor is
an intelligent agent which is reactive, instructable, adaptive and cognitive. Actors
are able to play different roles according to the learning situation in a co-operative
environment.
3.2 Architecture of an Actor
The architecture (Figure 2) of an actor contains four modules (Perception, Action,
Control and Cognition) distributed in three layers (Reactive, Control and Cognitive).
8
COGNITION
7
CONTROL
6
3
2
4
PERCEPTION
ACTION
1
EXTERNAL VIEW
Environment
Other actors + Common Information
INTERNAL VIEW
5
Previous behavior
Reactive Layer Control Layer Cognitive Layer
9
Other
actors
Functioning modes :
1
2
3
4
Reflex mode
Control mode
Reasoning mode
Observation mode
Impacts of cognition layer :
5
6
7
8
Improvement of perception
Creation of actions
Improvement of control
Change of access permissions
9 Improvement of cognition
Fig. 2. Conceptual architecture of an actor [4].
An original point of this architecture is that each actor can observe the previous
behavior of the other actors, a record of their actions (and not only the results of
actions). As we will later show, the view an actor will have on the behavior of the
others will depend on its position inside the system. To allow this capability each
actor has an external view on the other actors and an internal view allowing other
actors to consult its own behavior. So, beyond a common memory, the environment
of an actor consists of all the other actors.
The architecture of an actor relies on four modules.
• The perception module detects changes of the environment, and identifies the
situations in which the actor may intervene. Evolution of the environment results
from the activity of the other actors (the fact that the troublemaker has just given a
misleading information or that an answer of the learner becomes available in the
common memory). This module consists of a set of typical situations [7]. Each
typical situation describes a specific condition of activation according to the
characteristics of the environment.
• The action module allow the actor to act on the environment. It consists of a set of
action tasks. The elementary action tasks that are directly perceptible by the other
actors are called operating tasks (for instance: display an answer, congratulate),
the others are named abstract tasks (for instance: in the case of the tutor, FindNew-Problem is an abstract task that can be hidden; in the case of the
troublemaker, the Mislead abstract task calls the Display-Answer operating
task with a wrong answer as a parameter).
• The control module handles situations which imply a planning aspect in order to
determine the actions to be activated (e.g. the tutor can decide to stop or continue
the tutoring session, the troublemaker may decide to give a right or wrong
solution). The control module contains several control tasks which are activated
by typical situations or by other control tasks. The goal of a control task is to
participate in a decision process which selects and activates a sequence of action
tasks (see next section).
• The cognition module concerns the improvment ability of the actor. This module
will allow the actor to dynamically improve its performance. In a first step, we
want to allow this module to help the designer when adjusting the actor's behavior
to new situations (for instance, by advising him of what seems wrong in the actor's
behavior). Then we will move toward an automatization of the improvment
process. To reach these goals, this module consists of several cognitive tasks. Each
cognitive task attempts to improve a specific aspect of the actor, for instance
improve actor's perception (arrow labelled ) or expand the control (❺)...
Cognitive tasks are not activated from other components (typical situations and
tasks) but are permanently running; they possess two parts: a learning algorithm
and an action part. For instance, a cognitive task dedicated to the improvement of
perception will use a case-based reasoning algorithm in order to infer new typical
situations.
The links between these different modules allow four functioning modes. The
reflex mode (❿) involves perception and action modules; in that case, there is a
direct association between a typical situation and one task of the action module
(abstract or operating task) without reasoning capabilities (spontaneous action). In
the control mode (❡), the control module co-ordinates the perception and action
subsystems; starting from the activation of a typical situation, a control task takes a
decision among possible alternatives and calls the suitable action tasks. The two
other functioning modes will involve the cognition module. The reasoning mode
(➆) will allow the cognitive tasks to override the knowledge of the other tasks in
order to improve the actor current behavior. While the primary purpose of these
three modes is to have the actor interact with its environment, the actor may learn
(since the cognitive tasks are always active). In the observation mode (➘) the actor
will remain passive but will try to learn from the observation of the others. So, this
mode will only involve the perception and cognition modules. This mode will allow
the actors that are not directly involved in the strategy to learn from the others.
