=Paper=
{{Paper
|id=Vol-70/paper-2
|storemode=property
|title=Towards a Plan-based Learning Environment
|pdfUrl=https://ceur-ws.org/Vol-70/paper2.pdf
|volume=Vol-70
|dblpUrl=https://dblp.org/rec/conf/pgldb/CiarliniF03
}}
==Towards a Plan-based Learning Environment==
https://ceur-ws.org/Vol-70/paper2.pdf
Towards a Plan-based Learning Environment
Angelo E. M. Ciarlini Antonio L. Furtado
Departamento de Informática Aplicada Departamento de Informática
UNIRIO Pontifícia Universidade Católica do RJ
angelo.ciarlini@terra.com.br furtado@inf.puc-rio.br
Abstract
The use of the Plan Recognition/Plan Generation paradigm in the context of corporate
training is discussed. The learning environment is grounded on three-level conceptual
schemas of information systems, and offers a tool for simulating the behavior of agents
with an adequate degree of realism. After arguing for the relevance of Plan-Based Learning,
we stress the need of taking into account both cognitive and affective characteristics of the
various agents operating in the specified multi-goal/multi-agent information system, as
conveyed by their individual profiles and current internal states.
1. Introduction
The fundamental basis of our current research project is the development of more
realistic methods for the conceptual specification of information systems, taking a
broader perspective than their simple description as sets of software tools performing
specific tasks. Information systems are recognized as complex structures composed of
agents that can be either software agents, humans or organizations, which interact with
each other. They may cover domains of practical applications, such as sales, banking,
etc. Incorporating a temporal dimension, we can go beyond static descriptions to follow
the narratives that arise in the mini-world delimited by the domain, consisting of events
caused by the agents' interactions. Thus, in a banking application domain, one can
usefully trace stories of clients handling their saving accounts and making investments,
and their contacts with the management of the bank. But fiction also supplies domains,
such as fairy-tales or detective stories, wherein descriptions and narratives are also
amenable to computerized specification and simulation techniques ([1], [2]). The ability
to handle domains belonging to literary genres seems particularly relevant to the
growing area of entertainment applications ([3], [4]).
In our previous work [5], we showed how to elaborate formal specifications at three
levels for information systems having a database component:
1. At the static level, facts are classified according to the Entity-Relationship
model. Thus, a fact may refer either to the existence of an entity instance, or to
the values of its attributes, or to its relationships with other entity instances.
Entity classes may form an is-a hierarchy. All kinds of facts are denoted by
predicates. The set of all facts holding at a given instant of time constitutes a
database state.
2. The dynamic level covers the events happening in the mini-world represented in
the database. Thus, a real world event is perceived as a transition between
database states. Our dynamic level schemas specify a fixed repertoire of domain-
specific operations, as the only way to bring about events and thus cause state
transitions. Accordingly, we equate the notion of event with the execution of an
operation. Operations are formally specified by the facts that should or should
not hold as pre-conditions and by the facts added or deleted as the effect of
execution.
PGLDB’2003, pp. 9-19, 2003.
PUC-Rio, Rio de Janeiro-RJ, Brazil
Towards a Plan-based Learning Environment
3. The behavioural level models how agents are expected to act in the context of
the system. To each individual agent (or agent class) A, we assign a set of goal-
inference rules. A goal-inference rule A:S → G has, as antecedent, a situation S
and, as consequent, a goal G, both of which are first-order logic expressions
having database facts as terms. The meaning of the rule is that, if S is true at a
database state, agent A will be motivated to act in order to bring about a state in
which G holds. In addition, we indicate the typical plans (partially ordered
sequences of operations) usually employed by the agents to achieve their goals.
The first two levels encompass an object-oriented view of information systems, whereas
the third level extends this view to incorporate an agent orientation, with a stress on
goal-driven requirement analysis ([6], [7]).
