Towards User Centred Multi-Agent Systems :Illustration on Cooperative Agricultural Robots

Jakob Appel1, Kurt Nielsen1, and Yves Demazeau2

1 The Maersk Mc-Kinney Moller Institute for Production TechnologyUniversity of Southern Denmark

Campusvej 55, DK-5230 Odense M, DENMARK{appel,kurtn}@mip.sdu.dk

2 Laboratoire Leibniz-IMAG, CNRS46, avenue Felix Viallet F-38031 Grenoble Cedex FRANCE

Yves.Demazeau@imag.fr

Abstract. This paper reports first trials to include the User aspect into Multi-Agent Systems, which is a basic need if Multi-Agent Systems want to contributeto environments where the User is tightly coupled with decentralised systems,such as pervasive computing. The paper first outlines how Multi-Agent Systemshave developed from Agent Centred Multi-Agent Systems to Organisation Cen-tred Multi-Agent Systems. We exemplify this with problems from robotic sys-tems in the agricultural environment. We then elaborate how Multi-Agent Sys-tems should be designed by focusing on the User, and we propose to designatethis class of resulting systems as being User Centred Multi-Agent Systems. Weillustrate how this approach enables us to model and analyse more complex prob-lem domains than Organization Centred Multi-Agent Systems are able to. We fi-nally discuss the future directions and examples of possible application domainsof such User Centred Multi-agent Systems.

Key words: Agent-oriented software engineering and agent-oriented method-ologies, applications of autonomous agents and multi-agent systems

1 Introduction

The research in the Multi-Agent Systems (MAS) community has brought some newaspects to the forefront of MAS that were not initially accounted for. These aspectsconcern mostly organisation of agents or, more precisely, how to model and implementcollaboration among agents. In [1] it is argued that organisational and social science arestarting to influence MAS research. It is further claimed that software systems do notresemble societies in which the overall global behaviour derives from the self- interestedintentional behaviour of its individual members. So, it is safe to say that much thinkingmoves to support systems thinking and organic behaviour, which makes it difficult forus to envision MAS consisting solely of digital devices. We foresee the future MASas systems that comprise the interplay of human and User activity and digital devicesin order to increase the capacity of approaching and deserving human activity. Put inanother way, the question of User interaction in MAS has been raised precisely because

the technology has pushed us to the point where we can sensibly consider how MASshould fit in with the Users, instead of the other way around where Users have to fit intoMAS.

1.1 Structure of the paper

Section 2 deals with the theoretical and practical background of this paper, which areused extensively in the following sections. Section 3 presents the original ”Agent Cen-tred MAS” approach (ACMAS). This approach will be further elaborated by explaininghow agent architecture is typically designed in order to achieve a means-end reasoningagent. Lastly, the section illustrates the class of problems that the ACMAS approachaddresses by involving a practical agricultural problem. Section 4 elaborates on ”Or-ganisation Centred MAS”(OCMAS) which is a alternative development to the ACMASapproach. This approach is further clarified by introducing a concrete model, the Agent-Group-Role model [2], by which implementation and design of OCMAS can be real-ized. The last subsection illustrates the class of problems that the organisation centredapproach addresses by involving a practical agricultural problem. Section 5 concernsthe necessity of evolving the current MAS approach from OCMAS to ”User CentredMAS”(UCMAS). It proposes to extend the VOWELS method by adding a particularvowel, the U, to explicitely take into account the user. Lastly, the section ends witha description of a problem from the agricultural domain, that represents the complex-ity class of the problems that UCMAS address. Section 6 summarises on UCMAS aspresented in this paper, and briefly outlines perspective of applications for this MASapproach.

2 Background

This section introduces the theoretical and practical background of this paper. We willstart off by presenting the VOWELS paradigm [3], which proposes a MAS method thatbreaks down a MAS into its constituting parts. After this introduction, we will elaborateon the AgroBot project from which we present practical scenarios in order to illustrateour theoretical assertions.