Since tasks are classified according to four categories (operating, abstract, control
and cognitive), the actor’s behavior can be observed according to several levels of
abstraction or views. The basic view (operating view) which presents operating tasks
only, roughly, shows what the actor has done while the other views explain why. So,
actors can have a more reliable behavior by knowing the reasons of the other actors’
activities.
4. Example: the Learning by Disturbing Strategy
In order to illustrate the previous architecture, we take the syntactical example of
a simplification of the learning by disturbing strategy, which has been briefly
described in section 2. We give below a short informal description of this simplified
strategy applied to the management of a multiple choice questionnaire:
The tutor asks questions. The troublemaker can give right or wrong solutions, but
it can react only once to each of the tutor’s request (intervention before the
learner, after the learner, or no intervention). Finally, according to the answer of
the learner, the tutor approves or congratulates him, or gives him the right
solution.
TUTOR
T-TS3
1
T-TS1
T-TS2
Scene2: Assess
5
3
4
6
Scene3: NewQuestion
Strategic
view
Find-NewQuestion
Congratulate
Approve
Abstract
view
Learner-answer
Stop?
go on
Operating
view
Common Memory
congratulate
t1.
(Scene1: Start
(Find-New-Question)
(Display-Question))
Tactical
view
t1. ME (TUTOR)
(Scene1: Start
(Find-New-Question)
Approve-Or(Display-Question)
Congratulate
t2. TROUBLEMAKER
2
(Mislead
(Display-An-Answer)
Scene1: Start
)
t3. ARTIFICIAL LEARNER
(Answer)
Previous behavior
ImproveDecisions
INTERNAL VIEW
Behavior of the actors
DisplayQuestion
EXTERNAL VIEW
TROUBLEMAKER
Previous behavior
t2.
(Scene1: React-To-Question
(Choose-An-Attitude)
(Mislead
(Display-An-Answer)))
ARTIFICIAL LEARNER
Previous behavior
t3.
(Scene1: Answer
(Answer)
)
COMMON MEMORY
Learner-answer
ENVIRONMENT
Fig. 3. Implementation and example of functioning of an actor: the tutor
To define a new strategy, our approach promotes reuse. We encourage the
designer to take into consideration the existing actors and to implement this new
strategy by using a simulation process [7]. This process allows him to progressively
adjust existing actors to fit the new strategy and to define the new actors. Creating,
or adjusting, an actor requires the definition or modification of typical situations and
tasks.
In this example, we have defined two new typical situations for the tutor (T-TS1
allows it to start the session and T-TS2 which leads to the evaluation of the student).
Two typical situations allow the troublemaker to intervene before or after the
answer of the learner answers.
To illustrate the functioning of these actors, let us consider the following
situation : we are at time t4, the tutor has asked the first question (t1), the
troublemaker has then tried to mislead the learner (t2) who nevertheless has given
the right answer which is now available in the common memory (t3).
Because the troublemaker has already reacted on the current question (time t2),
none of its typical situations is now triggerable; so, the tutor tries to become active.
First it tries to rebuild, step by step, the whole behavior of the society according to
its view on the two other actors. This explains why the result of this operation
(behavior of the actors indicated on the left side of the figure 3) contains only the
abstract and operating tasks of the troublemaker, and only the operating tasks of the
artificial learner. Then, the tutor checks each of its three typical situations2. Each
typical situation is described by an object with three parts: a focus, a condition, and
a conclusion (see the example of the T-TS2 typical situation on the table below).
The focus restricts the view of the environment in order to only consider
information that is relevant for the evaluation of the condition (here, the behavior of
the three actors for the current question and the common memory). The condition is
a logical proposition (here, the fact that the student does not need to react to the
troublemaker). The conclusion refers to the task to be activated when the condition
is true (here, the Scene2: Assess control task).