To experiment with our methods, we have been developing an executable prototype
tool, using Logic Programming enhanced with Constraint Programming ([8], [9]). The
tool, named IPG (Interactive Plot Generator), is based on a plan recognition/plan
generation paradigm [10]. Plan recognition algorithms detect what plan an agent is
trying to perform, by matching a few observations of the agent's behavior against a
repertoire of typical plans contained in a previously constructed library of typical plans
(TP-Library). Plan generation algorithms create plans as partially ordered sets of
operations whose execution, starting from a given state of the mini-world, would lead to
the satisfaction of an indicated goal. The plan recognition part of IPG implements
Kautz's algorithm [11], whereas plan generation uses the techniques developed in the
Abtweak project [12]. As will be seen later, plan modification is yet another useful
function provided by IPG, whereby the plan generation algorithm starts with a still
incomplete plan or with a plan that cannot reach the given goal due to unremovable
obstacles.
Both the plan recognition and the plan generation algorithms in IPG proved to be
powerful enough to handle not only plots of simple database narratives but also of fairly
intricate folktales [13], allowing to perform predictions (via plan recognition) and
simulations (through plan generation) over the real or fictional mini-worlds specified. In
more detail, when using the three-level schemas for simulation, our prototype runs a
multistage process in which the application of goal-inference rules alternates with
planning phases. The execution of a plan brings about new situations, which may lead
to new goals, and so forth; these iterations continue until either there is no new goal to
be inferred or the user decides to stop the process.
Predictions and simulations serve a variety of purposes (e.g. to help decision-
making). In special, as will be argued in this paper, both may prove most helpful for
corporate training, as services to be offered in the context of learning environments,
installed over information systems having a database or data warehouse component
[14]. Section 2 introduces the Plan-Based Learning concept, and sections 3, 4 and 5
outline further methodological enhancements to be incorporated into our project.
Section 6 contains concluding remarks.
2. The Plan-Based Learning concept
Plan-Based Learning (hereafter PBL), as an application of the plan recognition/plan
generation paradigm to learning environments, can basically take one of the following
modalities:
Proceedings of the PGL DB Research Conference 10
Towards a Plan-based Learning Environment
a. Learning by plan recognition
b. Learning by plan generation
c. Learning by plan modification
Consider the case of a sales information system, wherein the classes of agents are
salesmen and clients, each class having available a repertoire of operations. Assume
further that different sub-classes of clients have been identified in the past, according to
their personality (borrowing from [15]): dominant, political, steady, and wary. Although
the same operations are allowed to all clients, their pre-conditions and effects may be
somewhat different for each sub-class. For instance, a wary client will not buy a product
unless, among other requirements, he is sure that an unconditional "satisfaction
guaranteed or your money back" policy is adopted by the Company. Accordingly, such
clients may have been observed to resort to a typical plan including the request of a
printed form stating this policy.
Besides modelling the possible operations and typical plans of each class of agent, we
usually need to model goal-inference rules, stating the behaviour of the agents of our
system, in particular of those who are not under our control. In this way, we are able to
test and choose the best policies to achieve our goals. In our example, a rule could say
that advertising very often by e-mail would make clients willing not to have any contact
with the Company at all. In a simulation that takes such a rule into account, the
possibility of being included in an anti-spam list would probably be considered by the
Company. In order to prevent this from happening, different advertisement strategies
could be tried.
Now, suppose that a tool such as IPG is used in salesmen training. A particular
salesman S can be given different kinds of cues to initiate an interaction in this
environment. If modality (a) of PBL is used, he may be given the observation that the
client solicited the above form. Submitting the observation to IPG, S would learn that
the observation is part of the typical plan mentioned before; as a consequence, S would
also learn that he has to do with a wary client, since the typical plan is registered in the
TP-Library in connection with this sub-class. Learning how to recognize what plan an
agent is currently engaged in, by noting which few actions he has executed thus far, is
tantamount to learning to anticipate his next actions and the goals motivating his
behaviour. Identifying the sub-class to which he belongs leads to the ability to also
anticipate other plans that he may try in the future. More importantly, the trainee can
perform simulations to evaluate the efficacy of his own intended actions — a wary
client, for instance, cannot be expected to react with blind enthusiasm to the salesman's
offer of "special" prices. In such simulations, it might be possible to foresee the result
of the intended actions both when the client continues with the execution of the detected
plan and when he tries different alternatives to achieve the same goals.
