Kolski C., Garbay C., Lebrun Y., Badeig F., Lepreux S., Mandiau R., Adam E. (2014). Interactive

Surfaces, Tangible Interaction: Perspectives for Risk Management. In P. Millot (Ed.), Risk Management

in Life critical Systems, ISTE-Wiley, London, pp. 351-373, ISBN 978-1-84821-480-4

Interactive surfaces, tangible interaction: Perspectives for risk management

17.1. Introduction

Many researches concern currently interactive surfaces and tangible interaction, relatively recent research ways in

Human-Computer Interaction domain (see for instance the last editions of the CHI conference). In this chapter, we are

particularly interested by interactive tables (also called tabletops): they can be considered as new interaction platforms, as

collaborative and co-localized workspaces, allowing several users to interact (work, play, etc.) simultaneously. There is a

growing interest for these interaction platforms: since the first prototype, called Digital Desk proposed by Pierre Wellner

[WEL 91], many prototypes and commercialized products have been proposed [KUB 11]; dedicated conferences and

workshops (e.g. IEEE Tabletop, ACM Interactive Tabletops and Surfaces, etc.) are also organized.

Our approach consists to use the TangiSense interactive table. It is equipped with RFID technology and is connected

with a multi-agent platform (JADE) (for more details about multi-agent approaches, the reader may read the papers on the

subject, for example [CHA 92] [FER 95] [WEI 00]). It allows users to interact with tangible and virtual objects.

Our goal is to share an application between several users, platforms (tabletops, mobile and tablet devices and other

interactive supports) and types of interaction, allowing distributed human-computer interactions [LEP 11] [LEP 12]. Such

approach may lead to new perspectives for risk management; indeed it may become possible to propose new types of

remote and collaborative ways in this domain.

The second part of this chapter presents a state of the art. In the third part, our proposition is explained: it consists of

User Interfaces (UI) distributed on interactive tables and other surfaces for risk management. Two case studies illustrate

this research: the first one concerns road traffic management; the second one is an implementation of the Risk game. A

conclusion and research perspectives end this chapter.

17.2. State of art

The state of art is composed of two parts. The first one is devoted to usual interaction supports for Risk management.

The second part is about new interactive surfaces and tangible interaction: they will be envisaged in this chapter as new

supports for Risk management.

17.2.1. Supports for Risk management

Crisis and risk management is a research field of growing interest, known to raise numerous scientific challenges

[NEW 01] [HOO 00]. Decision-making, planning and action have to be operated under a heavy cognitive pressure and a

high degree of unpredictability, by complex social organizations that may be characterized as distributed, open,

collaborative, and multicultural [DUG 10].

Knowledge and information sharing, together with context awareness is a crucial issue in this respect [BOY 05]:

people must be provided with the right information, under the right format, at the right abstraction level and at the right

Chapter written by CHRISTOPHE KOLSKI, CATHERINE GARBAY, YOANN LEBRUN, FABIEN BADEIG, SOPHIE LEPREUX, RENÉ MANDIAU, EMMANUEL ADAM.

Risk Management in Life Critical Systems

moment. Contextual awareness is mandatory to reinforce understanding the reliability and usage of knowledge. To

increase operator’s efficiency, information display, preferably based on effective layout of the geographical environment,

must allow to easily getting mental representations of situations [OWR 01]. Despite recent advances in the field of virtual

reality, current collaboration support systems are often limited to mere communication tools (e.g. Google Wave or Wiki),

working under clearly defined and bounded contexts. Their adaptation to crisis management [RUP 07] is only possible for

well-circumscribed urgency routines with no exceptions [FRA 10]. Varying communication and collaboration needs are to

be considered in addition, depending on the type of organization: small teams, widely distributed organizations, smart

communities, etc. Some other work approach crisis management under a service-oriented or information system

interoperability viewpoint, like the SoKNOS German project (see http://www.soknos.de/) or the IsyCRI ANR project (see

http://www.irit.fr/IsyCRI).

Incompleteness of knowledge, uncertainty and paucity of information is also a major issue [BOY 05] [OWR 01],

which combines with the difficulty to predict changes in the environmental or human situations to be faced. Errors or

inappropriate actions are prone to occur in this case, which can hardly be recovered by written procedures or protocols.

