=Paper=
{{Paper
|id=Vol-2099/CAID17_paper2
|storemode=property
|title=Approaches to Embed Bio-inspired Computational Algorithms in Educational and Serious Games
|pdfUrl=https://ceur-ws.org/Vol-2099/paper2.pdf
|volume=Vol-2099
|authors=Michela Ponticorvo,Angelo Rega,Andrea Di Ferdinando,Davide Marocco,Orazio Miglino
|dblpUrl=https://dblp.org/rec/conf/ijcai/PonticorvoRFMM17
}}
==Approaches to Embed Bio-inspired Computational Algorithms in Educational and Serious Games==
https://ceur-ws.org/Vol-2099/paper2.pdf
Approaches to Embed Bio-inspired Computational Algorithms in Educational and
Serious Games
Michela Ponticorvo 1*, Angelo Rega 2, Andrea Di Ferdinando1, Davide Marocco1 and Orazio
Miglino1
1
Department of Humanistic Studies, University of Naples “Federico II” , Naples, Italy
2
IRFID, NeapoliSanit, Ottaviano, Italy
*michela.ponticorvo@unina.it
Abstract micro-worlds [Rieber, 1996] that can work as a lab where to
experiment something: behaviours, emotions, strategies in a
Bio-inspired computational algorithms can be effectively somewhat protected environment. Games can also start from
employed to develop games for learning. In the present
reality and go beyond, this is the case for hyper-realistic
paper we will introduce different approaches to embed this
games as war simulators or surrealist games.
kind of models in Serious and Educational Games. For this paper purpose, we will now focus on digi-
According to a multi-level description of game design
tal/electronic games that, in the last years, have assumed an
process, bio-inspired computational algorithms can be
important role in the game market with an ever-increasing
visible to the user, residing at an external, shell level; can be diffusion. Also their application in education has been
invisible to the user, residing at the core, internal level; can
massive for many reasons. Digital entertainment games
be employed in the evaluation and tutoring level pertaining
have some specific features that are very useful in an
to user profiling and supporting learning and teaching educational context: games engender motivation [Malone,
processes. This different approaches are explained by
1981], are engaging [Gee, 2003] and exploit learning by
introducing some educational games example: BreedBot in
doing [Aldrich, 2005].
which bio-inspired computational algorithms are used to To fully exploit game potentials, design plays a crucial role
develop the player-game interaction and are explicitly
[Chandrasekaran, 1990], as it must consider the interaction
visible by the user; Learn2lead, where these techniques are
between the player and the digital game, in the more general
used to model the game mechanics and are invisible to the frame of human-computer interaction [Lieto and Radicioni,
user; and Infanzia Digi.tales project in which these
2016]., keeping the lessons derived from neurosciences,
techniques are functional to develop smart educational
cognitive science and psychology, related to attention,
materials by implementing adaptive tutoring systems. executive functions and spatial cognition [Bhatt and Freksa,
2010].
To implement these aspects, computational models can be
1 Introduction taken into account, in particular bio-inspired computational
In recent years an epochal turn has been observed in models can be effectively embedded in games.
education coming from a twofold pathway. On one side, a Bio-inspired computational models, at the edge between
growing effort has been devoted to the use of new natural and artificial, are extremely fit for educational goals
technologies, in particular ICT technologies, as educational [Ponticorvo et al., 2016] if the goal is to teach biological,
tools. Technology-Enhanced learning (TEL) has intercepted psychological and social matters, as it will be evident later,
this tendency by promoting new educational practices, new because they allow to convey knowledge about dynamic and
communities and new ways of communication [Balacheff et complex system, emergence, evolution and development.
al., 2009]. On the other side, a lot of interest has arisen
about the use of game for learning. This interest is witnessed 2 Serious Games design process
by the numerous research branches that emerged, game- The SG design process can run according to a multi-level
based learning [Tobias and Fletcher, 2011], edutainment
framework, with two concentric levels, the shell and core
[Charsky, 2010], gamification of learning [Kapp, 2012], just
level and a ubiquitous one, the evaluation and tutoring level
to cite some. In particular many games have been developed [Dell'Aquila et al., 2016], represented in Fig.1. The shell
under the label Educational Games and Serious games
and the core level are present in every game of game, and,
which include card, board and videogames. Serious Games
more in general in almost every cultural product. The shell
(SG) are games that educate, train, and inform [Michael and level represents the visible content that is immediately
Chen, 2005], sharing the same educational mission.
accessible to players. It frames the game engine, the game
Why games are so appealing as educational tools? Games
dynamics that are hold in the core level. The third level, the
are often models of reality that simplify what happens in evaluation and tutoring level, is characterizing for
real world including some relevant aspects of it. They are
Educational and Serious games, as it allows, on the teachers In the case of educational games, the core modeling process
side, to understand if and how the player/learner has is addressed not only by the chosen kind of game, but also
acquired the concepts conveyed by the Educational game. on what content we want to convey.