In modality (b) of PBL, the cue may be a current state, wherein a wary client already
has received the form but is still not convinced that the policy is actually followed, and
the final goal of the salesman to successfully complete the deal. Submitting both the
state and the goal to IPG, S would learn from the tool a generated plan, which includes
the delivery to the client of a list of previous clients in a position to confirm the
consistent application of the policy. Learning to generate a plan exposes the trainee to
the mechanics of planning: how to fulfill the pre-conditions of an action by previously
executing other actions with the required effect, how to determine what must be done
serially or may be done in different orders or in parallel, and how to find alternative sets
of actions reaching the same objectives but possibly with significantly different side-
effects. Moreover, if there is a behavioural model of the clients (i.e. goal-inference
11 Proceedings of the PGL DB Research Conference
Towards a Plan-based Learning Environment
rules), it will be possible to simulate their reaction to the execution of plans, so that the
trainee will be able to learn which plans render the best results in each situation.
In modality (c) of PBL, a possible cue would consist of a current state and a typical
plan picked from the TP-Library. This would be the case, for example, when, after a
preliminary modality (a) run, we detect that an agent is executing a plan to achieve a
certain goal and we decide to help him, but the typical plan is not immediately
executable. Suppose the agent’s typical plan is the one we saw, adequate to wary clients
and where the form is demanded, but assume that, at the current situation, there is an
obstacle to the cooperation defined in the typical plan: the form is unavailable for the
moment. Having these cues as input, IPG would supply to S a modified plan, in which
the list of previous clients is delivered instead of the form. Learning to modify an
existing typical plan is comparable to learning how to adapt for reuse stored software
packages, a familiar concept from Software Engineering.
More generally, PBL is expected to prepare the trainee to work with, and choose
between, two types of strategies to pursue a goal: (1) take a ready-made plan "from the
shelf" (the typical plans) and use it as it is (or with only the changes that are absolutely
necessary), noting that such plans correspond to the traditional — in some cases
inadequate —practices of the corporation, or (2) create a solution from scratch, ideally
trying to maximize the gains according to some criterion.
Not only executors, such as salesmen, but even those who design or are responsible
for the operation or maintenance of an information system can benefit from PBL
training. By experimenting with both kinds of plans, they can exercise a form of meta-
level (also known as double-loop [16]) learning, wherein the trainee learns more about
the meaning and implications of business rules, which must have been correctly
captured in the conceptual specification. In particular, the trainee may fall upon
unexpected situations reachable by plans, and sometimes detect loopholes, i.e. ways to
violate constraints that the specification was supposed to enforce. Double-loop learning
permits, besides checking whether the mini-world is functioning according to the
specified model, to also question the quality of the model itself. Another possibility is
considering the construction of the library of typical plans itself as a learning task in
which the designers try to “learn” the typical behaviour of the agents. In a previous
work [17], we presented a prototype we have implemented to semi-automatically help
the construction of such a library based on an interpretation of events executed in the
past and recorded in the system’s log.
We are now proceeding to expand IPG, in an attempt to cope with more realistic
scenarios. The current investigation mainly concentrates on how to model goal-
inference rules and on how to use them during simulations. In its present
implementation the tool relies on some rather common assumptions:
a. Omniscience – Agents (humans or organizations) cannot be expected to know all
facts currently holding. An agent may well ignore a fact, and may even have an
erroneous notion about it. So, an agent A may fail to behave as predicted by a
goal-inference rule A:S → G, of which he is supposed to be aware, simply
because he does not know that the motivating situation S holds.
b. Competence for logical reasoning – Similarly, human beings are not equally
proficient to apply precise methods logical inference, probabilities, etc. to
reach conclusions. For example, practical experiments [18] have demonstrated
that people with training in statistics have been found to rate the occurrence of p
∧ q as more probable than facts p or q alone! Likewise, even if A knows that S
holds, he may fail to apply an apparently well-understood goal-inference rule
leading to G as an implied consequence. This fact is also observed in software
Proceedings of the PGL DB Research Conference 12
Towards a Plan-based Learning Environment
agents, which may act according to very different patterns, varying from a
purely reactive behaviour to complex reasoning mechanisms.
c. Rationality – Far more disturbing is to note how a person well provided with
factual knowledge and reasoning skills, after duly concluding that a goal G
corresponds to the best course of action available at the moment, can decide
against it with no declared justification. An eloquent example is the episode
having as protagonist the English philosopher Herbert Spencer, who chose not to
move to New Zealand despite his conclusion that, according to his own
evaluation — based on an astonishing early version of modern utility functions
— his departure would be more advantageous than staying in England [19]. In
the same way, software agents designed to act as similarly as possible to human
beings can occasionally exhibit some kind of “irrational behaviour”.
