=Paper=
{{Paper
|id=Vol-1148/paper6
|storemode=property
|title=Pedagogical Agents for Social Music Learning in Crowd-Based Socio-Cognitive Systems
|pdfUrl=https://ceur-ws.org/Vol-1148/paper6.pdf
|volume=Vol-1148
}}
==Pedagogical Agents for Social Music Learning in Crowd-Based Socio-Cognitive Systems==
https://ceur-ws.org/Vol-1148/paper6.pdf
Pedagogical agents for social music learning in
Crowd-based Socio-Cognitive Systems
Matthew Yee-King and Mark d’Inverno
Goldsmiths, University of London, UK
dinverno@gold.ac.uk
Abstract. This paper considers some of the issues involved in building a crowd-
based system for learning music socially in communities. The effective imple-
mentation of building such systems provides several fascinating challenges if
they are to be sufficiently flexible and personal for effective social learning to take
place when they are large number of users. Based on our experiences of building
the infrastructure for a crowd-based music learning system in Goldsmiths called
MusicCircle we address several some of the challenges using an agent based ap-
proach, employing formal specifications to articulate the agent design which can
later be used for software development. The challenges addressed are: 1) How
can a learner be provided with a personalised learning experience? 2) How can a
learner make best use of the heterogenous community of humans and agents who
co-habit the virtual learning environment? We present formal specifications for an
open learner model, a learning environment, learning plans and a personal learn-
ing agent. The open learner model represents the learner as having current and
desired skills and knowledge and past and present learning plans. The learning
environment is an online platform affording learning tasks which can be carried
out by individuals or communities of users and agents. Tasks are connected to-
gether into learning plans, with pre and post conditions. We demonstrate how the
personal learning agent can find learning plans and propose social connections for
its user within a system which affords a dynamic set of learning plans and a range
of human/ agent social relationships, such as learner-teacher, learner-learner and
producer-commentator.
1 Introduction
2012 has been called the ‘year of the MOOC’, the massive, open, online course [13].
Indeed one of the authors of this paper was part of a team which delivered a course to an
enrolled student body of around 100,000 in 2013. The obvious problem with MOOCs
is that there is a very high student to tutor ratio. This means it is not feasible to pro-
vide students with direct tutor support when they have problems with their learning and
complex assessments which cannot be automated become impractical. The current so-
lutions seem to be the use of forums and other social media wherein peer support can
take place, and the use of peer assessment techniques such as calibrated peer assess-
ment [7]. Running our MOOC, we noticed that the forum seemed to be an inefficient
tool through which students could find information, where the same questions would
be asked and answered repeatedly, and where the constant churn pushed old answers
away1 . It was not clear who would bother to answer a given question, or who would be
the ideal person to answer it. Regarding the assessment, there was a tendency to assess
others’ work superficially - to simply fulfill the most basic requirements of the peer
assessment task. This was probably an instance of strategic learning, where the learner
does the minimum to meet the apparent requirements. Another problem is a high drop
out rate on courses. For example, we had around 10% of our 100.000 students still ac-
tive at the end of our MOOC; Norvig and Thrun’s famous Stanford AI CS211 course in
2011 went from 160,000 enrolments to 20,000 completions [14]. These figures improve
if we instead consider the number of students actively accessing learning materials at
the start of the course; in our case, 100.000 becomes 36,000. So motivation to complete
the course is another area that needs work.
But how might one motivate a learner, given the particular characteristics of a
MOOC, i.e. the high learner to teacher ratio, the presence of a large, heterogeneous
peer group, the distance learning component and so on? Might motivation be ampli-
fied by leveraging the learner’s peers - the social network? What might a ‘networked
learner’ gain from being part of an active learning community? How can the learner be
made aware of the structure and members of the community, and how that might help
them achieve their learning goals?
In summary, guidance for learners, feedback to learners (on their work) and general
learner motivation are areas for improvement for MOOCs. These are the key points
we aim to address in our wider research work. In this paper we present our work on a
representative component of this: the invention of a type of pedagogical agent called a
personal learning agent which can provide a more intuitive and efficient route through
the learning materials and information, which can hep the learner to network to find
help or to provide help and feedback to others.
