collaborative design approach to intelligent...

142 ACADIA05: Smart Architecture

J. Lee. Y.E. Kalay / Collaborative Design Approach to Intelligent Environments

Collaborative Design Approach to IntelligentEnvironments

Jaewook Lee1, Yehuda E. Kalay2

1 University of California, Berkeley2 University of California, Berkeley

Abstract

Intelligent environments are buildings and other settings that can recognize the changing needs of their usersand/or the changing nature of their context, and respond to them by adjusting some key environmentalparameters (temperature, light, sound, furnishings, etc.). Unlike the currently common approach, which isbased on systems theory (i.e., adjusting the parameters of the environment to match some pre-defined useprofile), the approach proposed in this paper is based on dynamic, collaborative design: it views the (built)environment as comprised of multiple independent object-agents, each of which is responsible for one smallaspect of the environment. Each can sense the immediate changes pertaining to its domain of responsibility,and propose corrective measures, which are negotiated with other agents to form a collective response. Thepaper hypothesizes that such an approach can be made more context-sensitive and dynamic, is easily scaleable,and can respond to the needs of multiple different users of the environment at the same time. The paperpresents the rationale for developing the multi-agent approach, its hypothetical implementation, and itsapplication to hypothetical case studies.

ACADIA05: Smart Architecture 143


Introduction

Once man-made environments, such as buildings,automobiles, and urban places are built, they areoften ‘frozen’ in one or a few interchangeableconfigurations, intended to support a singleactivity or environmental condition, or a numberof closely related activities/conditions. When theactivities or the conditions do not match those forwhich the environment was designed, adjustmentsare needed. They can take the form of opening orclosing a window, turning on the light, re-arranging the furniture, or remodeling the building.Each adjustment requires conscious action by theoccupant(s). ‘Intelligent environments,’ on theother hand, can actively support diverse humanactivities and different environmental conditionsby automatically and dynamically adjusting theirconfiguration to meet the changing needs of theiroccupants without explicit human intervention.

The idea of making buildings “intelligent” hasbeen first suggested by Nicholas Negroponte, inhis influential book “Soft Architecture Machines”(1975). Such buildings will be able to adjust theirenvironmental and configurational settings tomatch the occupants’ needs and activities withoutexplicitly being ‘told’ to do so through manualenvironmental controls. As such, they will be ableto perform optimally for a wide range of activitiesand needs, rather than an “average” daily activityor need set at the time of their design. Negropontewent as a far as stating that such intelligentenvironments will obviate the need for architects:they will “redesign” themselves whenever the needarises.

Attempts to make buildings more intelligent haveproliferated since the advent of affordable,ubiquitous computing devices (Intel Research,2005). These attempts, which focus primarily onthe technical aspects of buildings and informationsystems, are typically guided by a systemsapproach, whereby the building adjusts itsparameters to conform to some pre-definedschema (e.g., a given temperature, lighting, oranother parameter). This approach, which is basedon traditional cybernetic principles, focuses on thephysical environment itself, rather than on thedynamic interrelationship between humanactivities and the environments in which theyoccur. Little attention is paid to how anenvironment (e.g., a room, a building, etc.) shouldbehave as users and their activities change; which

possible conflicts may arise between the needs andactivities of multiple different occupants of thesame space at the same time; and how theseconflicts can be resolved.

This paper describes research that attempts toanswer some of these questions, by providing aframework for developing intelligentenvironments using a collaborative designapproach, rather than the systems approach. Theapproach proposed by this research is derived fromthe observation that adjusting the environmentaland physical parameters of a building after it hasbeen built can be compared to the process ofdesigning the building in the first place (before itis built). During the design process, the individualparticipants (architect, engineers, client, etc.) of adesign team interact with one another in ways thateventually lead to a joint solution, influenced bythe goals, contributions, and constraints of eachparticipant. Modern design (and organizational)theories (Benne, 2004; Kalay, 2004) claim that thisnegotiated process is not based on a top-downsystems approach, as cyberneticists would have it(“the building is a machine,” according to LeCorbusier (1927)), but rather on methods ofcollaborative decision-making that respects theneeds and wishes of each participant. Suchdynamic, negotiated, and collaborative adjustmentof building parameters typically ends when thebuilding is constructed, and it is “locked” into oneor a few fixed states. Thereafter, a system-based,mechanistic method is used to monitor and controladjustable building parameters (e.g., light, energy,security, elevators, etc.): the “system” is adjustedto conform to some pre-conceived schema.