2.1 The VOWELS paradigm

The VOWELS paradigm represents today’s areas of focus within MAS. This is interest-ing for this paper as we advocate a widening of today’s MAS focus to include the Useraspect. The VOWELS paradigm is a decomposition model that breaks down MAS intoits constitutive parts. The four basic bricks of the VOWELS paradigm as presented in[3] are the Agents, the Environment, the Interactions and the Organisations (A, E, I, O).According to [4] agents range from simple fine-grained automata to complex coarse-grained knowledge-based systems. The environments are usually spatialised but thereis no constraint about them. Interaction structures and languages range from physics-based interactions to speech acts. Organisations range from static to dynamic ones, andfollow any kind of structure from hierarchies to markets. VOWELS is then guided by a

declarative principle, which states that a MAS is composed of several agents, an envi-ronment, a set of possible interactions, and possibly at least one organisation:

MAS = A + E + I + O

2.2 The AgroBot project

AgroBot [5] is a consortium consisting of the Danish Institute of Agricultural Sciencesat Bygholm[6], the Maersk McKinney Moller Institute[7] and several corporate com-panies[8].

The need for the AgroBot project has occurred as today’s farming is an ongoing bal-ancing act between two opposing requirements: environmental and working conditionsones, and profitability and competitiveness ones. The focus has been so far to concen-trate on large scale agriculture with the help of larger machines and mass production inorder to maximise profitability and competitiveness. Seen from an environmental pointof view, this situation is untenable as the use of large scale machinery and undifferen-tiated mass production are harmful to the environment. A change from the current fo-cus on mass production to knowledge- intensive development and production is highlylikely to be the future for the agriculture. Therefore, the objective of the AgroBot projectis to investigate the possibilities of applying advanced robot and software systems to theagriculture domain in order to facilitate the change of paradigm.

The AgroBot project consists of several scenarios which we will refer to as AgroBotproblems. In this paper we will use these problems to illustrate how the locus of theproblem changes from ACMAS to OCMAS as the problems complexifies and eventu-ally changes into UCMAS.

3 Agent Centred MAS

This section introduces the original ideas of MAS, that were concentrated on the agentlevel and focusing on construction of an autonomous agent. This will be followed byan introduction to how most agent architectures are currently implemented in order toachieve means-end reasoning. Lastly, an AgroBot problem will be introduced to illus-trate such ACMAS issues .

3.1 Systems of Agents

MAS have been considered as societies of agents since their development in the 80’s,and from this conception it is clear that designers should be concerned with both agentsand societies [2]. In classical Multi-Agent Systems design however, an important em-phasis has been put on the agent side leading to the term Agent Centred Multi-AgentSystems (ACMAS). ACMAS is based on assumptions that lie in the core of the earlywork on MAS [2]:

– An agent may communicate with any other agent.– An agent provides a set of services, which are available to every other agent in the

system.

– It is the responsibility of each agent to constrain its accessibility from other agents.

These assumptions have their foundation in one underlying assumption, namely that anagent may communicate with any other agent. It is then the responsibility of each agentto constrain its accessibility from other agents.

Furthermore, Zambonelli and Parunak argue in [1] that the following characteristicsdescribe general MAS:

– Situatedness: Agents execute in the context of an environment, can influence it, andcan be influenced by it.

– Openness: MAS are subject to decentralised management and can dynamicallychange their structure.

– Locality in control: Agents represent autonomous and proactive loci of control.– Locality in interactions: Despite living in a fully connected world, agents interact

with each other accordingly to local (geographical or logical) patterns.

These characteristics from [2] and [1] have during the earlier years of MAS given birthto many discussions of how to design an agent architecture that ensured autonomousagent conduct and a reasonable behaviour of the system as a whole. The followingsubsection will elaborate on a particular agent architecture that has received a lot ofresearch and recognition.

3.2 Agent Architecture

Among the main requirements of an autonomous agent is the ability to perform means-end reasoning, i.e. the ability to select a course of actions that ultimately achieve thegoals of the agent. Thus, methods to accomplish such a requirement has long beena major research issue in Artificial Intelligence. This has led to many approaches topractical reasoning, where the most notorious is the Belief, Desire, Intention (BDI)agent model [9, 10]. Such an approach allows an agent to break down its goals so thatthe agent can commit itself to achieve only a subset of its entire goal universe.

By implementing a BDI architecture the agent achieves means-end reasoning, it isable to act autonomously and, if designed properly, it acts in order to serve the generalpurpose of the system. The following subsection will illustrate an agent centred problemthat needs means-end reasoning by the agents in order to achieve a sensible solution.

3.3 Decentralised Motion Planning

This problem originates from the AgroBot project where robots should coordinate theirmotion in the agricultural environment. When physical robots are moving around in anenvironment some sort of motion planner is necessary to generate a possible trajectorythat each robot can follow. Robots should move around the field from A to B withoutcolliding with static obstacles, and collisions between robots are unacceptable. Thisproblem is illustrated in figure 1.