For [SEN 00], pro-activeness is the only way to maintain a consistent coupling between processing structures and

environments that are evolving, subject to unpredictable changes and uncertainty and too complex to be perceived

completely. Heavy support may however result in making human operators passive, and facilitate complacency [ØWR 01].

As quoted by [ROG 06], what is needed is “proactive people rather than proactive systems”, that is systems that act

through incentives rather than directives.

How to share authority and control between human(s) and machine(s) has been studied since the early 1980s [MIL 95]

[MIL 12], with a distinction often performed between strategic, tactical and operational tasks, between vertical

(supervision) or horizontal (task sharing) allocation styles. A difficult issue, especially in the domain of risk management,

is to decide whether the system is meant to assist human decision-making by providing missing information or

complementary analysis or whether it is to supervise human actions and prevent potential errors. The current hierarchical

model of control, where authority is centralized on the ground, is evolving toward a distributed model of authorities where

coordination occurs among evolving agents communicating through a common frame of reference [BOY 09]. An original

approach to man-machine cooperation has been proposed in AMANDA (Automation and MAN-Machine Delegation of

Action) [MIL 12]. This system offers the possibility for controllers to delegate some tasks via a Common Work Space

(CWS). CWS plays a role similar to a blackboard, displaying the problems to be solved cooperatively and the evolution of

the solution in real time. Current evolution toward ubiquitous computing and cloud technology results in the development

of “Systems of systems”, which involve networked human and machine agencies [BOY 13], with an increased potential

for context-sensitive processings via pervasing sensing.

In our view, collaborative support must not result in constraining or driving human action. Rather, it must allow

enhanced context-awareness and support proactiveness. Tangible interfaces allow working under informal, opportunistic

styles [GUT 08], thus implying increased attention to coordination issues. Our proposal is to rely on dedicated objects,

called tangigets [GAR 12] [LEP 12], to assist coordination. Informed virtual feedback is further implemented to situate

human action with respect to the rules constraining collaboration. Our hypothesis is that constructive collaboration

involves not only the sharing of other’s actions, but also and more deeply the sharing of the (often implicit) norms and

rules driving these actions [BOU 01]. In this way, collaboration is seen as a conversational process embodied within the

physical workspace [SHA 10].

17.2.2. Interactive surfaces, tangible interaction

Interactive surfaces are becoming more and more numerous and varied in everyday life (eg, tablet, touch screen laptop

that can be placed horizontally, smartphone offering varying screen sizes, interactive table ...). These surfaces can be of

different sizes and may offer different forms of interaction. In particular they can be tactile (most common case), but also

tangible, i.e. provide an interaction with physical (tangible) objects. Our work focuses on this type of tangible interaction,

which corresponds with an emerging technology [BLA 07].

The Tangible User Interfaces (TUI), include interaction techniques with which the user interacts with a digital system

through the manipulation of physical objects [HOR 06] [ISH 08a] [ISH 08b] [ULM 00]. Tangible objects associated with

virtual world are proposed by Rekimoto and colleagues [REK 01] which present Datatiles a modular platform mixing

physical and graphical objects. The idea have influenced Walder and colleagues [WAL 06] who propose work, akin to

Interactive surfaces & tangible interaction

those of Rekimoto, through an assessment of tangible interface. So tangible objects interest researchers, especially if it is

possible to include them in interactive systems. Tangible User interfaces have been developed in several interactive

systems: augmented reality [LEE 04], collaborative system [STN 02], embodied system [BAK 12], games [MAR 13],

interactive surfaces, in particular tabletops [COU 07].

The interactive tabletops have successfully growing since 1991 with the Wellner’s DigitalDesk [WEL 91]. The

concept of interactive table can suppose a collaborative and co-localized workspace allowing several users to work at the

same time [KRU 03]. Nowadays, there is not many platforms allowing a simultaneous collaboration between users (such

as multi-pointing or sharing of documents in real time) [DEI 01] [WIG 06] [SHA12] [YUI 12]). The technology evolves in

term of capture system, for example: FTIR (Frustrated Total Internal Reflection (FTIR) technology, Diffused Illumination

(DI) technology, Diffused Surface Illumination (DSI) technology [HAN 05], RFID technology [KUB 12], optical fiber

[JAC 09], capacitive technology [DIE 01]. In terms of display dimension, various propositions are described in the

literature: from 24’’ [WEI 10] to 85’’ [LEV 06]. For more details on technological aspects, see [KUB 11] who presents

many tabletops and there features.