In the next sections some initial considerations on the enhancements envisaged for IPG
in order to remove the need for these assumptions will be briefly outlined. They would
certainly be relevant in several applications, and appear to be indispensable to effective
PBL environments.
3. Internal states and profiles - cognitive elements
To avoid assuming omniscience, an internal state can be attributed to each agent A,
registering the facts that A believes, correctly or not, as holding in the current global
database state. We now establish that, for a goal-inference rule A:S → G to affect the
behaviour of A, it is not enough that the facts denoting situation S be objectively true; in
addition, such facts must be believed by A, and, accordingly, be part of A's internal
state. One may even admit that, if the facts are believed by A, the rule is applicable even
if they are not actually true.
Moreover, it is not enough that, believing S, A concludes that G is desirable. Except
in cases where A's behaviour is purely reactive, he will still be free to decide, by some
presumably objective criterion, whether or not he will actually commit to G as a goal
and, consequently, adopt or develop a suitable plan Π to achieve it which
characterizes deliberative behaviour [20]. Individual beliefs, rather than global
knowledge, and the concept of intentions, as the result of purposefully adding
commitment to mere desires, are among the basic tenets of BDI-models ([21], [22],
[23]).
Separate research has been applied to investigate what leads to this transition from
desires to intentions. An approach that seems quite rational but is unfortunately hard to
apply in domains of some complexity is based on the notion of utility [24]. Firstly, it
requires that the desirability of a goal G be expressed by a numerical utility value. This
would seem to be easy whenever a number is naturally attached to G; for instance, G
may consist of the possession of an amount m of money. But the same amount m will
have a different importance to people of different income levels, and so the utility value
u of G, although depending on m, would not be necessarily identical to it. And, in
general, the utility value may also be influenced by the internal state of the agent. If no
quantitative attribute is attached to G, the determination of utility values becomes even
harder. One should, at the very least, choose the values so as to ensure the ability to
order situations according to their desirability; i.e. if G1 is intuitively more desirable
than G2 then their respective utility values u1 and u2 should be determined so as to
have u1 > u2.
13 Proceedings of the PGL DB Research Conference
Towards a Plan-based Learning Environment
An additional concern is that reaching a goal G by executing a plan Π is often an
uncertain process. Instead of the purported G, the plan may achieve significantly
different results G1, G2, ... , Gn, with probabilities p1, p2, ... , pn, respectively. Of course,
replacing G by the n possible results of Π requires that different utility values u1, u2, ... ,
un be assigned to each Gi. The overall utility value of executing Π then becomes a
statistical average, to be computed by a utility function:
U(Π) = ∑i pi × ui, for i = 1, 2, ... , n
Whenever there is more than a plan to reach a goal, the utility functions of all such
plans have to be evaluated, and a "rational" agent should choose the plan of maximum
utility. An analogous decision problem arises when an agent has to choose between two
or more mutually exclusive goals (more about this in section 5), as in Spencer's
dilemma. The difficulty of avoiding arbitrariness when determining utility values and
the computational effort involved in the maximization calculations are drawbacks that
must be recognized, since they can render unpractical the exhaustive comparison of all
alternatives.
The adoption of internal states allows to consider what facts an agent A believes to be
true at a given state, dropping thereby assumption (a) (omniscience). On the other hand,
individual differences in logical reasoning competence, which underly assumption (b)
(competence for logical reasoning), as well as other relatively stable (i.e. state-
independent) personal characteristics of agents, should be captured in profiles, to be
specified for each agent class with as small granularity as convenient, and even, if
necessary, specialized for individual agents. For an initial design of profiles and their
corrections and adaptations, as experience may demand, the methods and techniques in
the stereotype approach to user-modelling ([25], [26]) look promising.