1.1 Pedagogical agents
Skiar et al. present a review of research where agents are used [15] and we propose the
reader look at this for more details than we are able to present here. According to Soli-
man and Guetl, Intelligent Pedagogical Agents (IPAs) are agents which help learners by
providing narrations and guidance in order to resolve difficulties and improve motiva-
tion’ [16]. Magus et al. describe a math tutoring game which includes a conversational
agent [11] which has some similar agent characteristics.
Animated pedagogical agents (APAs) operating in realtime virtual learning envi-
ronments allow learners to request information that helps build an understanding of a
student’s thought processes and methods of knowledge acquisition [6]. Lester et al.
trialled a 3D animated character with 100 middle school children. They discuss the per-
sona effect, which encompasses the agent’s encouragement (of learners), utility, credi-
bility, and clarity, and which is much enhanced by the use of an animated character [8].
Johnson et al. provide a list of technical issues for designers of animated pedagogical
agents to consider [5] and the interested reader is recommended to look at this paper for
more details.
1
this is somewhat alleviated by up-and down-voting of questions and answers but this is far
from perfect
Xiao et al. empirically assessed the effect of pedagogical agent competency where
learners were learning how to use a text editor supported by pedagogical agents with
varying competency at the task [18]. Baylor et al. present an initial study where agents
take on different roles (as in Electronic Institutions [4] when supporting learners: Mo-
tivator, Expert, or Mentor. More knowledgeable agents were more credible and seemed
to transfer more knowledge but motivating agents were more engaging [1]. In [17], an
agent based approach is used to simulate interactions between learners within a group.
2 The Music Circle System
The word presented in this paper is part of the efforts to build an online learning system
for massively online learning of music within normative communities of practice. It is
funded under the FP7 Technology-Enhanced Learning Program called Practice and Per-
formance Analysis Inspiring Social Education (PRAISE). The first author is the Techni-
cal Project manager the project and the second author is the principal investigator. The
major research questions of the project are as follows
1. How to evidence increase participation in musical learning activity?
2. Is giving and receiving feedback related to engagement with practice?
3. What is the right level of social coordination?
4. How to evidence musical learning?
5. How can automatic techniques be used to evidence feedback?
6. How to build a personal learning agent for personalised learning experiences?
In order to achieve this one of the key technologies we are developing is that of
the personal learning agents that can enable pathways to and through information for
learners, better feedback to learners (on their work) and general learner motivation, and
as a resource for finding fellow students within which we can learnt together. In this
paper, we will address the following questions which fall within this wider remit:
1. How might one formally specify a human learner that enables an autonomous per-
sonal learning agent to represent them in a crowd-based system for music learning?
2. What kind of operations might be useful, given the wider research goals articulated
above?
The specific online system we are developing within the PRAISE project is called
Music Circle and next we describe Music Circle and why it meets all the criteria for a
Crowd-based Socio-technical System.
3 Music Circle as an example of a Crowd-based Socio-technical
System
Before moving onto specify the personal learning architecture we first wish to state
why this system fits the definition of a crowd-based socio-technical system that is de-
scribed in a sister paper in this workshop by Pablo Noriega and Mark d’Inverno entitled
”Crowd-Based Socio-Cognitive Systems” [12]. In this section we take their description
of a Crowd-Based Socio-cognitive System and item by item explain why our Music
Circle system matches the description given by Noriega and d’Inverno.
– Dimension 1. The Music Circle system contains agents which are both computa-
tional or human and are autonomous in that they can exhibit purposeful behaviour.
– Dimension 2. The population is a mix of human and software agents.
– Dimension 3. The human and computational agents have a model of the system in
which they operate.
– Dimension 4. The agents within the Music Circle system are rational in that they
are capable of choosing different courses of action based on their own models (how-
ever simple or complex these may be).
– Dimension 5. The human and computational agents in Music Circle are social in
that they interact with other agents.
– Dimension 6. The human and computational agents in Music Circle have social
models of the other agents in the system.
– Dimension 7. The human and computation agents are socio-cognitive in the sense
that they base their decisions on some decision-making process which takes into
account the models of the other agents in the Music Circle system.
– Dimension 8. The agents in Music Circle have social capabilities including aware-
ness and models of others, and the ability to understand the norms of the learning
community in which they are situated.
– Dimension 9. The Music Circle system is defined by the system of interacting
agents which means that the state of the system at any stage can never been known
by us as the designers and engineers of the systems (opacity).
– Dimension 10. Agents may enter and leave our system at any time and cannot be
known in advance.