Finally there are tables that enable tangible interactions. This is the case of [WEI 09a] [WEI 09b] offering in their

work a system of tangible widgets, called SLAP (Silicone iLluminated Active Peripherals) for use on an interactive table.

Patten and his colleagues [PAT 01] propose SenseTable, a platform for tracing wireless object for tangible user interfaces.

A set of prototypes is then proposed to exploit the capabilities of interactive tables [PAT 02] [STÂ 02] [NOM 04]. Some

prototypes are associated with a new technology that can recognize shapes printed on objects on the table via a camera

[KAL 06] [JOR 07] [MAX 09]; RFID technology will also be used to allow detection of physical objects [OLW 08]

[HOS 08] [KUB 12]. RFID technology is used by the TangiSense table (designed and produced by the RFIdées company,

www.rfidees.com), illustrating this chapter. We will see in the following sections potential benefits of this technology for

crisis management.

17.3. Proposition: distributed UI on interactive tables and other surfaces for risk management

Many risk management situations involve different groups of people which have to collaborate (cf. §17.2.1). A major

difficulty for these people of various profiles and located in different places is to interact through different functions to

solve more or less complex problems.

A distribution of the user interfaces may bring new perspectives in this domain. According with [LEP 11], two global

strategies may be proposed:

- In the first strategy an interactive table is declared to be the master and the other devices are slaves (the master

table is the central object in Fig. 17.1a). In this case, the table manages the information transfer according to the

objectives of each platform and it centralizes all the information available in the distributed system. The interface

distribution can be seen in the form of tree. The master surface is the root of the tree (in the figure, this surface is

the interactive table). The other platforms correspond to the nodes or leaves; in Fig. 17.1a, children

(S1)={S2,S3,S4,S5,S6,S7,S8}. This strategy is useful when the UI is complete on one support with priority and if

UI have to be distributed on other supports. Its disadvantage is that breakdowns are not tolerated.

- In the second strategy all the platforms are independent and at the same decision level (Fig. 17.1b). They form a

graph where n corresponds to the number of distributed UI (in Fig. 17.1b, n=9). Here, a relation between two

platforms means a distributed UI.

Risk Management in Life Critical Systems

(a) (b)

Figure 17.1. Two configurations for Risk management UI: (a) Centralized distribution of UI, (b) Network of Distributed UI [LEP 11]

A first illustration concerning risk management (and even in this case, crisis management) is given in Fig. 17.2: a

possible use of TangiSense tables is proposed, involving tangible and/or virtual objects; various devices (interactive tables

or other surfaces) used by several users are also visible in the figure.

As explained in [LEP 11], when a significant event such as a forest fire occurs, the people concerned are not together.

Some are situated at a place where information is centralized; supervisors/decision makers are to be found among them.

They collect information from other actors who are geographically separated on the ground, concerning elements such as

like the state of the fire, its propagation velocity and the direct implications. The crisis unit makes decisions based on the

collected information and must transmit them to the on-site teams. They are also in contact with other structures such as

the police officers who must, according to the case, prohibit access to certain zones or warn/evacuate the potential disaster

victims. The state of the system at one given moment with an example of use per device and actor is shown in Fig. 17.2.

Figure 17.2. Crisis unit using TangiSense and other platforms (adapted from [LEP 11])

Interactive surfaces & tangible interaction

17.4. Case studies

In this section, our objective is to present briefly two prototypes of distributed applications developed on interactive

tables. The first one concerns road traffic management; the second one is an implementation of the Risk game. These

applications may bring perspectives for the study and development of new types of applications usable or adaptable for

risk management domain.

17.4.1. Distributed road traffic management

In this section, we show a case study related to the distributed simulation of road traffic management to implement a

network configuration presented in the section §17.3.