If our goal is to build educational tools and materials which
are related to biology, psychology and sociology, exploiting
concepts such as emergence, complex and dynamic systems,
evolution and development, we can glean from a wide class
of bio-inspired algorithms.
Bio-inspired computing [Pintea, 2014] is a field of study
that exploits the study of natural phenomena to apply it to
machine learning: from evolution to genetic algorithms
[Goldberg, 2006], from natural complex systems to cellular
automata [Chopard and Droz, 1998], from the nervous
systems to artificial neural networks [Patterson, 1998].
A particular class of bio-inspired algorithms, Agent Based
models (ABM), is, in our opinion, particularly well-suited
for game design. ABM [Helbing, 2012] is a class of
Figure 1 A multi-level game design process with shell, core computational models used to simulate phenomena
and evaluation/tutoring level. Shell and core levels can be belonging to various domains ranging from biology to
found in every kind of game whereas the evaluation/tutoring psychology and sociology starting from the action and
level characterizes educational games interaction of simple agents. These agents are autonomous
and can represent individual or collective entities such as
groups. We adopt a wide definition of ABM: in SG, ABM is
2.1 The shell (game narrative) and core (game not used to understand collective behaviors starting from
mechanics) levels simple rules, but it aims at representing in detail agents
interaction and the agent itself. In other words, a great effort
The shell level represents what the player sees, what we call is devoted to modelling agents too, in this respect
the game narrative. Digital games, as many other cultural resembling multi-agents systems approach [Van der Hoek
products, are expressed trough a narrative metaphor that and Wooldridge, 2008] where agents can be very complex.
carries out the crucial role to give sense to the game. Let us If we adopt ABM, in the core level, every agent is defined
consider for example, the Monopoly game. Throwing the in function of its sensory features, what it sees, hears,
dice and moving on the boxes has the meaning to represent smells, touches in the setting and about the core, and action
real estate commerce and this strongly helps to engage the endowment, what it can do to affect the core state. These
player. actions must follow game rules that are defined both by
In designing the shell level we have to define the context: setting constraints and by agent actions chances residing in
who are the agents, what actions they can display, what the core level.
interactions are possible between them. The shell level, As we are in the domain of digital SG, agents can also be
based on narrative, holds an hidden level with a specific artificial agents: in this case the agent is not human, but a
operation, the game engine, the core level. bot whose artificial intelligence can rely on bio-
The game engine allows to implement core functionalities computational models as well.
related to game dynamics, for example related to physics,
animation, artificial intelligence, etc. The core level defines
precisely the characters with sensory-motor endowment, the 2.3. The evaluation and tutoring level
environment with its features and every possible interaction
between characters and characters/environment. In a SG, a relevant role is played by the evaluation and
These levels are in dynamic interaction and have strong tutoring level. A SG has an explicit educational goal that is
effects one on the other: the narrative provides a frame to allow the player to accomplish specified educational
where the hidden content resides. objectives. The evaluation and tutoring layer complements
The shell level is necessary in providing a semantic context the core and shell layers. This level analyze player’s game
to educational activities whereas the core level defines the performances relatively to the specified training objectives,
skills or the abilities to be transferred. and provides the players and the trainer, whose role is
In digital games, the core can host the game engine based on indeed relevant in Educational and Serious games, with
computational algorithms. important information and data about the learning process.
At this level we find learning analytics [Siemens, and Baker,
2.2 What the core level holds 2012], which are the measurement, collection, analysis and
reporting of data about learners to improve the whole
The core level holds the game mechanics, the engine. It can learning process. This level is also crucial from the teachers’
be conceptualized differently depending on the kind of point of view, as it provides specific tools and function to
digital game we aim at building. support teaching processes.