The approach proposed in this paper, on the otherhand, wishes to extend the dynamic adjustmentof building parameters after it has been built, usingcontinual negotiation rather than a pre-conceivedschema. One can argue that such a mechanism isalready in place, in the form of the on-goingactions taken by the building occupants (turningon lights, opening windows, re-arranging thefurniture, remodeling the building, etc.). Thisresearch proposes to replace the actions of thehuman participants with intelligent software agentsthat will extend the on-going negotiation andcollaborative decision making that characterizesthe design phase of buildings, thereby off-loadingthese decisions and actions from the humanoccupants.



The research discussed in this paper focuses onthe processes that control intelligent environments,rather than the physical devices themselves thatsense and respond to the required changes. Thedesign of the sensors and actuators has beenresearched by others (Coen, 1998; Holmes et al,2002; Krogh et al, 2001; Streitz et al, 1998).

Conventional Approaches to IntelligentEnvironments

After Negroponte’s first experiments in the 1970swith the adaptation of computer technologies tothe building industry, many attempts were madeto develop intelligent environments. Two majorapproaches emerged: equipping the environmentwith devices that can help users do their job moreeffectively (e.g., process information, etc.), andapproaches that adjust the environment’s ownqualities (e.g., light, temperature, etc.). The firstapproach is known as the Information Systemsapproach, and the second as Building Operations/Environmental Controls approach.

According to the Information Systems approach,an environment is considered a means (a machine)for processing information. Most such systemshave been based on ubiquitous computingtechnology that inserts programmable microchipsinto building components and appliances tomonitor and control them through networkedcommunication (Weiser, 1993). The primary goalof this approach is to make computers invisibleand become part of the environment, mostly atroom-scale. For example, in the i-Land project(Streitz et al, 1998), a set of roomwarecomponents, such as “DynaWall” (an interactiveelectronic wall), “CommChairs” (mobile andnetworked chairs), and “InteracTable” (aninteractive table), creates a collaborative workenvironment that supports office workers and theiractivities. In the case of the Intelligent Room(Coen, 1998), computer vision and speechrecognition systems with built-in ArtificialIntelligence are used to minimize the number ofembedded devices and to identify ordinary humanactivities, creating natural human-computerinteractions. Computer devices in this kind ofintelligent environments are mainly used forcontrolling other connected devices to access, storeand display information similar to conventionalpersonal computers. Thus, the environment itselfis treated as a container, a backdrop for users’information-processing activities.

The Building Operation/Environmental Controlapproach, on the other hand, places more emphasison indoor environmental quality (e.g.,temperature, illumination, etc.), energy saving, andbuilding operation. Such building automationshares the concept of ubiquitous computing withthe approach described above, but aims atmaximizing operational efficiency and thermalcomfort by various embedded sensors andcontrollers, rather than the effectiveness of theusers themselves. The iDorm project (Holmes etal, 2002) uses various networked devices thatmonitor the current state of a dorm room and adjustenvironmental conditions to meet user’spreferences. A set of distributed sensors collectsvarious data about the room’s condition and sendsthem to the iDorm main control unit. The systemadjusts the connected effectors (e.g., heater, cooler,door lock, blind, window, etc.) based on thereceived data. Similarly, the ACHE (AdaptiveControl of Home Environment) project (Mozer,1998) is a house equipped with an intelligentcontrol system. It uses neural network-basedpredictors that control basic residential comfortsystems including lighting, air heating, waterheating, and ventilation. The system can learn theusers’ preferences conveyed through their manualadjustment of lights or thermostats and strives tobalance their comfort with energy savings.

Two fundamental drawbacks of the conventionalapproaches to developing intelligent environmentsare the absence of an overall methodology forenvironment-wide behavior control, and the lackof attention to the environmental impacts onhuman behavior.