The simulator of road traffic proposed is a network composed by links (roads, highways, etc.) and nodes (crossroads).

Its first version on one interactive table is described in [LEB 12] [KUB 13] and [LEB 13a]. The simulator is intended to

be used by experts in security, architecture, transportation and also by non expert like local elected member to obtain

agreement on road or infrastructure modifications. The simulator aims to find a common policy on the management of the

traffic jam in the presence of a crisis (natural disaster, zone evacuation, sporting events, etc.). It is important for the public

authorities to anticipate on the impact of the events on traffic jam and the risks in terms of safety (accidents, emergency).

The authorities will regulate access to several key points: the input / output highway, near the sports complexes, shopping

centers, hospitals and fire stations. These problems require a strong coordination of heterogeneous actors; for example, to

close a road, evacuate people from a specific location, specify the location of a fire engine on the table. This coordination

is expressed through generic tangible objects, called tangigets [LEP 12] on the table to activate a function (e.g., I start a

task, my task is done, etc.).

In the case study, the simulator is implemented on two connected interactive tables. Tables share the same virtual

environment as shown in the Figure 17.3.

Figure 17.3. A road traffic simulation on two TangiSense interactive tables

The virtual environment is composed of the road network and virtual agents. Virtual agents are represented on each

tabletop by vehicles moving randomly or according to a set of goals on the entire road network. These agents have

behaviors that allow or not for example to respect the Highway Code [DON 06] [DON 08]. The agents are managed using

the JADE (Java Agent DEvelopment Framework) platform [BEL 01], a FIPA (Foundation for Intelligent Physical Agents)

standard software implemented in Java, and used to simplify the deployment for the multi-agent applications.

Each table has a set of tangible objects (specific to the traffic management application) and tangigets (usable in

different types of application, not only traffic management) to interact locally with the simulation but also with the remote

simulation (on the other table). In this case, the display may be different as shown in Figure 17.4. This figure shows that

the user manipulates the simulator locally using an object, called Zoom (allowing zooming in and out on particular zones

of the traffic); this kind of object (with local properties) is used for manipulating the map. This tangible object is

manipulated by the users and it interacts with other virtual objects. This object is equipped with RFID tags to be able to

Risk Management in Life Critical Systems

modify the network structure. For example, to move the map, to view information about the name or the speed of the

road, to zoom in or out and change the scale of the map, the user manipulates different tangible objects.

Figure 17.4. Use of Zoom tangible object, without effect on the other table

To interact remotely with the other table, users have the ability to use tangigets (tangible object managed by a software

agent with distributed properties). These properties allow it to be cloned using agent located in another interactive surface.

Figure 17.5 shows the use of a tangiget, called Synchronisation. The advantage of this tangiget is to coordinate display

tabletops to work together on the same area. When this object is put down on the TangiSense 1, the TangiSense 2 will

generate a clone agent; these two agents (the agent managing the tangiget and the clone) exchange messages in order to

keep the data coherence. Messages contain the position and the scale of the map. This message is used by the TangiSense

2 to obtain the same vision as the tabletop 1.

Such representative examples let envisage new functions concerning traffic management usable by future risk

management systems. They can be distributed on two interactive tables [LEB 13b], with a generalization to several

interconnected tables: the interactions between interactive surfaces enable the collaboration and the information exchange

between different users during simulation sessions.

Figure 17.5. Tangiget Synchronisation with effect on TangiSense 2

17.4.2. Distributed Risk game

The Risk game is a strategic board game for two to six players, played on a board depicting a political map of the

Earth, divided into territories [PAR 63]. Players are allocated armies and fighting power, placed on occupied territories.

An attack takes place between two attacking/attacked territories that the attacking player has to designate. Dice rolls are

then used by both players to determine who is loosing or winning this round. The assault continues until the attacking

player decides to retire, or until one of the two is eliminated (all his or her armies on the attacking territory have been

Interactive surfaces & tangible interaction

lost). Figure 17.6 displays an example view of the Risk game (prototype version), as played on the TangiSense table. As

in the previous application, the agent aspects are managed by using the JADE platform.