– Dimension 11. Music Circle communities are regulated in order to enable the social
coordination of music learning in communities.
– Dimension 12. The human agents are autonomous and may not necessarily want
to provide helpful feedback to the other music learners in the community and so
we need to regulate communities in order to be sensitive to how such agents could
destroy the trust within a learning community.
– Dimension 13. A Music Circle system is dialogical as all interactions are mediated
by technological artefacts and may therefore be wrapped as communicative acts or
messages typically in relation to audio media.
– Dimension 14. We currently have several hundred of students enrolled with the
hope and expectation of getting this to thousands of music learners within the next
12 months
– Dimension 15. The system is being designed specifically so that norms can be de-
termined by the community. Moreover, we are specifically including models of how
trust and reputation arise and can be managed within such systems.
– Dimension 16. We are developing the Personal learning agent, and the Music Circle
system in general (including norms, coordination, rest and reputation managements
systems and so on) so that it will help the human users take a wide-variety of
feedback on audio and suggested plans for practice from different agents in order
to synthesise a particular plan of practice activity.
That is to say that that our system is central example of a Crowd-based Socio-
Cognitive System and we are using norm-based MAS techniques in the specification,
design and implementation of our system. Next, we move to the specification of the
personal learning agent which works with the human learning agent to facilitate music
learning in our PRAISE system.
4 Requirements of the Personal Learning Agent
There are several issues about large online systems including (i) motivating the learner
(high drop out rate, personalised learning pathways) (ii) connecting the learner (who can
help me with this? and (iii) who is having the same problems, etc.) and giving the learner
an individual pathway (how can I learn to do this?) One approach to combat these
issues is to define a Personal Learning Agent we will use the specification techniques
developed by Luck and d’Inverno over the last 20 years or so (e.g. [2, 9, 10]).
We will begin by framing the agent specification presented later with some require-
ments for the functionality of the agent. There are 4 key requirements: to store learner
state, to report learner state, to find learning plans and to propose social connections.
Each of these requirements has sub-requirements, as listed below:
1. Storing learner state:
(a) Storing the goals of a person
(b) Interpret the goals into desired skills and knowledge
(c) Storing a person’s current skills and knowledge
(d) Storing a person’s current and previous plans
2. Reporting learner state:
(a) Reporting current state of goals and plans
(b) Reporting current state of knowledge and skills
(c) Reporting status of data/ content provided to and from the community i.e.
plans, feedback, feedback agents, trust model
3. Plan finding:
(a) Propose plans whose pre-conditions match current skills and knowledge
(b) Propose plans whose post-conditions (goals) match a personal learning agent’s
goals
(c) Generate evaluation data for plans based on users
(d) Propose plans which are successful, i.e. verified post conditions
4. Agent finding:
(a) Propose social relationships/ connections to people with similar goals/ skills/
knowledge (potential peers, potential as they must actively agree to connect to
make a social relationship)
(b) Propose connections to people with similar (musical/ geographical/ etc.) data
(c) Propose connections to people who have related but superior skills and knowl-
edge (potential tutors), or teaching goals. (I want to increase others’ knowledge
of scales on the guitar). These people might be able to assign plans, for exam-
ple.
5 Formal specification of the Personal Learning Agent
In this section we will use the specification language Z to develop the models of our
agents, following the methodology developed by Luck and d’Inverno [2, 9, 3].
Learner model
We begin our description by introducing our learner model. The purpose of the learner
model is to represent various aspects of a person operating within our learning environ-
ment. There are two types which users of the system might want to learn about or teach
about. The specification remains neutral about how they are encoded but this encoding
might include free text descriptions or formulas in predicate calculous for example.
[Skill, Knowledge]
As an example a user might have the skill of playing the C major scale and the
knowledge which includes being able to state which notes are in the scale of C major.
We then define Proficiency as the combination of skills and knowledge, representing
all that a person would potentially wish to learn in music.
Proficiency ::= skillshhSkillii | knowledgehhKnowledgeii
A particular person can be given a score which is an evaluation of their learning
level regarding a particular skill or knowledge element:
Score == N
Learning environment
We continue the description with some details about the learning environment which
learners, teachers and agents will inhabit. For the purposes of our wider research, it
is specialised for music education, and it is designed around a social, blended learn-
ing pedagogy wherein people upload recordings of themselves playing instruments
and other media items. Discussion and feedback can occur around the uploaded items.