In a broad sense, a setting modification by anintelligent environment can be viewed as a designactivity that transforms a present situation into adesirable one (Kalay, 2004; Rittel, 1973; Simon,1984): a (human) designer, in the course of solvinga design problem, identifies a design problem andthe goals a design solution should achieve,generates possible solutions by gathering relevant(external) information and using her/his (internal)knowledge-base (e.g., past experiences, reasoningrules, etc.), and evaluates the candidate solutionsby testing and verifying them compared to thegoals and constraints.

This design-oriented view can provide thetheoretical foundations for the development ofintelligent environments. The task an intelligent



environment deals with at a given point in timecan be considered a dynamic design problem. Theintelligent environment perceives users’ activitiesand the current state of the environment (problemidentification), determines the goal state andconstraints that reflects users’ needs (goalformulation), finds potential environmentalsettings that can achieve the goal state and abideby the constraints (solution synthesis), andevaluate the settings and select the best-fitting oneamong them (evaluation).

The conventional approaches to intelligentenvironments that were discussed above havemore or less disregarded such design-orientedview. Thus, design-based models andmethodologies for intelligent environments havenot yet been proposed. As a result, a designsolution (i.e., setting modification) generated byan intelligent environment, based on assumptionsmade at an earlier time, may conflict with theactual needs of the user(s), and therefore mightbe overruled (by manually adjusting suchparameters as windows, lights, and thermostats),or lead to dissatisfaction of the users (Arens et al,1997).

Hence, although intelligent environments havebeen developed to improve human-environmentinteraction in one way or another by utilizingcomputing technologies, they remain largely staticand passive entities. They have paid virtually noattention to the environmental impacts on humanbehavior, especially of the differing needs ofmultiple simultaneous users of the sameenvironment. The drawbacks of conventionalapproaches can be summarized as follow:

A Collaborative Design Approach to IntelligentEnvironments

Most design problems in the real world are toocomplex to be solved by a single designer. Rather,design is a collaborative activity, involvingmultiple specialist participants. Since eachparticipant has limited knowledge and abilities,the design project can only be accomplished bythe combined efforts of the participants who haveparticular tasks to complete based on theirspecialty. In other words, while each individualparticipant maintains autonomy to deal with her/his individual tasks, coordination (e.g., taskallocation, scheduling, conflict resolution, etc.) isrequired to accomplish shared organizationalgoals. Therefore, collaboration can be defined asthe “agreement among specialists to share theirabilities in a particular process, to achieve thelarger objectives of the project as a whole” (Hobbs,1996).

We posit that an intelligent environment can bebuilt with compositional objects (e.g., walls, doors,windows, furniture, lights, etc.) as an ensembleof autonomous intelligent objects, each of whichknows how to interact with context-specific useractivities. Thus, an intelligent environment can beviewed as a team-like organization of multipleindependent agents. Moreover, a settingmodification performed by the intelligentenvironment can be viewed as a dynamiccollaborative design activity, in which designproblems are distributed to multiple participants(i.e., intelligent objects) and solutions aresynthesized through collaboration and negotiationamong them. This approach can overcome thelimits of the knowledge possessed by individualintelligent agents, while the division of labormakes the system overall simpler and moreresponsive to unexpected needs of the users. Sinceeach intelligent object comprising the environmentis functionally, spatially, temporally, andinformationally bounded (i.e., has limitedknowledge and effect), a shared organizationalgoal (i.e., environment-wide modification) canonly be accomplished by combining modificationsapplied to many individual objects. The divisionof labor and individual autonomy, on the otherhand, can improve the efficiency of the overallperformance of the intelligent environment byreducing the cost of information processing.

· Lack of design-oriented view and theo-retical models for controlling the envi-ronment.

· Prone to conflicts between users and en-vironments.

· Less attention to the effects of spatialquality of the built environment on hu-man behavior.

· No ability to deal with differing, evenconflicting needs of multiple simulta-neous users.