This application is a case of a static environment of limited complexity, with players operating at the same

organizational level, under strict coordination rules. In this application, as in all games, the "risk" does not come from

critical evolutions in the physical environment. Rather, it comes from the unpredictable character of the player's

intentions, and from the fact that opponent players are willing to win the game. Getting accurate pictures of situations

(past, current as well as expected moves), is mandatory to play correctly. This raise challenging issues when playing from

distant places, with no face to face communication with other players (we consider that the players have no

communication means except the table and its tangible and virtual equipments).

In this context, the goal of our design is (i) to provide rich communication, through a wide palette of tangible objects

and informed virtual feedback, (ii) to ensure the follow-up and sharing of rules, alleviating the burden of

mentalizing/guessing complex norms, and (iii) to offer a coordination framework ensuring smooth interplay, while leaving

room for proactivity.

Figure 17.6. The TangiSense table as equipped for the Risk game with ground map display, tangible objects and virtual feedback

shown.

We propose to address collaborative activity as a conversational process where the signs of dialog are provided by

tangible object moves and virtual feedback. Face-to-face oral conversation usually involves several speech acts (designing,

acknowledging, referring, turn taking, giving speech) that may reveal difficult to model in this limited context. Dedicated

tangible objects (called tangigets) are designed to this end. In addition, informed virtual feedback will allow the follow-up

of tangible object moves on distant tables together with additional information about the context in which these moves

have been operated. Beyond conversation, context awareness is a core issue, especially in the case of complex

organizations that may exhibits heterogeneous working styles and conventions. It should not be reduced to exchanging

views, or sharing the production of results. Rather, we approach context awareness as sharing the task-dependent and

organization-dependent constraints that frame activity. This is ensured in our design by the handling of numerical and

physical traces, which reflect both human activity and its relationship to the constraints under consideration (virtual

display underneath a tangible object reflecting the conformity of its move to the actor responsible for this move). These

feedbacks may be considered as incentives for the co-evolution and better coordination of actions among the partners of

the collaboration.

Following the Clover approach for groupware design [LAU 02], we propose an architecture coupling production,

communication and coordination spaces (figure 17.7). The system is designed as a normative multi-agent system, to ensure

a clear distribution of the tasks to be undertaken among the three spaces, a clear separation between explicitly modeled

constraints and autonomously acting processes, a situated follow-up of concurrent and interleaved traces, a context-aware

communication with human actors.

Risk Management in Life Critical Systems

Figure 17.7. Functional view showing the various types of agents, filters and traces.

Numerical traces are defined as multidimensional components, whose role is to reflect the actors’s (either human or

agent) activities and their conformance to the task requirements. Any trace is considered as a set of pairs (property, value),

with properties typed, to register their compliance to the norms. Traces evolve under the activity of agents and norms. Any

trace is defined as possessing the following minimal properties (identifying name, type of tangible object, tangible table

where originated, spatial position on this table, time of move):

Trace = {(type,vtype), (table,vtable), (name,vname), (pos,vpos), (time, vtime) }

A norm has the following simplified expression: N = <id, context, role, object> in which context represents an

evaluation condition, role represents the agent’s role concerned by this norm and object is a complex field, typically

written as launch (conditions, actions) characterizing the conditional action attached to the norm (launching of agents’

behavior, annotation of trace properties). We distinguish between three kinds of norms. Communication norms ensure the

creation of traces to follow-up tangible objects move. They further ensure the launching of feedback agents. Production

norms launch production agents to manage trace elements. Coordination norms include consistency norms that check the

compliance of human activity with respect to current norms. These norms enrich the trace via the corresponding trace

properties fields. Some coordination norms specify constraints over the collaboration process and launch coordination

agents to this end.

An agent is defined as: Ag = <id, role, behaviors, norms> with id a unique identifier, role the role of the agent in the

system ( {production, communication, coordination}), behaviors a list of concurrent agent abilities, norms the set of

norms that the agent has subscribed to. We distinguish between three types of agents: production, communication and

coordination. Communication agents ensure the follow-up of incoming traces and the launching of virtual feedbacks.