Whereas both internal states and profiles might be restricted to diverse cognitive
elements basically related to awareness of facts and expertise to apply rules in
order to abolish assumptions (a) and (b), another type of elements must be brought in, if
we propose to do without assumption (c) (rationality) as well. Conceivably, Herbert
Spencer decided to stay in England because he "felt" better staying there than moving to
a remote country. Now, feeling is not a rare determinant in human decision-making, and
a recent trend in Artificial Intelligence research — on which our next section is based
— is dedicated to what has been called affective computing [27].
4. Internal states and profiles - affective elements
One must recognize that behaviour is largely influenced, sometimes determined, by
drives and emotions, among other affective elements (e.g. moods, not treated here)
([28], [29], [30]). There is already some recognition that believable agents, i.e. agents
that provide the illusion of life, show emotions even when trying to behave rationally,
and, to some extent, act under their influence; this remark is still more crucial in
attempts to combine agent technologies with those of the entertainment industry,
including cinema, interactive television, computer games, and virtual reality [3]. Drives
are basic physical needs, such as hunger and thirst, to which it is legitimate to add social
needs, such as the urge to acquire money or prestige. Emotions have been classified
according to distinct criteria, depending on the purpose of the classification; one popular
classification considers six primitive emotions, with the convenient feature that they can
be easily mapped into sharply distinguishable facial traits [31]: anger, disgust, fear, joy,
sadness, surprise. An emotion can, in general, be either taken by itself, e.g. "a person is
Proceedings of the PGL DB Research Conference 14
Towards a Plan-based Learning Environment
angry", or with respect to an object, another person or an event [32], e.g. "a person is
angry at the prospect of being cheated by a salesman".
Both drives and emotions are amenable to a numerical scale representation, showing
their intensity within a lower and a higher limit. A drive or emotion is said to be present
in an agent if its intensity measure exceeds an appointed threshold. With the passage of
time, the intensity of an unsatisfied drive increases. The satisfaction of a drive is
accompanied by an increase in "positive" emotions (e.g. joy), whereas leaving it
unsatisfied or, on the contrary, reaching an overwhelming regime by going beyond
saturation, can stimulate "negative" emotions (anger, sadness). The intensity of an
emotion decays after some time. Certain emotions are able to excite or inhibit other
emotions, e.g. fear may excite anger and inhibit joy. As would be expected, the
assignment of numerical measures is a no less delicate process here, needing to be
validated for its adequacy in actual practice. Curious experiments [30] to emulate a
toddler with purely reactive behaviour have been conducted, dealing with mutually
stimulating or inhibiting interactions among various drives and emotions.
The intensities of drives and emotions of an agent A must be recorded as part of the
internal state of A. Likewise, personality traits of A (e.g. whether or not A is an
introvert, or is aggressive, etc.) should be part of A's profile. Thus, both internal states
and profiles should contain both a cognitive component and an affective component
which, together, contribute to determine A's behaviour. Indeed, it seems clear that most
people decide under the combined influence of rational and affective factors. Both kinds
of factors should therefore be taken into consideration as constituents of pre-conditions
and effects of operations and, hence, of situations and goals in goal-inference rules.
Additionally, they should both be taken into account in the determination of utility
values; in special, the satisfaction of fundamental physical and social drives tends to be
at the root level in goal hierarchies.
Examples of the relevance of affective factors are easy to find. A client will buy an
item from a salesman only if he is happy with the salesman's service. Delays in delivery
will have the effect of increasing the client's anger against the salesman. The action of
watching a movie aims mostly at procuring pleasurable emotions, not rationally
determined profit. Some goals, like the purchase of food, owe their desirability to their
contribution to satisfy a drive, such as hunger. Computer interfaces offering unrequested
advice may cause anger in users with a high degree of expertise on the subject, as their
profile should indicate and preventively lead the system to turn down the advice-
giving facility. For Herbert Spencer, the utility value of going to New Zealand may
have been reduced by his fear of facing a new physical and human environment.
Making or keeping a client happy has been defined as a "softgoal" in the requirements
analysis literature ([6], [7]), due to the imprecision of the notion of "happy"; one may
expect that, by numerically measuring emotions and the increasing or decreasing effect
that operations can have on their level, it should be possible to contribute towards the
treatment of softgoals as ordinary goals.