In general, when the uncertainty of the taskincreases, the amount of information that needsto be processed among decision-makers tends toincrease. In the case of an organization builtaccording to a mechanistic model, where tasks arecentrally preplanned and executed with stronghierarchical authority, the increased uncertaintyof the task may reduce the degree of organizationalperformance due to the limits of capacity inhandling exceptions and processing information(Galbraith, 1977) — a phenomenon that was well-demonstrated by the I-CAAD system (Pohl et al,1994). In order for the organization to cope withgrowing uncertainty, the organization needs toeither increase its capacity of informationprocessing or decrease amount of informationpassed among the members to accomplish the task.A team-type organization is a case in point:individual members have more or less self-contained tasks. Hence, the amount of informationtransmitted between members is reduced,compared to a hierarchically structuredorganization with rigid rules.

Although collaboration can overcome individuallimits to accomplish shared goals, it might alsoinduce inter-personal conflicts due to differencesin perspectives, goals, or knowledge among theparticipants. In a collaborative design processwhere each participant has her/his own tasks todo, satisfying the goals of one individual mayinterfere with satisfying the goals of anotherindividual (Kalay, 2004), and this may preventsatisfying the overall organizational goals.Similarly, when objects are constructed asintelligent entities that know how to behaveaccording to built-in reasoning capabilities, eachone of them may perceive the same situationdifferently from other intelligent objects, due totheir spatial and temporal boundedness. This maylead to behavior conflicts between objects. In anintelligent environment in which multiple objects,users, and activities interact with one another,conflicts between various entities need to beefficiently resolved to properly modify the settingof the environment as a whole, much like partialdesign solutions proposed by the multipleparticipants are tested and verified for consistencyand ability to meet the goals and abide by theconstraints. When approaching an intelligentenvironment as an organization of multipleintelligent objects, coordination andcommunication among the objects are majorconcerns.

Implementation with a Multi-Agent System

Multi-Agent Systems

Considering the difficulties in constructingintelligent environments and the drawbacks of theconventional approaches, a method is needed thatcan be responsive to users and user activities, andthat can efficiently handle the conflicts that arisein the course of its solution generation. We havefound that a multi-agent approach can be apromising method for developing intelligentenvironments.

An agent can be defined as any entity that canperceive its environment through sensors and acton that environment based on its own reasoningcapability (e.g., a human agent, a robotic agent, asoftware agent, etc.) (Russell et al, 2003). Thus,autonomy, interactability, and adaptability are theessential attributes that an agent should have. Thisconcept of autonomous agent has been a coreresearch subject in Artificial Intelligence andwidely used in many industries including robotics,process control systems, email clients, and searchengines, for the purpose of developing intelligentapplications.

Agent-based computing has the potential forconceptualizing, designing, and implementingcomplex multi-user distributed systems (Jennings,1999). In general, agents can be built in anyimaginable environment. Their behaviors arestrongly dependant on the nature of a taskenvironment. In theory, for any task environment,either single or multi-agent systems (MAS) arepossible. A single-agent may work well when atask environment is simple, small, and/or static,whereas multi-agent systems are more appropriatefor complex, large, and/or dynamic environments(Russell et al, 2003). The power of MAS lies inthe division of labor and the cooperation of agentslike human organizations. Whether organizational,physical, or computational, the most basictechnique for tackling any large and/or complexproblem is to “divide it into smaller, moremanageable chunks” (e.g., individuals, mechanicalcomponents, software modules, etc.) (Booch,1994). As such, multiple agents can represent thedecentralized nature of the problem, multiple lociof control, multiple perspectives, or competinginterests. Within an agent organization, the agentsneed to interact and negotiate with one another to



achieve their individual as well as common goals(Jennings, 1999).

Such MAS-based approach has recently beenapplied to building intelligent environments. Inthese environments, multiple computational agentscommunicate with one another to control variousbuilding components. A system developed byXerox PARC (Huberman et al, 1995) utilized amarket-based MAS for managing the thermalconditions of an office building. In this system,individual temperature controllers (i.e., multipleagents) of the offices bid to ‘buy’ or ‘sell’ cool orwarm air by participating in an auction that ismoderated by a central computer auctioneer, aspecialized agent. Similar MAS approaches arefound in other applications for the developmentof intelligent environments (Boman et al, 1998;Coen, 1998; Colley et al, 2001). The primaryconcern of these MAS-based intelligentenvironments has been the interaction of the agentsthemselves and, as a result, the aspects of humanbehavior and their relationship to the builtenvironment have largely been neglected.

