=Paper=
{{Paper
|id=Vol-2321/short1
|storemode=property
|title=Co-constructing Subjective Narratives for Understanding Interactive Simulation Sessions
|pdfUrl=https://ceur-ws.org/Vol-2321/short1.pdf
|volume=Vol-2321
|authors=Anne-Gwenn Bosser,Ariane Ariane Bitoun,François Legras,Martín Diéguez
|dblpUrl=https://dblp.org/rec/conf/aiide/BosserBLD18
}}
==Co-constructing Subjective Narratives for Understanding Interactive Simulation Sessions==
https://ceur-ws.org/Vol-2321/short1.pdf
Co-constructing Subjective Narratives for Understanding
Interactive Simulation Sessions
Anne-Gwenn Bosser Ariane Bitoun
Lab-STICC, CERV, ENIB MASA Group
Brest, France Paris, France
bosser@enib.fr ariane.bitoun@masagroup.net
François Legras Martin Diéguez
Deev Interaction Lab-STICC, CERV, ENIB
Brest, France Brest, France
francois.legras@deev-interaction.com dieguez@enib.fr
Abstract
Stories are used by human beings to transmit knowledge, explain events, and
generally make sense of the world. Creating a narrative is then considered as
an activity producing meaning, allowing the narrator to formulate causal rela-
tions between selected events. Through the act of telling a story, the narrator
makes explicit their own understanding of a given situation.
In this paper, we describe an ongoing project, based on an existing simulation-
based training software. We are creating a set of tools allowing users of such
software to make sense of what happened during the simulation from their
point of view. Based on previous work allowing to represent the causal flow
of events formalized as actions, we describe the current issues we tackle for
providing a tool helping users to describe their own view of what happened.
In addition to helping with reflection about the training session, such a tool
has the potential to support learning by allowing to contrast different points
of view during pedagogical activities such as cooperative learning or tutored
debriefing.
1 Introduction
Simulation Training is a form of experiential pedagogy, considered as particularly effective. It may be underpinned by
software (the family of serious games, or useful games dedicated to learning), and associated with a debriefing session
allowing the participants to understand what has happened during training [FG07]. This allows a safe and cost-effective
solution where participants learn from the actions they performed during the simulation.
Military Training often relies on a simulation whether it occurs at a sophisticated instrumented range, in a collective
training simulator system, or in a command and staff exercise using a mathematical model driven war game. Training
occurs in live, virtual, constructive, or mixed simulations of battlefield environments. In the live environment, units use
operational equipment and actual terrain to perform against an opposition force composed of military personnel (live
force-on-force) or targets (live fire). In virtual environments, units use simulators to represent equipment and weapons.
Weapon effects, terrain and enemy forces are computer generated. In constructive environments, battlefield outcomes are
Copyright c by A-G. Bosser, A. Bitoun, F. Legras, M. Diéguez. Copying permitted for private and academic purposes.
In: H. Wu, M. Si, A. Jhala (eds.): Proceedings of the Joint Workshop on Intelligent Narrative Technologies and Workshop on Intelligent Cinematography
and Editing, Edmonton, Canada, 11-2018, published at http://ceur-ws.org
determined by a computer simulation in order to provide battle effects supporting command and staff training. Training in
all of these simulation environments should provide individuals and units with feedback about how their actions contributed
to mission success or failure.
Broadly speaking, a training session starts with a phase of preparation, where the realistic operational environment
is created, followed by a phase of exercise where all the participants take part in the simulation and, finally, a phase of
debriefing where the players have an interactive discussion (guided by a moderator) in order to understand what happened
during the training and why, as well as how to improve or sustain performance in similar situations in the future. Duration
and timing of the discussion [AS94] are very important: too many details lead to a lack of concentration among the
participants, and inadequate timing tends to make them forget the reasons behind them taking a specific course of actions.
The large amount of data generated during training complicates this task 1 . To alleviate this, we propose to develop
a narrative creation toolkit for assisting human-made explanations, in terms of a story (or narrative), of a given simula-
tion session, to all the participants involved in it. The idea is to provide a semi-automated analysis of the course of the
simulation-based narrative reconstruction of the (potential) causal links that exist between the events that occurred in the
simulation, and tools to support their structured presentation. Causal graphs in the tradition of [Pea09] will then be inte-
grated to the war game replay interface by a system of vignettes that provides the participants with useful information for
explaining a given situation which occurred during the simulation.