Practically, a BDI agent architecture was applied in the resolution of this problemin the AgroBot project. The appropriate plans were selected by the agent kernel, whena potential collision was detected by the agent. This problem illustrates the class of

A2

A1collision

(a) Two agents are about to collide

A2

A1

(b) The two agents change their trajectoriesand avoid collision

Fig. 1. The motion planner

ACMAS problems, as it solely consists of the environment, the agent and their mutualinteractions. The next section will introduce the further development of MAS that areable to solve more complex problems than the ACMAS approach.

4 Organisation Centred MAS

This section deals with the organisation of agents into groups of mutual interest anddependencies. We will start off by elaborating on the term ”Organisation Centred Multi-Agent Systems” (OCMAS), followed by a concrete implementation model with whichto achieve a sensible organised MAS. Lastly, an AgroBot problem will be introducedthat represents the complexity of problems that are addressed by ”Organisation CentredMulti-Agent Systems”.

4.1 Organisation of agents

MAS has moved from the simple ACMAS into Organisation Centred Multi-Agent Sys-tems (OCMAS), where agent organisations provide a description of how the membersof the population interact with one another [11]. Agent organisations have properties,norms and authority relationship structures which transcend individual agents and helpthem coordinate their actions, regardless of whether the agents are collaborating toachieve a common goal, or acting as competitors. The set of properties, norms andstructures form the organisational setting, which contains lines of communication, linesof authority, rules and patterns of interaction and descriptions of expected behaviour[12]. In [13], Demazeau and Costa make a more formal description:

Organisation(MAS) = Roles + Links

that is, the organisation is a set consisting of all the roles that exist in the system, andall the links that connect the roles. So, roles are an inherent part of organisations, andare a way of describing behaviour, responsibility and interaction [14]. The followingconstraints are general in MAS when an agent takes on a role:

– Obligations and responsibilities: A role is inherently connected with certain duties,and a role, therefore, has responsibilities.

– Requirements and abilities: Not all agents can take on a role, they often requirephysical and/or logical abilities.

Roles become a good organisational modelling tool because they constitute generalproperties that agents should adhere to in order to play the role [15].

¿From the work represented by Masolo et al. in ¿[14] we adopt the following dy-namic properties of roles:

1. An agent can play different roles simultaneously.2. An agent can change a role.3. An agent can play the same role several times simultaneously, meaning that an

agent might be acting different specialisations of the same role at a time.4. A role can be played by different agents, simultaneously or at different times.

The above properties means that roles can be assigned to agents in at least two ways:endogenously (emergent self- organisation as the system runs) or exogenously (by thedesigner when the system is initialised) [16]. Note that an agent can easily play multipleroles, as long as they do not coincide in their responsibility area, which would result ina conflict and a dysfunctional organisation.

The following subsection will elaborate on how organisations can practically beobtained in a MAS.

4.2 Agent-Group-Role

Groups and roles were combined in [17], where Ferber and Gutknecht proposed anorganisational based modelling scheme, called AGR for Agent-Group-Role, to describeMulti-Agent Systems in organisational terms. Only agents that are members of the samegroup may communicate, but agents can be members of several groups simultaneously.Groups are commonly formed to regulate, foster, or support the interaction of thoseagents within the group [18]. Furthermore, groups can be formed to take advantages ofsynergies of their members, resulting in an entity that enables products and processesthat are not possible for any single individual. The next subsection will introduce aproblem from the AgroBot project that serves to illustrate how organisation of agentsconstitute means in order to achieve a solution to a complex problem.

4.3 Tool Changing

Tool Changing is a relevant problem in the agricultural environment, where a robotshould be able to take on a number of different abilities in order to be useful for theUser. An agricultural robot should be much like a tractor that is able to dynamicallychange its tool and adapt to new problem domains. If we decompose the result of a toolchange we get the following results:

– Change of obligations and responsibilities: a tool is a physical representation ofa certain capability. Inherently, an agent’s capability is connected to a number ofobligations and responsibilities which the agent should attend to with the certaincapability.

– Change of mutual dependencies: when changing tool an agent should also changeits surroundings expectations towards the agent and its own mutual dependencies.