In our proposed model of intelligent environment,each building component is represented as anintelligent or smart agent that knows how tobehave given any activity of any user, and has theability to perceive contextual changes of theenvironment and adjust its behavior in accordancewith its immediate context to better support users’context-specific activities. A layered multi-agentapproach can efficiently endow an environmentwith intelligence by organizing agents andcontrolling their behaviors. Built-in conflictresolution mechanisms will minimize conflicts andensure environment-wide behavioral consistency.

Object, User, and Activity Profiles

The first step in designing an intelligentenvironment is to embed processors andmechanisms within objects to allow them to senseand respond to user activities. For example, thedoor of a room can open itself when a userapproaches. This behavior must be programmedinto the object (i.e., when and how to respond).Such object behavior description can be stored inthe form of a profile, Object Profile. In additionto an object profile, each object requires a controlinterface that actually generates actions based onexternal stimuli and the object’s profile. Thus, withan object profile and a control interface, every

object can be made to ‘know’ when and how toinvoke its behavior. Such programmed objects canbe viewed as intelligent (object) agents.

In a multi-user environment (e.g., an office), eachuser has her/his own preferences forenvironmental settings. These preferences can alsobe programmed and collected in a User Profile,which stores user preference including user ID,object IDs, property variables, and their values(e.g., the kind of music she likes, the lighting level,heat/humidity levels, height of the chair/desk, etc.).A user profile can be encoded in a card key or abadge that can be read by objects through wirelesscommunication (e.g., Bluetooth, RFID, etc.).Similar approaches have been used in designingintelligent environments (Sharples et al, 1999;Colley et al, 2001). Objects will need a mechanismthat can identify the user’s profile, and combine itwith their ability to sense the environment toinvoke the appropriate action.

Figure 1. User Profile + Activity Profiles + Object Profiles= Setting Modification

In addition to object profiles and user profiles, athird kind of a profile, Activity Profile thatdescribes the activity of the users is needed,because the same users may perform differentactivities at different times and have differentsetting preferences for their activities. With object,user, and activity profiles, the environment canidentify users and their activities, and thus modifythe settings of the environment accordingly(Figure. 1).

Design Activity of Object Agents

Problem Identification and Goal Formulation

The design activity of individual object agents ofintelligent environments is initiated by perceivinga user or users. When an object agent detects auser in the environment, the agent retrieves a part



of the user’s profiles (i.e., user and activity profile),which is related to the object that the agentcontrols, and loads it in the memory of the agent.Each user profile contains a set of requirementsthat describe her/his preferred configurations ofthe environment. User requirements may includefunctional (on/off, open/close, etc.), spatial(position, size, etc.), environmental (color,temperature, lighting, etc.), or informational(music style, etc.) properties of objects themselvesas well as interrelationship(s) with other object(s)(in-between distance, etc.). Each property (orvariable) of an object has a value or a range ofvalues that a particular user prefers. Once it isprocessed, it becomes a part of knowledge-basefor the setting modification of the object agent.An object agent formulates a set of goal states andtheir constraints by processing the user and activityprofile of the detected user.

Since the initial problem space of each object agentcontains all the possible states (i.e., all theproperties and their possible values) of the object,the process of goal formulation can significantlyreduce the search space of the object agent (i.e.,individual users tend to have a limited number ofactivities and preferred environmental conditions).For example, when the object agent of ‘Chair-A’detects ‘User-A,’ it loads the portion of Chair-Afrom the user and activity profile of User-A on itsworking memory. The loaded profile contains aset of properties and their values (or ranges ofvalues) that represent User-A’s preferred state(s)(i.e., the goal state(s)) of Chair-A. It may alsoinclude relationship(s) with other object(s) (e.g.,distance between the chair and a desk, etc.). Whenthe agent perceives an activity of the user, thesearch process for a setting the activity will followthereafter.