Within the environment, people and agents can carry out tasks, where a task is some-
thing to be undertaken.
[Task]
We have identified 9 distinct tasks which can be carried out within our learning
environment.
TaskType ::= Practice | Listen | Makemusic |
Upload | Share | Annotate |
Question | Answer | Visualise
Earlier, we mentioned that feedback might be provided about a media item. For the
time being we define feedback as a given set. It is possible to define feedback in terms
of constructive and evaluative praise and criticism. However, these are our first attempts
at defining feedback we will remain neutral for the time being.
[Feedback]
We define evaluate to be a function which maps an proficiency to a natural number,
e.g. I have evaluated the way you have played C major as scoring a 5.
evaluateproficiency : Proficiency → N
In the system the community may evaluate many different aspects. One of those is
to evaluate feedback for example.
evaluatefeedback : Feedback → N
Goals, Beliefs and Plans
As with the definition of the SMART Agent Framework [2] we take a goal to be a state
of affairs in the world that is to be achieved (by some agent).
[Goal]
The way that goals (or, equally, learning outcomes) are achieved is through a work-
flow of tasks: a sequence of tasks that have to be completed in order. We do not specify
here who determines whether the tasks have been accomplished successfully or not
because in general this could be a mixture of the system, the user themselves, the com-
munity and/or a teacher. Plans are typically specified in terms of what must be true
before they can be adopted, what is true after they have been successfully completed,
and the kinds of actions (or in our language tasks) that have to be completed in order
Next we define a plan to be a set of preconditions (the skills and knowledge and agent
must have before undertaking the plan) and a set of post conditions (which describe the
new set of skills and knowledge the agent will have after the plan). The predicate part
of the schema state that the intersection of the pre and post conditions are necessarily
empty.
Plan
pre : P Proficiency
post : P Proficiency
workflow : seq Task
pre ∩ post = {}
In specifying this system, it is useful to be able to assert that an element is optional.
The following definitions provide for a new type, optional[T], for any existing type,
T, which consists of the empty set and singleton sets containing elements of T. The
predicates, defined and undefined test whether an element of optional[T] is defined (i.e.
contains an element of type T) or not (i.e. is the empty set), and the function, the,
extracts the element from a defined member of optional[T].
optional[X] == {xs : P X | # xs ≤ 1}
[X]
defined , undefined : P(optional[X])
the : optional[X] →
7 X
∀ xs : optional[X] •
defined xs ⇔ # xs = 1 ∧
undefined xs ⇔ # xs = 0
∀ xs : optional[X] | defined xs •
the xs = (µ x : X | x ∈ xs)
Bool ::= True | False
Using this definition we can now specify the state of a plan. The state of a plan can
be thought of as a running instance of a plan during the lifetime of a users activity. It
means that the plan has been adopted to achieve a goal. In order to specify this we keep
the information contained in the specification of a Plan using schema inclusion. We also
state that if the plan has been started but not finished there will be a current task that
the agent is currently undergoing. By also defining a flag called finished we can specify
a plan state as follows. The predicate part states that the current task must have been
defined in the workflow of the plan.
PlanInstance
Plan
current : optional[Task]
finished : Bool
thecurrent ∈ (ran workflow)
The initial plan state (for any state schema the initial state should be specified in Z)
is where the plan has just been proposed or adopted by a user.
InitialPlanInstance
PlanInstance
undefined current
finished = False
We are now in a position to define four specific sub-types of the plan state as follows.
1. Proposed Plan. A plan which has been selected to achieve a goal but which has
not been started by the agent. As no task has been started the current task is set to
undefined.
ProposedPlan
InitialPlanInstance
2. Active Plan. A plan which is ongoing. It has not been completed and the current
task is set to defined.
ActivePlan
PlanInstance
defined current
finished = False
3. FailedPlan. This is a plan which has a defined task but a flag set to finished. For
example, this represents a situation one of the tasks in the workflow of a plan is too
difficult for the user and the plan is discarded by the user.
FailedPlan
PlanInstance
definedcurrent
finished = True
4. Completed Plan. The flag finished is set to true and the current task becomes unde-
fined.
CompletedPlan
PlanInstance
undefined current
finished = True
There are several operations that we could specify at the level of the plan but the
key one is finish task. Either this leads to the plan being completed or the current place
in the work flow moves to the next task.