Production agents perform the computations required by the task under consideration (trace elements analysis and

interpretation). Coordination agents maintain the consistency of the trace components in a context where distant human

performs concurrent actions. They further ensure the adequacy of the set of norms to the current task and step of the

collaboration. Such design follows the definition of normative multi-agent systems: sets of agents (human or artificial)

working under norms “serving to guide, control, or regulate proper and acceptable behavior” and defining “a principle of

right action binding upon the members of a group” [BOE 06]. Deontic rules are usually defined, to express permission or

obligations regarding the way norms are applied, that are not considered in the present proposal.

When applied to the Risk game, the system operates according to the following information flow: (1) early detection

of a tangible object move by communication norms operating at the infrastructure level: creation or updating of the

corresponding local trace; (2) triggering of the coordination norms: updating of the corresponding local traces; (3)

triggering of the production norms: computation of some local trace property; (4) triggering of the communication norms:

feedback to local and distant human actors. When the game starts, a default norm policy is activated to handle the process

associated with game initialization. When the game comes in the state fight, the players handle two specific tangible

Interactive surfaces & tangible interaction

objects (tangigets) to proceed to designation of attacking territories and dice rolls. New norm policies must be applied to

deal with these new states. Coordination norms are designed to this end. The example of the Norm-attack-policy is

provided below: its role is to launch a coordination agent Manage-norm-policy to perform the necessary updating and

ensure game consistency.

Norm-attack-policy = <id, [step = fight], synchronization, launch(cond, Manage-norm-policy)> with cond =

[trace.type(?t1) = coordination] & [trace.onTable(?t1) = ?tab1] & [trace.value(?t1) = “attack”] & [trace.type(?t2) =

designation] & [trace.onTable(?t2) = ?tab1] & & [trace.value(?t2) = ?jd]

At the end of a fight, each player handles dices to determine a winner and a looser for this fight. The production norm,

called Norm-dice-result, ensures the follow-up of the dice roll results, the determination of a winner and loser and the

launch of an agent whose role will be to update the traces accordingly:

Norm-dice-result = <id, [step = fight], dice-result, launch(cond, win)> with cond = [trace.type(?t1) = dice] &

[trace.value(?t1) = ?v1] & [trace.onTable(?t1) = ?x] & [trace.type(?t2) = dice] & [trace.value(?t2) = ?v2] &

[trace.onTable(?t2) ?x] & [?v1 > ?v2]

This application is also representative of new possibilities offered by interactive tables connected with a multi-agent

platform; we think that such concepts are also adaptable for risk management domain. More details about this application

are available in [GAR 12] and [BAD 13].

17.5. Conclusion

Interactive surfaces and tangible interaction are two research and development themes which are particularly studied by

the Human-Computer Interaction scientific community, as well as by many IT companies, leading to many propositions,

prototypes and products. In this chapter, the TangiSense interactive table and its potentialities have been presented. Its

architecture allows UI distribution between different interactive tables and surfaces in general. This distribution is

possible by integrating an intelligent management of the UI distribution into the agents of distributed multi-agent systems

(developed with JADE).

Several developments of distributed interactive applications on one or several TangiSense tables are in progress. This

chapter was first focused on road traffic management which is on important problem to consider in case of crisis

management. For instance, it is important to study and find solutions in case of traffic jam, serious accidents implicating

many vehicles, forest fire or flooding with consequences for the traffic, and so on. A distributed road traffic management

simulator has been implemented on two connected TangiSense tables. The second application, implemented on several

interaction supports, and described in this chapter, concerns a distributed Risk game, a well-known game in which risk

management is a central aspect for the users involved.

Such new interactive applications distributed on several supports offer new research and development ways for risk

management. Several perspectives may be underlined. In fact, it is important to continue the technical tests with several

tabletops and other interaction supports. It is also possible to envisage different types of surfaces (floor, wall…). We plan

also to take into account real and more and more complex scenarios, and to prepare and perform different types of

evaluations with distributed contexts.

17.6. Acknowledgements

This research was partially financed by the French Ministry of Education, Research & Technology, the Nord/Pas-de-

Calais Region, the CNRS, the FEDER program, CISIT (Plaiimob project), and the French National Research Agency

(ANR TTT and IMAGIT projects, financial IMAGIT support: ANR-10-CORD-017).

Risk Management in Life Critical Systems

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