Solution Search and Synthesis

The design method that object agents use for theirsolution search and generation is means-endsanalysis. In the above example, when the user(User-A) initiates an action on the chair (Chair-A), the first step for the setting modification ofthe object agent is to identify the type of the user’scurrent activity. If the user’s current activity is‘Office Work,’ the agent looks up the activityprofile loaded on the working memory andretrieves the goal state, the user’s desiredconfiguration (ends) of the chair for the given useractivity. This process further reduces the problem

space of the object agent into the goal space. Thenext step is to determine the solution state withinthe goal space and select an action or a set ofactions (means) from the object profile, which cantransform the current state of the chair to thesolution state (e.g., change the height of the chairfrom ‘x’ to ‘y,’ etc.). Before an actual modification,the chair agent needs to test and verify the selectedaction(s), which is the next phase of the designactivity.

Evaluation and Confirmation

The evaluation phase of the object agents is notmuch different from that of human designers. Thesolution state sought from the previous phase,which is represented as a set of actions, isevaluated by the object agent.

In intelligent environments, the design decisionof the object agent should be made within thecurrent context of the environment. In other words,the object agent has to test the fitness of theproposed solution to the goal state in relation toother objects in the same environment beforemaking any modification to the object. This isbecause the objectives of the individual objectagents are not totally independent of each other.When they are combined to modify the setting ofthe intelligent environment as a whole, designdecisions of an object agent to achieve its owngoal may interfere with the goal of another objectagent. Again in the previous example, the positionand height of the chair object may be constrainedby those of a desk object in the same environment,which may lead to a conflicting situation betweenthe two. Thus, the object agents must verify (orcheck) the side-effects of their actions, beforeconfirming them. If any conflict happens, anappropriate modification should be made toresolve the conflict.

Uncertainty of the Design Problem

The ill-structuredness of design problems (Simon,1984; Rowe, 1987) can also be found in the taskwith which the object agent deals in intelligentenvironments. As ill-structuredness comes fromthe uncertain nature of a design problem, in anintelligent environment, uncertainty is mostlyinduced by the dynamic nature of users and theiractivities. To cope with this uncertainty, the objectagent requires more capabilities, in addition to theproblem-solving skills.



First, since users and user activities are not staticelements (i.e., a new user can enter or a newactivity can be initiated), the goal state (i.e., theneeds of particular users and activities) that theintelligent environment needs to satisfy is notfixed. In other words, the intelligent environmentmust continuously gather information about thepresent state of the environment for its settingmodification by monitoring users and useractivities. Second, such newly acquiredinformation (e.g., new user and activity, etc.) maykeep modifying the goals and constraints, whichin turn may require changes to the solution (i.e.,changes of the setting) (Figure. 2), much like ahuman designer keeps updating design solutionsin relation to the goals and constraints that s/hediscovers through the design process. Thus eachobject agent has to be able to update its knowledgeand behavior accordingly as the state of theenvironment changes. Third, different users mayhave different needs, which require differentenvironmental settings. These may conflict withthe settings needed by other users of the sameenvironment, at the same time, thus requiringnegotiation and compromise between differentobject agents. In order to handle this uncertainty,collaboration and conflict resolution mechanismsshould be established in the intelligentenvironment for environment-wide modificationand its consistency.

Figure 2. Design Process of an Object Agent

Design Collaboration of Agents:Communication and Knowledge Exchange

Knowledge sharing through communicationchannels is one of the fundamentals of multi-agentsystems. As computational systems become morecomplex, dynamic, and larger, the traditionalArtificial Intelligence approach with a single locusof internal reasoning and control has shownlimitations in building computational intelligence.Hence, as an alternative, the multi-agent systemsapproach with distributed reasoning and controlhas been widely applied. Because of the nature ofmulti-agent systems in which multiple autonomous

agents exist as a form of an organization or asociety, social interaction through communicationnetworks is inevitable for agents to achieve theirown goals and the goals of their organization orsociety. Therefore, agents do not need to knoweverything, rather rely on other agents to knowthings required for their action generation(Jennings, 1999). Communication protocols in anorganization of agents enable agents to exchangeand understand messages and to coordinate theirbehavior, resulting in systems that are morecoherent.