In the first case the specification looks like this:
FinishTask1
∆PlanInstance
current = {last(workflow)}
finished = False
undefined current0
finished0 = False
In the second case like this:
FinishTask2
∆PlanInstance
current 6= {last(workflow)}
finished = False
current0 = {workflow((workflow∼ (the current)) + 1)}
finished0 = False
The other is to instantiate a plan which essentially means creating a PlanInstance in
it is initial state from a Plan.
instantiateplan : Plan → InitialPlanInstance
∀ p : Plan; ps : InitialPlanInstance
| ps = instantiateplan(p) •
ps.pre = p.pre ∧ ps.post
= p.post ∧ ps.workflow = p.workflow
The (almost) inverse function of this is a function which takes any PlanInstance and
returns the plan.
recoverplan : PlanInstance → Plan
∀ p : Plan; ps : PlanInstance | p = recoverplan(ps) •
ps.pre = p.pre ∧
ps.post = p.post ∧ ps.workflow = p.workflow
Beliefs
This is a representation of what the agent knows and what it can do. Again we remain
neutral on the representation.
[Belief ]
The Personal Learning Agent In the schema below we have the following definitions.
1. An agent has a set of goals at any stage which we call desires (typically these are
associated with learning outcomes as described earlier in the document.)
2. An agent has a set of beliefs. These refer to the information which is stored about
what the user knows or what the user can do (skills).
3. An agent has some interpret function which takes a goal and returns a set of profi-
ciency (skills and knowledge). Note that the complexity of this function may vary
as in some cases goals may be expressed as a set of proficiency directly and so this
function becomes a simple identity function. However, in other situations this func-
tion has to take a free text description and turn it into a set proficiency. Clearly, in
general no automatic process can do this and such an operation will often be left to
the community. In which case we specify the agents interpret function as a partial
function.
4. An agent has a similar interpret function for beliefs which maps its beliefs to a set
of machine readable (skills and knowledge).
5. intdesires is a set of proficiencies which can then be used by the agent and the
community to plan. Note then, that interpreteddesires is made up of the automatic
function interpret of the agent, possibly the automatic interpretation of other agents,
but also from human users in the music learning community.
6. intbeliefs is the analagous set of proficiencies which the agent has recorded as
known or accompished by the agent.
7. It is not unreasonable to suggest that all tasks are not available to a user at all times
and so the agent can record which tasks are currently available to a user. (If the
internet is down, upload is not an available task. If a newcomer joins a community
then possibly they do not feel like giving any feedback and so the agent can record
that the user is currently not offering this task.).
8. Then we define the set of plans which the agent knows about (possibility learned
from other agents). This is where the agent contains its procedural knowledge about
what plans work in what situations to achieve which desired proficiency.
9. The agent maintains a record of all of the plans that have been completed and all of
those which have failed.
10. There is a record of the intentions. This is a mapping from a set of proficiencies
(this set may only have one proficiency in it of course) to the plan instance which
the agent has adopted to attain those proficiencies.
11. Finally, we record all those interpreted desires for which the agent has no active
plan.
There are also two dummy variables that we can use (which can be calculated from
the variables described so far but which aid us in the readability of the specification)
12. We define a variable record the tasks that the agent is current involved in (currenttasks)
which can be calculated as the union of the tasks from the current plans.
13. We define a variable to records the current plan instances of the agent
Next we consider the constraints on the state of a personal learning agent
1. The interpreted desires are the result of applying the interpret desire function to the
desires.
2. The interpreted beliefs are the result of applying the interpret desire function to the
beliefs.
3. The intersection between interpreted desires and interpreted beliefs is an empty set,
(in other words you can’t desire a proficiency you already have).
4. If there is a plan for a subset of proficiencies then those proficiencies must be con-
tained in the the interpreted desires.
5. If there is a plan for one subset of proficiencies and a plan for another distinct set
pif proficiencies then their intersection is empty.
6. The unplanned desires are those interpreted desires for which there is no intention
7. The current tasks are calculated from taken the current plans and the current task
from each.