5. Cognitive and affective factors in multi-goal/multi-agent environments
At a given state, some of the goals resulting from the application of goal-inference rules
may form one or more sets of mutually interfering goals. Robert Willensky [33] notes
that interferences can be separately characterized, on the one hand, as negative or
positive, and, on the other hand, as internal (involving goals of the same agent) or
external (goals of different agents). On the basis of these two dimensions, he proposes
the following classification of goal interferences:
15 Proceedings of the PGL DB Research Conference
Towards a Plan-based Learning Environment
a. goal conflict: negative, internal;
b. goal competition: negative, external;
c. goal overlap: positive, internal;
d. goal concord: positive, external.
A major cognitive requirement in multi-agent environments has to do with the need to
establish communication in order to adapt interfering goals and the corresponding plans.
We saw that each agent perceives the external world in terms of beliefs, which are part
of his internal state. Communication [34] between agents then means the ability of one
agent to act on the other agent's internal state, changing his beliefs, typically by an
exchange of information. Speech acts [35] thus provide an additional repertoire of
operations such as inform and request noting that the latter is essential whenever
an agent A1 wants another agent A2 to perform an operation which A2, but not A1, is
authorized to execute. Operations corresponding to speech acts, besides being included
in plans, intermingled with the domain-specific operations, can serve as a basis for
agent communication languages [36].
But speech actions go beyond their cognitive effect. They are associated with
emotions, which, in turn, may be manifested by facial expressions [37].
More generally, affective considerations certainly influence the choice of strategies
for handling the various cases of interfering goals. Temperament traits, which we
propose to model as part of the agents' profiles, may establish a preference for either
goal abandonment or for aggressive outdo, or even undo (i.e. obstruct the competitor's
plan), competitive acting. A prototype reported in [15] (and cited here at the beginning
of section 2) has been developed to help training salesmen by simulating their
interaction with clients with four different personalities; the same actions of a salesman
were expected to elicit different reactions in each case.
A study of emotions that stresses interpersonal relationships [38], and was used in the
above-mentioned training prototype, attempts to formally characterize what is meant by
a number of words and phrases expressing emotions closely related to behaviour,
grouped as follows:
Well-being: joy, distress;
Fortunes-of-others: happy-for, gloating, resentment, sorry-for;
Prospect-based: hope, satisfaction, relief, fear, fears-confirmed, disappointment;
Attribution: pride, admiration, shame, reproach;
Attraction: love, hate;
Well-being & attribution: anger, remorse.
Gloating, for example, as analysed in the corresponding expression in the authors'
situation calculus formalism, means to be pleased about an event undesirable for
another agent. Reproach is disapproving of the action of another agent, assuming that
the action is considered blameworthy. Love and hate (or like and dislike) are not
decomposed into simpler terms, being considered primitive and hence unexplainable.
Such kinds of emotions may well play a role in the choice of strategies. In a pair of
salesmen competing to win a client, one of them may find that an undo strategy is
justified if he feels reproach for past actions of the other salesman. On the contrary, he
may spontaneously abandon his attempt, especially if he has a benevolent personality,
in view of his admiration for the competitor. Individual agents may reconsider their
goals to better suit the needs of a group to which they belong; in [38], for instance, an
agent can demonstrate pride or shame for, respectively, a praiseworthy or blameworthy
act attributed to a "cognitive unit" of which he is a member.
Proceedings of the PGL DB Research Conference 16
Towards a Plan-based Learning Environment
Going further, if the agents involved are not individual persons or groups, but rather
industrial firms or some other kind of organization, it becomes far more difficult to
characterize their activity in cognitive and affective terms. For human agents, computer
scientists seek the orientation of Cognitive Psychology ([39], [40]). For organizations,
fortunately, some clues are provided by Management Science, in particular from studies
on Theories of Organization. Showing that the various proposed theories can be
classified according to the metaphor through which they visualize what the concept of
"organization" signifies, Gareth Morgan [16] argues convincingly that all classes of
theories have important contributions to offer; for instance, whereas mechanistic
theories stress a rational concern with efficiency and profit, other theories detect
practices inherent in the company's traditional "culture", or the pressure of hidden
agendas emerging from political struggles for power, etc.