BrainFarm http://150.146.65.191/BrainFarm
3 Bio-inspired computational models in the
proposed multi-level framework
Bio-inspired computational algorithms can enter the SG
design process in many different ways and at different level
thus producing a diversified game typology. If the
designer’s goal is to build a SG that is explicitly addressed
to biology-related matters, bio-inspired computational
models can flow from the core level to the shell level, thus
becoming visible to the user. On the contrary, the designer
can leave a traditional appearance to the game whereas the
bio-inspired computational models work in an invisible Figure 1 The Brainfarm interface with custom neural
manner, staying in the core level. Moreover bio-inspired networks
computational models can be employed on the
evaluation/tutoring level, providing the artificial evaluator The breeding is implemented through a user-guided genetic
with a guise of artificial intelligence thus supporting the algorithm. The software side of Breedbot shows users a
teachers’ role. In the following sections we will present population of nine wheeled robots, with infrared sensors and
some example of such usage of bio-inspired computational motors and controlled by a simple feedforward neural
models. network, representing an artificial nervous system. The
neural network parameters are encoded in a genetic string
that will undergo an evolutionary process guided by either
3.1 Bio-inspired computational models in the shell the users (artificial selection) or the machine (automatic
level: when game narrative and mechanics selection). In this latter case the player can anyway
converge manipulate the relevant evolutionary variables.
By manipulating directly the parameters related to the
genetic algorithm the player can understand the
The first case is the use of bio-computational algorithms underpinning dynamics and experience different
starting from the core and arriving to the shell level: the evolutionary pathways in a controlled environment.
game mechanics are directly visible to the user. The shell In this example various bio-inspired computational models
becomes transparent and what happens in the core level can that flow from the core to the shell level in a pervasive
be accessed by the player; this way the SG becomes a manner. First of all, the robots are conceived as agents in the
virtual laboratory where the user can directly manipulate the wide conception of ABM. Each robot, in fact, is seen as an
relevant variables involved in the game, thus determining embodied agent interacting with a physical environment and
the game evolution in an immediate manner. This direct with other robotic agents.
manipulation takes place in a protected environment where Their artificial intelligence is implement adopting a
failures or error do not determine a menacing outcome. This connectionist framework with artificial neural networks and
virtue is counterbalanced by the unavoidable complexity their evolution/development carries out adopting
reduction. evolutionary algorithms. Moreover the player can affect
These games use Bio-inspired computational models for an directly the evolutionary pathway acting as a breeder that
explicit interaction mechanism. The user interacts with the selects the preferred agents. The breeder acts as an expert
game using traditional bio-inspired computational methods, using knowledge and expertise to select the best solutions.
for example by evolving a population, training an organism,
setting up an ecological system, etc.
An interesting example of this kind of games is Breedbot 3.2 Bio-inspired computational models in the core
and its sequels Bestbot, and Brainfarm [Miglino et al., 2008; level: using bio-inspired algorithms to model
Ponticorvo et al., 2006].
the game engine.
These are integrated software/hardware platforms that allow
players, even without any particular computer skill, to The second case is the use of bio-inspired computational
breed, within customizable virtual worlds, artificial models in the core level. The game engine is invisible for
organisms that can be downloaded onto real robots (Fig. 2). the player that interacts with the game in a traditional
These games can be reached though the following links: fashion, without perceiving what happens in the game
engine. Bio-inspired computational algorithm are used to
BestBot http://eutopia.unina.it/bestbot model complex system but the user does not interact with
the computational models directly.
These models can be derived from scientific theories in The game is played across numerous levels in which the
many different domains and, obviously, the choice is driven player will lead teams in different corporation departments,
by the designer educational objective. from the catering department to the research and
As hinted in the introduction, one model that can be fit for development one.
game design is ABM. This kind of models can be employed The game reproduces the day-by-day running of the
to model both the interactions between agents and the agent department, including jobs with precise deadlines and
itself. Moreover, if a careful description of agent is provided workloads. The player must assigning staff to work on those
in terms of what it perceives, it knows about the external jobs. The basic challenge is to ensure that followers finish
environment, and how it makes decisions upon its action, it all jobs in time and the leader must manage the assignment
is possible to model the agent behaviours according to a of followers on jobs and their performance. With a smart
specific psychological theory. management, the team can complete a respectable number
Agent based modelling has been used to build Serious of job tasks within their deadlines. However, leadership
Games in various contexts: for crowd simulation, economics involves more than management, and if the player uses a
and artificial societies, just to cite some. strategy for developing followers, there will be an effective
An interesting SG that exploits ABM to model and teach advantage in the game, in terms of completed tasks.