The change of obligations and responsibilities can be modelled as a change of roleand the change of mutual dependencies, can be modelled as a change of group.

To illustrate this OCMAS approach, figure 2 concretely shows how a change of rolecan mimic the change of tool. The figure does not include a group change, since thedepicted scenario does not conceive anything about dependencies toward other agents.Whether a tool change should be modelled as a role change or as a role and groupchange is dependent of the activity, context and agent dependencies that the new toolimposes.

R1R2

(a) An agent has two roles R1 and R2, butonly R1 is active and, hence, only the blackboxes can be handled.

R1R2

(b) The same agent has now changed its ac-tive role from R1 to R2, which enables it topick up the other boxes instead.

Fig. 2. The effect of a role change.

This problem illustrates the complexity class of problems that OCMAS adress. Thenext section will, however, introduce a new approach of designing MAS that allows tosolve even more complex problems than the one just encountered here.

5 User Centred MAS

This section introduces the term ”User Centred Multi-Agent Systems”. We believe thisapproach is necessary in order to solve and model problem domains of higher complex-ity, than previous MAS approaches were capable of. We will start off by elaboratingon the term ”User Centred Multi-Agent Systems” and clarify why we believe this newapproach is necessary. The following subsection will further elaborate and justify theapproach by extending the existing VOWELS paradigm. Lastly, we will introduce aproblem from the AgroBot project in order to illustrate which complexity of problemsthat the approach is suitable for.

5.1 Users and agents

In the future of computing, scientists and engineers will have to design MAS to exe-cute in a world where an uncountable multitude of autonomous, embedded, and mobilesoftware components is already executing and interacting with each other and with theenvironment on the basis of local interaction patterns [1].

The move from computing in some hidden corner (where computers and our re-lationship to them are distinct and separate) to our current life being submerged bycomputers and computing power makes it necessary to include the human element incombination with the real-world practicalities and technological possibilities [19]. Onlyif we are able to firmly model this new emerging world can we effectively start design-ing and creating applications. According to [19] the world consists of a physical, aninformational and a conceptual dimension. As scientists, if we wish to properly modelthis world, we need to perceive these dimensions as interplaying spaces in the world ofpervasive computing as introduced in [20]. Appropriate MAS can only be constructedby considering at the same time these three spaces and their mutual dependencies. Sofar, the development of MAS concentrates on building organisations of agents that ex-ist and interact in their own conceptual world. Today’s definition of MAS lacks of arepresentation of human beings in the system model. Therefore, we strive to perceiveMAS as organisations of agents that should be fitted into a real-world context of ex-isting systems and physical environments with human and virtual (e.g. existing MASor other systems) Users. This induces numerous requirements to approach MAS with,striving for the integration of the User. Furthermore, we believe that future MAS willalso be designed as an extension or as a User of some existing systems (being it physi-cal, virtual or both). So, we foresee that it is necessary to broaden focus from how MASfunctions to include into the picture how MAS affect and perform with their Users.From now on we will refer to this approach as User Centred MAS (UCMAS). We willexemplify this approach by extending the VOWELS method and by elaborating on theUser Intervention problem in an agricultural context.

5.2 Extending VOWELS

As it is the case with most of today’s MAS methodologies, the VOWELS paradigmdoes not currently include the notion of a User. By extending the paradigm with U,the VOWELS explicitly models the User in any form. The User may be a human being,another MAS, one or more agents or any other system that has the privileges of a User. Itis necessary to extend the VOWELS paradigm as the existing AEIO blocks do not fullycover the role of the User. This is the case as the User, as opposed to equal agents andthe environment in the system, has got to have some direct influence and guidance of theinternals of the agent. This direct influence is set apart from normal agent interaction,by offering a direct altering of the agent’s beliefs and goals and the entire organisationof the MAS. Essentially, it becomes necessary that we include the User into MAS,because of safety measures and custom adaptability that Users now strongly require.As a matter of fact, some Users can be described as Agents or parts of the Environment,as these Users do not own privileges to perform direct intervention on the agent or the

organisation. It should, however, be emphasized that this paper is not addressing issuesof this kind.

With the addition of the User to the VOWELS paradigm is as follows:

MAS = A + E + I + O + U

The next subsection will elaborate on the extension the User’s privileges and why aUser can not be assimilated as, yet, another agent.