8. The current plans are calculated from taking the range of the intentions.
[X, Y]
map : (X → Y) → (seq X) → (seq Y)
mapset : (X → Y) → (P X) → (P Y)
∀ f : X → Y; x : X; xs, ys : seq X •
map f hi = hi ∧
map f hxi = hf xi ∧
map f (xs a ys) = map f xs a map f ys
∀ f : X → Y; xs : P X •
mapset f xs = {x : xs • f x}
PersonalLearningAgent
desires : P Goal
beliefs : P Belief
interpretdes : Goal → 7 P Proficiency
interpretbel : Belief → 7 P Proficiency
intdesires : P Proficiency
intbeliefs : P Proficiency
availabletasks : P TaskType
plandatabase : P Plan
completedplans, failedplans : P Plan
intentions : (P Proficiency) →7 PlanInstance
unplannedintdesires : P Proficiency
currenttasks : P Task
currentplaninstances : P PlanInstance
S
intdesires = S (mapset interpretdes desires)
intbeliefs = (mapset interpretbel beliefs)
intdesires
S ∩ intbeliefs = ∅
(dom intentions) ⊆ intdesires
∀ ps1, ps2 : P Proficiency |
(ps1 6= ps2) ∧ ({ps1, ps2} ⊆
(dom intentions)) •
Sps1 ∩ ps2 = {}
unplannedintdesires = (dom intentions) \ intdesires
currenttasks = {t : Task; ps : PlanInstance |
ps ∈ (ran intentions) • the ps.current}
currentplaninstances = ran intentions
Plan Finding Plan finding is the process of taking a set of candidate plans and selecting
those whose preconditions are met and where at least some subset of the postconditions
are desired. For this operation we assume the input of a set of candidate plans. Again we
do not specify whether these comes from the agent (i.e. the agent’s database of plans),
other agents in the community, or from the user or from other users and in general with
be a synthesis of the users and the agents of users working together.
For now we will suppose that suitable plans have all preconditions satisfied and it
is the case that both: (a) none of the postconditions are things which the user is already
proficient in (b) all of the postconditions are current interpreted desires of the user. In the
schema below SuitablePlans is generated which satisfy this constraint and from these
one plan adoptedplan is selected. The state of the agent is then updated to include that
the current plans now includes a mapping from the pre-conditions of the plan (which
are necessarily interpreteddesires for which no plan exists.
Plan Completion The very simplest way this could happen is as follows:
1. Because of a successfully completed task a plan instance becomes an element of
CompletedPlan
2. The post conditions are added to the interpreted beliefs (these may in turn be reverse
interpreted into beliefs which can then be seen by the community)
3. Any post conditions that were formerly desires are now removed from interpreted
desires (these may in turn be reverse inetreprered into beliefs which can then be
seen by the community)
4. The completed plans function is updated with the plan that has just successfully
completed.
CompletePlan
completedplan? : CompletedPlan
∆PersonalLearningAgent
completedplan? ∈ (ran intentions)
intentions0 = intentions −B {completedplan?}
intdesires0 = intdesires \ completedplan?.post
intbeliefs0 = intbeliefs ∪ completedplan?.post
completedplans0 = completedplans ∪
{recoverplan completedplan?}
However, this process will not be automatic in general within the system. In general,
the user (or other users in the community) will be asked to evaluate the plan. There
may be several ways in which this can happen. For example, a simple score could
be given but in general each user who is evaluaring the plan considers each of the
post conditions (or another member of the community does) to work out whether they
are now proficiencies (intepreted beliefs), whether they have not been met and so are
still interpreted desires, or whether they have not been met but are not desires. Indeed
the evaluating user could rank each of the postconditions with a score and the agent
may also wish to keep a snapshopt of the agent’s state for future comparison by the
community.
Fig. 1. The music discussion user interface
Finding and adopting a plan Plan finding is the process of taking a set of candidate
plans and selecting those whose preconditions are met and where at least some subset
of the postconditions are desired. For this operation we assume the input of a set of
candidate plans. Again we do not specify whether these comes from the agent (i.e. the
agent’s database of plans), other agents in the community, or from the user or from other
users and in general with be a synthesis of the users and the agents of users working
together. For now we will suppose that suitable plans have all preconditions satisfied
and it is the case that both: (a) none of the postconditions are things which the user is
already proficient in (b) all of the postconditions are current interpreted desires of the
user. In the schema below SuitablePlans is generated which satisfy this constraint and
from these one plan adoptedplan is selected. The state of the agent is then updated to
include that the current plans now includes a mapping from the pre-conditions of the
plan (which are necessarily interpreteddesires for which no plan exists.