team dynamics is LearnToLead (L2L) [Di Ferdinando et al., Indeed the followers are not all equals, as they are well-
2015]. L2L is a web-based game where the player covers characterized by ability level, motivation level, stress level
the leader role and learns theories about leadership by and personalit. The player, acting as a leader must take these
governing a team of artificial agents, the followers. The variables into account in assigning players to jobs and
theoretical starting point is the Full-Range Leadership making action to affect the cited variables. It is, for
Theory (FRL), a well-known and widely-employed theory example, possible to run workshops, organize team-building
that explains leadership dynamics in small groups [Bass and events, perform one-to-one coaching, send memos, propose
Avolio, 1994]. The game mechanics is developed by using training course, give lectures about performance, deliver
two bio-inspired computational algorithms, namely ABM evocative speeches at staff meetings, etc.
and artificial neural networks. On the core level, L2L is a logical structure where an
In L2L game the shell level, the game narrative is clearly asynchronous interaction happen between the leader and the
separated from the core level: it appears as a point and click followers. A turn-based structure to play is implemented, so
game in a 2D environment which is played on the web. It that players always have an unlimited amount of time to
takes place in a firms office, as it is evident from some carefully consider their actions, and consult reference
decorative elements: desks, chairs, PCs and mobiles, stacks material about FRL if necessary before making an action.
of paper, etc. (Figure 3). The player acts on the work environment and team
dynamics by setting the working plan of each follower and
influencing followers motivation, stress and their
contribution to the team, with the action recalled before.
More specifically, when the leader takes some decisions
about one or more followers, these decisions affect the
followers. These decisions become input for the follower's
network and these inputs from the leader, together with the
ones coming from the external environment, modify the
agents’ internal states that, in turn, will change and
influence the follower contribution to team job (Fig. 4).
In brief, each agents’ behaviour is determined by external
and internal variables: the first ones consist of leader’s
behaviour (interact with followers), the working
environment (total amount of workload, approaching of
Figure 3 L2L office where the game takes place deadlines), and the social interaction with other followers.
The internal ones are instead those relative to the
psychological aspects of followers, between which
This physical setting varies across all game levels looking motivation is the most important. In particular, three sub-
nicer and nicer as the player advances in career. components of motivation are simulated: intrinsic, reward
In L2L there is hierarchical interaction between the player and fear. The intrinsic component represents the internal
acting as a leader and the followers, artificial agents. The form of motivation, driven by an interest or enjoyment in
human player must manage the team of artificial agents, the activity. On the opposite, the reward and fear
which stands for a team of workers in a bank, a post-office components model external forms of motivation, which rely
branch or a local government office, for example. on external pressures like desire for reward, or fear for
punishment. The peculiar features of these three
components have been modelled using a different temporal In this game, the bio-inspired models are completely
decay. In particular, the intrinsic component has a slower invisible to the users. In fact, they serve as effective
decay than reward and fear, but can be activated only by technique to implement the FRLT, whereas in Breedbot (see
appropriate leader behaviours (typically related to previous section), the bio-inspired models are relevant for
leadership style). the interactive process.
Another important variable to consider is the followers' This kind of games allows to observe dynamics that can
personality, which has been modelled taking into account emerge form agents interaction and this is an important
the McClelland [1978] theory. In particular, three different positive feature. It permits, in fact, to experience directly,
personalities (or motivational drivers, according to even if in a controlled situation and a safe environment what
McClelland theory) were considered: happens in a group context, thus complementing more
a. Achievement: followers pursue excellence in traditional and theoretical learning methods.
performance, a continuing drive for doing better all the The negative point is represented by the possibility that
times. Excellence can be achieved through individual the emerging complex dynamics can slip away and generate
efforts; unforeseeble outcomes.
b. Affiliation: followers are interested in establishing,
keeping, and restoring close personal relationships with
others; 3.3 Bio-inspired computational models in the
c. Power: followers pursue a status with impact on others. evaluation and tutoring level: when Bio-
High power motivation induces highly competitive inspired algorithms model human trainer
behaviour. expertise
The stress level must be kept under control during the game The last case is about bio-inspired computational model in
as well, because it affects the effort and the contribution of the evaluation/tutoring level. This level, that is
followers to the team work. characterizing for educational games, foresees a smart
Moreover, the followers ability has been simulated, as there interaction with the user/player. This smartness resides in
are followers smarter (faster) than others doing their jobs. adapting, inferring, profiling and anticipation, functions that
Thus, followers' performance is linked to their ability. mimic human teachers’ actions.