6. Concluding remarks
The concept of Plan-Based Learning (PBL) opens a variety of possibilities for corporate
training. By implementing both plan recognition and plan generation, our IPG tool
seems to represent the right kind of engine needed to operate in PBL environments.
However, we believe that the enhancements planned for the tool, to better support
decision making and for entertainment applications, should be introduced before testing
IPG in practical experiments.
It is widely recognized, in fact, that characters of computer-generated stories and
games are expected to display lively personality traits, within the conventions of the
chosen literary genre ([41], [1], [2]). Another example is provided by the cooperative
interfaces [42] that emulate the behaviour of human beings, to provide useful responses
in a friendly fashion. Similarly, PBL environments, modelling real people and
corporations, cannot do without these relatively complex cognitive and affective
features, both to mimic real situations as closely as possible, and to offer an attractive
interface able to keep the attention of trainees.
References
[1] J. Carroll - The deep structure of literary representations. Evolution and Human Behavior
20 (1999) 159-173.
[2] N. M. Sgouros - Dynamic generation, management and resolution of interactive plots.
Artificial Intelligence 107 (1999) 29-62.
[3] G. Davenport, S. Agamanolis, B. Barry, B. Bradley and K. Brooks - Synergistic
storyscapes and constructionist cinematic sharing. IBM System Journal, 39, 3 & 4 (2000)
456-469.
[4] Special issue on digital entertainment - Scientific American, 283, 5, November (2000).
[5] A. E. M. Ciarlini and A. L. Furtado - Understanding and Simulating Narratives in the
Context of Information Systems. Proc. of the 21th International Conference on Conceptual
Modeling (2002).
[6] A. Dardenne, A. v. Lamsweerde and S. Fickas - Goal-directed requirements acquisition.
Science of Computer Programming 20 (1993) 3-50.
[7] J. Mylopoulos, L. Chung and E. Yu - From object-oriented to goal-oriented requirements
analysis. Communications of the ACM 42, 1 (1999) 31-37.
17 Proceedings of the PGL DB Research Conference
Towards a Plan-based Learning Environment
[8] M. Carlsson and J. Widen - Sicstus Prolog Users Manual, Release 3.0. Swedish Institute
of Computer Science (1995).
[9] P. C. Kanelakis, G. M. Kuper and P. Z. Revesz - Constraint query languages. Journal of
Computer and System Sciences 51 (1995) 26-52.
[10] A. L. Furtado and A. E. M. Ciarlini - The Plan Recognition/ Plan Generation Paradigm. In
Information Systems Engineering. S. Brinkkemper, E. Lindencrona and A. Solvberg
(eds.). Springer (2000) 223-235.
[11] H. A. Kautz. “A formal theory of plan recognition and its implementation”, in Reasoning
about Plans. J. F. Allen et al (eds.). San Mateo: Morgan Kaufmann (1991).
[12] Q. Yang, J. Tenenberg. and S. Woods. “On the Implementation and Evaluation of
Abtweak”. In Computational Intelligence Journal, Vol. 12, Number 2, pages 295-318,
Blackwell Publishers (1996).
[13] V. Propp. Morphology of the Folktale. Laurence Scott (trans.). Austin: University of
Texas Press (1968).
[14] S. W. M. Siqueira, M. H. Braz, R. N. Melo - E-learning content warehouse architecture.
Proc. of the IADIS International Conference - Lisboa (2002).
[15] C. Elliott: Using the Affective Reasoner to Support Social Simulations. Proc. of the 13th
International Joint Conference on Artificial Intelligence (1993) 194-201.
[16] G. Morgan - Images of Organization: the Executive Edition. Berrett-Koehler Publishers
(1998).
[17] A. L. Furtado and A. E. M. Ciarlini. “Constructing Libraries of Typical Plans”. Proc.
CAiSE´01, The Thirteenth International Conference on Computer Advanced Information
System Engineering, Interlaken, Switzerland (2001).
[18] A. Tversky and D. Kahneman - Extensional versus intuitive reasoning: the conjunction
fallacy in probability judgement. Psychological Review, 90 (1983) 293-315.