5.3 User Intervention

In the AgroBot project it is highly important that the User is able to guide and interactwith the operating agents in order to control and guide them without involving anyrisk of human or crop damage. This requirement is referred to as User Interventionand serves to show how the inclusion of the User aspect is necessary in a MAS thatfunctions in a real environment with human Users.

With robots interacting and executing in an agricultural environment User interven-tion is of vital importance, as the User will need to control and interact with the robots.For safety reasons and of mere interaction purposes the User should be able to changean agent’s beliefs, desires, intentions and capabilities, both via normal agent interactionsbut also due to other entry points to the agent, that should only exist for the User. Thisis illustrated in figure 3, where the applied agent architecture for the AgroBot project isshown. For simplicity we have replaced the representation of the internal Belief-Desire-Intention(BDI) architecture with a box called BDI. For a more elaborated explanationof how the agent architecture was specially implemented, see [21]. These additionalaccess points that the User apply in order to interact with the agent is also used in theTool Changing problem as introduced in subsection 4.3. In this case, the User is ableto force a tool change on the robot and, thereby, altering the organisation of the MASdynamically.

U

EI

Userinteract interact

O A

usesAGR BDIinteract interactAgents Environment

Fig. 3. The agent architecture with the addition of the User. The different vowels, includingthe additional U, are added here to localize them in this practical context.

Figure 4 shows the robotic testbed that enables virtual agents and physical robotsto interact, that were used in the AgroBot project. Figure 5 shows how the human Userhas an overview window so that the agents were monitored. Lastly, figure 6 shows

how the human User can intervene with each agent by double clicking on any agentin the overview window and actively force the agent to change role and, thereby, tool.Furthermore, the User is able to exchange plans between agents in order to control theirbehaviour.

Fig. 4. The testbed used in the AgroBot project that uses interacting virtual and physicalagents.

Fig. 5. Screenshot of the overview window used to monitor the agents. The numbers representagents, i.e. both virtual and physical robots.

Fig. 6. Screenshot of the User intervention possibilities in the AgroBot project.

6 Conclusion

In this paper we have argued the need for broadening the focus of MAS developmentto include the User aspect. This need occurs as we believe future Users of MAS will befully integrated in MAS. This itself advocates that the User perspective should be in-cluded in the MAS methodologies and approaches. In recent years MAS have evolvedfrom Agent Centred MAS (ACMAS) to Organisation Centred MAS (OCMAS), an evo-lution that is natural as MAS researchers have faced more complex and more challeng-ing tasks. However, very few researchers doubt that the future of computing will bepervasive. The mere use of the word ”pervasive” explicitly indicates the fact that com-puter systems ultimately are produced and developed for the human User. But the word”pervasive” does, however, also implicitly point to a future where an uncountable mul-titude of autonomous, embedded, and mobile computer systems are already executingand interacting with each other and with the environment on the basis of local interac-tion patterns. Therefore, we believe it is essential for MAS research to include the Useraspect in order to continue developing computer systems that are of future interest. Wehave designated this approach the ”User Centred Multi-Agent Systems” (UCMAS),and the need for this approach was exemplified by involving real problems from theconstruction of a MAS in the agricultural environment.

We have exemplified our theory by applying it to a future robotic system in theagricultural environment, but that application is barely a scratch in the surface of thewide range of applications that the User Centred Multi-Agent Systems approach aresuitable for. In this subsection we will mention a few candidates that serve as examplesof how widespread the applications could be:

– The Digital Hospital: The digital hospital is introduced in [22], where doctors andnurses use wireless laptop PCs, to pull up patients’ vital signs, notes, and lab testsfrom the hospital’s central system in order to find errors. Furthermore, the digitalhospital enables patients to monitor their care online, whereby they gain greatercontrol over their treatment.

– The Home of the Future: [23] envisions computer technology as ever-present inthe home of the future but in a subtle way. Information will be presented to people

at precisely the time and place they need it. Intille in [23] believes the pervasivetechnologies should empower people with information that helps them make deci-sions. Furthermore, Intille argues that pervasive systems should not strip people oftheir sense of control, which in [23] is claimed to be psychologically and physicallydebilitating.

It is finally important to notice, that the introduction of UCMAS does not take awaythe relevance of ACMAS and OCMAS. As can be seen in figure 3 the design of UCMASis a superstructure upon previous MAS approaches which have their foundation in bothACMAS and OCMAS. In other words, UCMAS is merely a small extension of MAS,but we believe it to be an essential one.

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