Focus
Condition
Conclusion
Scene2: Assess
Access to behaviors: Yes
(Learner-answer is-in Common Memory)
From: TUTOR last Operating Task & ((LastActor • TROUBLEMAKER)
Only: TUTOR, TROUBLEMAKER, | (Remain-Silent is-in LastActor.behavior))
ARTIFICIAL LEARNER
Common memory: Yes
The previous typical situation is now triggerable, so the Scene2: Assess
control task is activated (arrow labelled ❿ on figure 3). The algorithm of this task
first checks the learner’s answer; here, because this answer is correct, another
control task is activated in order to choose between approval or congratulation of
the learner (❡). To make this choice, the expertise of the
ApproveOrCongratulate control task can analyze the affective part of the
student model3 and has to consider that the learner has succeed in spite of the
intervention of the troublemaker. To define this expertise, the designer can
decompose this task into several subtasks. Finally, the elementary control tasks can
be encoded using rulebases, or other formalisms. Figure 3 supposes that the decision
is to congratulate the learner, so Scene2: Assess calls the Congratulate
operating task (➆). Then, Scene2: Assess calls another control task ( ) which
decides to go on; this decision leads to the activation of the Find-New-Question
abstract task ( ). This task returns a new question which is finally given as a
parameter for the Display-Question operating task (★). Activation of these
various tasks are automatically stored in the previous behavior area of the tutor.
Then, the different actors will try again to become active. The process will stop if
there is no actor who has a typical situation which can be triggered (here, typically
after the tutor decides to stop the session).
The previous scenario presents a typical example of the activity of the actor in the
control mode. To see examples of functioning in the reasoning mode (i.e. preemption of a control task by a cognitive task after learning), the reader can refer to
[4].
2 TM-TS3
is a typical situation which has been defined for another strategy and
allows the tutor to change its question consequently to the request of another actor :
the supervisor.
3 In the context of the SAFARI project, the learner model consists of three parts : a
cognitive model, which represents the domain knowledge of the student with an
overlay on the curriculum, an affective model, which stores the habits and
preferences of the student, and an inferential part allowing to dynamically update
the learner model [9].
5. Description of the Prototype
We have used the Smalltalk object-oriented language to implement a prototype of
a generator of co-operative pedagogical strategies. Each strategy involves several
actors that are described according to the previous architecture.
To define an actor, we supply the designer with two editors: one for the typical
situations, and one for the tasks. The functionalities of these editors promote the
reuse of components when defining new actors. For instance, when the designer
defines the tasks of a new actor, the editor displays the list of tasks that are already
implemented for other actors. So, it is easy to make two actors sharing the same task
(for instance, the tutor and the troublemaker share the Give-Solution abstract
task) or to define a new task by adjusting an existing one. In this prototype, the
coding of the tasks and of the condition part of typical situations is done directly in
Smalltalk. Primitives of a high level language allowing to express this expertise
have been defined but are not yet implemented. To define a new strategy, another
editor allows the designer to select the actors that he wants to involve.
In the present state, the main restriction concerns the cognition layer which is not
implemented in the prototype. So, actors can not dynamically improve themselves;
that is why the definition of new strategies requires using a simulation process. This
process allows the designer to progressively refine the actors’ knowledge
(modification of typical situations and tasks). To make this process easier, when
playing a session, a window displays the sequence of tasks activations and the state
of each actor is symbolized with a specific color (green when active, orange when
trying to become active and red when passive). We have also implemented a tool
which allows to replay a given session. The designer see the sequence of
activations; he can define breakpoints, consult the parameters and results of task
and, so, understand the behavior of the system.
We have first used these tools for implementing the simplified version of the
learning by disturbing strategy as described in section 4 (tutor, troublemaker and
artificial learner). We have then defined the learning by supervising strategy
(definition of a new actor, the supervisor, which can ask the tutor to change its
question). We have experimented the simulation process in order to try different
combinations of these four actors. This process has leaded us to modify some typical
situations in order to reach a reliable behavior for the system. The pedagogical
expertise that has been implemented is quite limited. A parallel study on the
learning by disturbing strategy will lead to the implementation of a concrete
expertise in a few weeks.
6. Conclusion
In this paper, we have described a general architecture of actor allowing to
implement ITS with multiple strategies. Parts of this architecture have been yet
implemented in a prototype which allow to edit and to combine pedagogical
strategies. To implement such strategies, the designer uses a simulation process
allowing to progressively adapt the actors’ behaviors. In the next step, the
implementation of the cognitive module will facilitate the design of new strategies
by providing the actors self-improvement capabilities.
Acknowledgments
This work has been supported by the Ministry of Industry, Trade, Science, and
Technology (MICST) under the Synergy program of the Government of Québec.
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