All these variables interact among each other, with the In other words, at this level, it is necessary to foresee tools
external stimuli and with the leader's behaviour, as depicted that extract two kinds of information: on one side, data
in Figure 4. about the learner such as learning style, preferences,
Personality and ability try to capture what the FRL theory weakness and strengths and, on the other side, about
says about individual consideration, so that the same leader teachers behaviour in order to reproduce artificially human
action may have a different impact on followers with trainer expertise.
different personalities or abilities. On the other side, the The evaluation/tutoring level implements what an human
leader who aims at raising the team motivation as high as expert in education would do while representing in an
possible needs to perform some individualised effective and concise way what the learner does.
considerations. Leaders should also pay attention when For example, this level provides an appropriate and timely
assigning followers to the same workgroup, as conflicts may feedback to player action, it adapts to player special needs
emerge depending on followers’ personality (Fig. 4). according to actual performance and the desired educational
In this example, Bio-inspired computational models are goals, it tracks player performance in terms of achievements
used to model the core of the game using an ABM and improvements. This smart interaction can mediated by
approach, moreover each agent artificial intelligence is the use of Intelligent Tutoring Systems (ITS) [Carbonell,
modelled using an artificial neural network, whose input and 1970].
output represent the already described external and internal Many examples can be found about this issue, as it has
variables. arisen a strong interest since research about ITS was born in
the seventies (for a recent review, see Wenger [2014]).
One key feature in ITSs is the presence of a student model.
To address the educational process it is crucial to pay
attention to a particular student’s cognitive and affective
states in order to tailor the whole teaching and learning
process on the individual.
To achieve this goal, it is necessary to build a student profile
and a fruitful way to do it is to employ specific data analysis
methodologies.
Figure 4. Agent model in L2L Learning analytics rely on huge amount of data that can be
used to improve learning. Educational data mining, for
example, is a research branch devoted to processes designed
for the analysis of data from educational settings to better as well as bio-inspired artificial learners. This can be done
understand learners and the settings which they learn in. starting from the regularities extracted by Educational data
Data mining, for specific learning goals too, can be run mining and by modeling learner/teacher and their interaction
adopting bio-inspired methods as neural networks [Lu et al., exploiting, once again bio-inspired computational
1996]. algorithms.
It can be also useful to run data clustering analysis and Bio-
inspired methods can be used for clustering data, thus 4. Discussion
illustrating another way these methods can be embedded in
The design process that leads to Educational and Serious
the evaluation/tutoring level.
Games can derive useful hints and borrow models from bio-
Data clustering consists in finding homogeneous groups in a
inspired models, meaning that artificial intelligence has a
dataset and Bio-inspired algorithms can be employed to find
deep impact on how cognitive elements are embedded in
new methods for clustering that include the human expert
people-centred design for games [Vanden Abeele and Van
role. In particular Interactive Evolutionary Computation
Rompaey, 2006].
(IEC) techniques [Bintrup et al., 2006] can be used. In this
The multi-level framework proposed in this paper goes in
case, a human breeder selects cluster configurations on the
this direction and allow to explicitate relevant issue on the
basis of their graphical visualizations.
future direction for the contribution of artificial intelligence
Data clustering is based on the analysis of explicit
and cognitive issue to game design. Indeed bio-inspired
information and quantitative variables (dimensions) that
computational methods can be applied effectively in
describe a given phenomenon and on latent and implicit
designing Serious and Educational games as they are
information captured by human cognitive mechanisms: this
isomorphic to teaching subject in the case of biology,
implicit analysis is what characterize human experts, also in
psychology, sociology.
education domain. The human experts are usually trained
Moreover teaching and learning with digital games can lead
for many years to recognize (categorize) natural phenomena
to neglect some relevant aspect that are, on the contrary,
on both explicit and latent information even if they cannot
fundamental in other educational contexts, such as physical
explain how they do it.
embodiment, autonomy, social interaction, evolution and
IEC allow to embed this feature in Evolutionary
development. These aspects allow biological organisms to
computation with the intervention of a human operator that
successfully adapt to unknown and changing environments
interacts with the artificial evolution process.
and widen artificial intelligence to embodied artificial
intelligence [Pfeifer and Iida, 2004].
The positive feature of this kind of models is that, applied to
teaching and learning processes, they can capture interesting
regularities that help profiling the student/player/user. This Acknowledgments
process supports teaching and improves learning, but it The INF@NZIA DIGI.tales has been funded by Italian
doesn’t foresee a complete teachers substitution. This Ministry for Education, University and Research under
compensates the dark side these methods display, that is the PON-Smart Cities for Social Inclusion programme.
temptation to image the educational process with a learner
totally immersed in a digital, automatic, artificial Authors would like to thank Onofrio Gigliotta for Breedbot
environment without any human contact. materials.
It is our opinion, on the contrary, that, especially in some
life periods, such as infancy, the social dimension of References
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