[19] W. Durant - The Story of Philosophy. Simon and Schuster (1961).
[20] A. Sloman and B. Logan - Architectures and tools for human-like agents.
Communications of the ACM 42, 3 (1999) 71-77.
[21] P. R. Cohen, H. J. Levesque - Intention is Choice with Commitment. Artificial
Intelligence 42, 2-3 (1990) 213-261.
[22] H. J. Levesque and G. Lakemeyer - The logic of knowledge bases. MIT Press (2000).
[23] A. S. Rao and M. P. Georgeff - Modeling rational agents within a BDI-architecture. Proc.
of the International Conference on Principles of Knowledge Representation and
Reasoning (1991) 473-484.
[24] S. Russell and P. Norvig - Artificial Intelligence - a Modern Approach. Prentice-Hall
(1995).
[25] P. Persson, J. Laaksolahti and P. Lönnqvist - Proc. of Stereotyping characters: a way of
triggering anthropomorphism? Socially Intelligent Agents - The Human in the Loop -
AAAI Fall Symposium (2000).
[26] E. Rich - User modeling via stereotypes. Cognitive Science 3 (1979) 329-354.
[27] R. W. Picard, E. Vyzas and J. Healey - Toward machine emotional intelligence: analysis
of affective physiological state. IEEE Trans. on Pattern Analysis and Machine
Intelligence 23, 10 (2001) 1175-1191.
[28] J. Bates - The role of emotion in believable agents. Communications of the ACM, 7, 37
(1994) 122-125.
Proceedings of the PGL DB Research Conference 18
Towards a Plan-based Learning Environment
[29] C. Breazeal - A motivational system for regulating human-robot interaction. Proc. of the
Fifteenth National Conference on Artificial Intelligence, Madison (1998) 54-61.
[30] J. D. Velásquez - Modeling emotions and other motivations in synthetic agents. Proc. of
the Fourteenth National Conference on Artificial Intelligence, Providence (1997) 10-15.
[31] G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman and T. J. Sejnowski - Classifying facial
actions. IEEE Trans. on Pattern Analysis and Machine Intelligence 21, 10 (1999) 974-
989.
[32] A. Ortony, G. L. Clore and M. A. Foss - The referential structure of the affective lexicon.
Cognitive Science 11, 3 (1987) 341-364.
[33] R. Willensky - Planning and Understanding - a Computational Approach to Human
Reasoning. Addison-Wesley (1983).
[34] G. Mantovani - Social context in HCL: a new framework for mental models, cooperation
and communication. Cognitive Science 20, 2 (1996) 237-269.
[35] P. R. Cohen and C. R. Perrault - Elements of a plan-based theory of speech acts. In
Readings in Natural Language Processing. B. J. Grosz, K. S. Jones and B. L. Webber
(eds.). Morgan Kaufmann (1986) 423-440.
[36] T. Finin, R. Fritzson, D. McKay and R. McEntire - KQML as an agent communication
language. Proc. of the Third International Conference on Information and Knowledge
Management (1994).
[37] C. Pelachaud, N. I. Badler and M. Steedman - Generating facial expressions for speech.
Cognitive Science 20 (1996) 1-46.
[38] P. O'Rorke and A. Ortony - Explaining Emotions. Cognitive Science 18, 2 (1994) 283-
323.
[39] S. Kaiser and T. Wehrle - Emotion research and AI: some theoretical and technical
issues. Geneva Studies in Emotion and Communication 8, 2 (1994).1-16.
[40] D. Rousseau, B. Hayes-Roth - A social-psychological model for synthetic actors.
Knowledge Systems Laboratory of the Department of Computer Science, Report KSL 97-
07. Stanford University (1997).
[41] M. A. Alberti, D. Maggiorin, P. Trapani - NARTOO: a tool based on semiotics to support
the manipulation of a narrative. Proc. of Computational Semiotics for Games and New
Media (COSIGN02). Augsburg (2002).
[42] J. J. Perez Alcazar, A. E. M. Ciarlini and A. L. Furtado - Cooperative interfaces based on
plan recognition and generation. XXVII Conferencia Latinoamericana de Informática
(CLEI) - Mérida (2001).
19 Proceedings of the PGL DB Research Conference