FindandAdoptPlan
PossiblePlans?, SuitablePlans! : P Plan
adoptedplan : Plan
∆PersonalLearningAgent
SuitablePlans! = {ps : PossiblePlans? | (ps.pre ⊆ intbeliefs) ∧
(ps.post ∩ unplannedintdesires) = {} • ps}
adoptedplan ∈ SuitablePlans!
intentions0 = intentions ∪ {(adoptedplan.post, instantiateplan(adoptedplan))}
It would be a simple matter to add more detail to this schema including choosing the
plan with the highest rating for example, or a plan which has completed successfully
in the community the most number of times, or making sure the plan has not failed
in the users history, or that the plan has not failed in the community with users which
have similar profiles as defined by the personal learning agent. In general, the plan
finding system requirements, and this specification alongside it, will develop as we gain
experience of how the system is used.
Community of Music Learning
Agent finding Now we move to defining a community of learners each of which has
one and only one personal learning agent. First we define the set of all users.
[User]
Community
community : P User
7 PersonalLearningAgent
agents : User
community = dom agents
To this we can define all kinds of social relationships. For example, peer and teacher
and others as they become useful. It is up to the designer of the system to state what the
constraints are on any such relationships. To provide examples (not necessarily ones we
would subscribe to) of how this is none we state that if user1 is a peer of user2 then
user2 is a peer of user1 and, in addition, if user2 is a teacher of user1 then user1 cannot
be a teacher of user2. Another example would be the idea of a fan who would always
adopt the advice of another.
SocialRelationships
peer, teacher : User ↔ User
fans : User ↔ User
∀ u1, u2 : User • (u1, u2) ∈
peer ⇒ (u2, u1) ∈ peer
∀ u1, u2 : User • (u1, u2) ∈
teacher ⇒ (u2, u1) 6∈ teacher
Using these schemas it then becomes possible to ask agents to start to look for users
who have similar profiles as stated in the requirements detailed earlier in this document.
In order to refine the search to include (for example) looking for agents who have a
motivation to teach, we will need to develop the specification to define ways in which
agents can broadcast that they are able to teach certain plans. This will come in later
versions of this specification.
6 Concluding remarks
We are developing a system for social music learning called MusicCircle that we hope
will be populated by large numbers of learners. As part of the design of our system we
will be incorporating personal learning agents to provide a more personalised, social
and effective learning experience. In this paper have use a standard agent-based formal
specification methodology for modelling social agent systems to specify the design of
these agents. The website can be found at musiccircleproject.com and a screen
shot of the system is given above.
At the heart of this is the design of a social system of personal learning agents that
can enable users to have a stronger sense of their place in the social community of mu-
sic learners. We have used a long-standing agent-based specification methodology for
building models of these agent systems which we are using in the principled develop-
ment of our system. In addition to using formal agent-based methodology for designing
agent systems and on the other hand we are testing versions of our systems with users
across a range of music learning cites in the UK (from school, to pre-consevertoire to
the HE sector) so that our work clearly spans agent-based theory and the practice of
building systems for large numbers of users. Furthermore, we have demonstrated that
Music Circle is an example crowd-based socio-technical system as described by Nor-
iega and d’Inverno in the paper.
In relating the theory and practice of sociological agent systems within the design
of socio-technical system more generally also enables us in future work to consider a
range of questions about how the scientific social multi-agent approach that our com-
munity has developed for 20 years or more can be applied to the analysis and design
of crowd-based socio-cognitive systems. We need to understand to what extent a MAS
approach to analysing and designing systems such as MusicCircle helps? Could we, for
example, start to map out the space of such systems relating technology to sociality in
a useful way using the multi-agent tradition? Then could we start to provide platforms
and design methodologies for building such systems in the future using a regulated
MAS approach?
One hope is that we will see a greater influence from the MAS community, applying
the work developed over recent years to become mainstream in the analysis, design and
specification of crowd-based socio-technical systems in the future.
Acknowledgements
This research was supported by the FP7 project in the Technology-Enhanced Learning
Program called Practice and Performance Analysis Inspiring Social Education (PRAISE).
The authors would like to thank Harry Brenton, Andreu Grimalt-Reynes, Maria Kriven-
ski, Jonathan James and Francois Pachet who are colleagues on the PRAISE project at
Goldsmiths.
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