2 Narrative debriefing for simulation-based training
Humans have always used stories to make sense of the world and explain the unfolding of past events. A number of
technology enhanced learning approaches have therefore naturally adopted narrative-based pedagogy [DP09]. In such an
approach, telling a story entails formulating causal relationships between selected events [vdBvOPV08, Abo10]. In the field
of education or serious games, storification [AAH09] is used to describe the creation of a causal structure by establishing
links between narrative events. One of the challenges in these areas is the realization of systems to automate or semi-
automate this activity in order to educate various user profiles. Conversely, studies in psychology of story understanding
have also shown the importance of the perception of causal relationships between narrative events [TS85].
While modeling causality occupies a central place in Artificial Intelligence (AI) [Pea09], Narrative Intelligence’s point
of view is closer to commonsense reasoning: the narrator must select the events to be told, express the causal links among
them and select a level of granularity of such connections in order to make the final story meaningful. Contrary to clas-
sical approaches from AI, narrative intelligence attempts to provide an explanation in a form that is presumably more
understandable for a human user [Rie16].
Our aim is not to provide a fully automated story construction system such as in recent machine learning approaches:
our system must support the confrontation of different points of view during debriefing and cooperative learning activities.
As such, it should help each user to construct and explain their own subjective narrative, depending on the information
they had access to (depending on the roles of the participants this may widely vary) and their decision rationale. The tutor
in charge will have access to all information and their constructed narrative will be different as well.
3 A Linear Logic based approach to story construction and analysis
The formalisation of narratives is a problem that has often been approached in Artificial Intelligence from the perspective
of Knowledge representation and Reasoning about Action and Change (RAC), starting from the atomic modelling of a
narrative action, and describing its impact on the environment. Authors of [BCC10, BCFC11] use Linear Logic [Gir87a]
for the modelling, which has led to formal approaches to story analysis and property verification. Among other advantages,
this approach allows to model in a declarative way each event, by describing its impact on the environment in terms of
consumption and production of resources. This has led to systems where stories generated from a linear-logic based
declarative specification could be described by reconstructing causal relationships between events and displayed in terms
of causal diagrams [MBFC13, MFBC14].
Building on these previous work, we propose to extend these formalisms in order to cope with two important aspects of
human understanding from the perspective of narrative analysis: the exploration of counterfactuals, and the granularity of
causality.
• Counterfactuals: from a psychological perspective, counterfactual reasoning is the mental simulation of alternative
scenarios of the type “what if ...”, where the invalidation of one or more events leads to the deduction of an alter-
native reality, which plays a central role in the judgement of causality associated to a set of events [Maz04]. In AI,
1 A short simulation may imply the generation of approximately 60000 messages
counterfactual reasoning was first formalised by D. Lewis [Lew73], who provided a clear semantics based on spheres
leading to a great deal of results in argumentation [Sak14], causality [Ort99] and hypothetical reasoning [Hal99]. The
problem has been recently revisited in [BBG18] where a novel formalisation in Answer Set Programming [BET11]
is provided. Exploring counterfactual scenarios entails analysing variants produced by the simulation in the replay
mode available in the tool.
• Granularity: the relationship between the number of causal relationships on a set of events and the importance of
perceiving an event in a story has been widely developed in [Maz04, TvdB85, TS85]. Based on these contributions, we
expect to develop heuristics that work on a predefined narrative structure. Those heuristics would allow, at least, the
assisted construction of a well-formed story that explains a given situation. Other heuristics based on domain-specific
knowledge as well as interaction patterns between the various actors of the simulation are also being explored.
We are currently working on identifying complex events as well as higher level actions and their possible decomposition2
4 A constructive simulation for military training
The training software we use relies on a constructive simulation which allows us to engage brigade and division command
staff in large-scale conflict scenarios such as stabilisation operations, terrorist threats or natural disasters. It simulates a
diverse range of situations in realistic environments and lets trainees lead thousands of autonomous subordinate units (at
platoon and company levels) on the virtual field. Agents can receive operation orders and execute them without additional
input from the players, while adapting their behavior accordingly as the situation evolves.
Models capturing such behaviors consist of two components: the algorithms that make agents perceive, move, commu-
nicate and shoot, and the description of the capabilities of the underlying equipment stored in a database. The simulation
session database contains three different types of information:
• The data regarding the physical element: Constitutions of the units are described here. Because the simulation
is constructive, most of the features of the equipment or units are described by their effects or their capacities. This
facilitates their description in terms of action and change.
• The initialization data for the scenario containing the following information: terrain, order of battle, weather, data
provided by the simulation such as events, knowledge obtained by the agents, etc.
• The data generated by the simulation describing the evolution of the situation: information describing the evo-
lution of the game containing all events, knowledge about the environment and all mission reports.
All this information is presented to the participants as a set of messages exchanged among the agents during the simulation
that contains all the information described above. We show an extract of the simulation below:
[07:29:47] - Report - ENG.Counter mobility platoon: Disembarkment
started
.....
[07:30:17] - Report - INF.Mortar troop: Unit detected at ...
.....
[07:30:17] - Report - INF.Rifle platoon: Unit detected at ...
Once the initialisation data, the elements of the simulation, and the report messages are translated into formal action
description, a raw analysis in the fashion of [MBFC13] is constructed. Visualization tools must then be developed to
support human explanation. The training software we use is equipped with a replay function which we intend to improve
with narrative content. Traditional military Command&Control tools are a suitable starting point (map layers overlaid with
specific symbology), but to make sense, certain points of view must be chosen for each node in order to understand each
situation and their relationships. We propose to build Vignettes to represent the most salient nodes of the narrative graph.3
This can provide an analysis of the current maneuver in order to :
1. replace the operator in the current situation and explain the current maneuver;
2. propose an automatic synthesis of the tactical situation. It can be the calculation of the current force ratio or simply a
realistic view of the geographic capacities of units (fire, intelligence, etc.) and illustrate a bad use of the forces on the
field.
2 Work on higher task decomposition has been considered in [HPX16].
3 The selection of the important nodes of the graph will be done via a mixed-initiative strategy involving automated scoring and user input.
a map thatbetween
erentiates differentiates between
zones that must zones that must conquered,
be recognized, be recognized, conquered,
controlled, etc., controlled,
or enemiese
must orbestopped.
ated eliminated
Thisor could
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be achieved be achieved
through through theofinterpretation
the interpretation the advancemenof t
current
s, missions,
and the nature ofand the nature
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missions.
3. calculate and alter the consequences of specific events . For example, calculating the delay for logistical units or
support units after a bridge broken event.
Support/supply
ply management management
Figure 1 presents examples of a vignettes representing delays and capabilities for some unit to support some other units.
Figure 1: Many-to-one fire-support capabilities toward the central blue unit against the red units (left). One-to-many
support capabilities of the lower left unit toward other friendly units (right). On these vignettes, timely and effective
Figure
support13duration
Fire support duration
calculation for calculation
a Figurefor
14 aSupport
Figure 14 Support
duration duration uni
for supported for
support capability is color-coded: from good (green) to poor (red). We use NATO Joint Military Symbology as defined
in [Nat14]
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5 Related work
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line of research with potential application in several scenarios such as military, health-care or business intelligence. They
a machine learning-based approach that takes into account the narrative structure as well as the causal links among
ta
the different events of the story.
The Bardic [BBCR+ 17] system uses narrativisations to describe the activity in complex domains in such a way that
the information becomes accessible to non-experts. This system translates a given log file into a first-order logical theory
4
expressed
PAPER NBR - 8 in Impulse [EBY15] and, from this representation, the system is able to obtain a causal graph STO-MP-IST-160-
by analysing all S
action preconditions with their corresponding effects.
Our proposal differs from [NYR+ 17] in the approach: we want the participants in the simulation to be able to author
(with assistance) what happened from their point of view, so whilst we plan to incorporate some localised supervised
learning to facilitate repetitive tasks, we are not looking for a fully automated machine learning based storyfication system.
With regards to the work reported in [BBCR+ 17], we find several similarities with the Bardic system : both systems are
supported by a logical formalism (In our case, we use a resource-based encoding of linear logic [Gir87b]) and both tools
are oriented towards the extraction of causal information from a source of data. However, the way such an extraction
is obtained differs: Bardic uses a STRIPS-based approach [FN71]. We work from an expressive logical formalism for
representing actions in Linear Logic, and plan to further explore the use of counterfactual causality [Lew73].
6 Conclusions and future work
In this paper we presented a research project relying on the use of logical tools for debriefing in simulation-based scenarios.
We base our work on previous approaches relying on the use of Linear Logic-based formalisms, and intend to exploit
counterfactual reasoning for allowing the co-construction of a causal graph that explains a given simulation by the user and
a narrative assistant. Finally, in order to display the causal information, we propose an interface based on vignettes.
We would like to remark that the use of a logical formalism for the narrative representation allows us to isolate the
kernel of our approach, and to apply it, modulo minimal changes, in a variety of simulation based training environments.
4 Impulse is a first-order temporal-epistemic framework for describing narratives that allows expressing temporal properties by means of Allen’s
relations [All83] as well as epistemic information of the characters thanks to the use of epistemic modalities [vDHvdHK15].
Acknowledgements
This paper is based on the STRATEGIC research project funded by the Direction Générale de l’Armement (DGA) through
the ASTRID Maturation program.
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