An intelligent environment is composed of a groupof objects of which each has particular role andbehavior controlled by an embedded agent.Although each object agent can generates its actionby monitoring the environment, information thatis required for the successful action generationmay not always be available to the agent. This isbecause each object agent may perceive only apart of the environment. For example, a lightingdevice in an office may require information aboutthe current state of user activity from otherobject(s) that the user activity is directly associatedwith (e.g., chair). Moreover, some agent actionsmay also be constrained by the state(s) or action(s)of other agent(s) (e.g., height constraint betweena table and a chair: IF the height of Chair-A is ‘a,’THEN the height of Table-A is ‘b’). Thus acommunication network that containscommunication channels and protocols betweenobject agents has to be provided for object agentsto efficiently collaborate with other object agents.

Languages that are included in the communicationprotocols are major means of communicationbetween object agents. They are used for encodingand decoding information to be transmitted fromone object agent to other agent(s). In the proposedmodel of intelligent environments, the set ofprofiles can also be regarded as a standardizedlanguage that facilitates agent collaboration. Forexample, when a user sits on a chair to switch fromwork to rest , the change of user activity (i.e., from‘Work’ to ‘Rest’) detected by the chair agent willcause the behavior modification of the chair agent.Thereafter, this change of user activity, as amessage, will be transmitted to the lighting agentthrough the communication channel, which in turnwill modify the setting of the lighting object (e.g.,change illumination). This communication processis described in Figure 3.



Conflict Resolution of Intelligent Environments

An environment is a combination of actors, theiractivities, and the physical (or object) settings,which interact in various ways. In our model ofintelligent environments, these interactions can berepresented as interactions among the respectiveprofiles (Figure. 4). Although it is possible toobserve six different types of conflict in theenvironment, in this paper we will discuss onlythe three most prominent types of conflict: ‘ObjectProfile Conflict,’ ‘User Profile Conflict,’ ‘ActivityProfile conflict,’ and their resolving mechanisms.

Object Profile Conflict

This type of conflict generally results fromperception or goal difference between objectagents (OAs). An object agent (OA), as a spatio-temporally and rationally bounded entity, can onlyperceive a (small) part of the environment, aboutwhich the agent thus has subjective knowledge aswell as limited reasoning capacity. As a result,different OAs may interpret a same user activitydifferently. Furthermore, each OA has its own goalwhich may be different from that of other OAs.These perception and goal difference are a majorsource of Object (Profile) Conflict. In order toresolve conflicts between OAs, another type ofagent, the Behavior Manager Agent (BMA) —which is similar to a human coordinator in ateamwork — has been introduced. The main taskof a BMA is managing, mediating, andcoordinating the behavior of the OAs assigned toit, by resolving conflicts among them.

User Profile and Activity Profile Conflicts

When two or more users are in a same zone (orroom) at a same time, there might be differencesin their preference over the settings of the zone(or room), which may result in a User ProfileConflict in an intelligent environment. Similarly,whereas a single user normally performs a singleactivity for a certain time period, two or more users

Figure 3. Topology of Agent Communication

Figure 4. Three System Profiles and Types of Conflict



may perform different activities in a same zone(or room) at a same time, which may lead to anActivity Profile Conflict. For instance, in an office,the lighting and sound preference for the restingactivity of one user may be conflicted with thepreference for the working activity of another user.Theses types of conflict arise due to the goaldifference between users and may not be easilypredicted in advance. Although these conflicts maysimply be resolved by referring them to the usersinvolved, this may be burdensome for the users.Therefore, an effort to resolve user and activityconflict should be made by the agents of anintelligent environment.

Layered-Agent Structure

In order to efficiently resolve possible conflictsand maintain the consistency of environment-widemodification, we introduce a layered-agentstructure. Briefly, the primary task of upper levelagents is to coordinate the behavior of lower levelagents and resolve conflicts arise between lowerlevel agents. The layered structure includes onlythree levels of agents, arranged in a hierarchicalstructure (Figure. 5). Conflicts are resolved byreferring them to the agent next level up. Thefunction of each level of agents can be describedas follow:

OA (Object Agent): A set of OAs control thebehaviors of objects in a zone (or room) of theenvironment. When a user forwards an action toan object, the OA of the object identifies the actionand responds to it based on the behavior criteriaof the object, which are described in its objectprofile, in conjunction with the user and useractivity profile. The OA passes the data of itscurrent state to its BMA, which is a higher levelagent as well as an intermediate agent whichsummarizes the current state of the assigned zoneand uses it to coordinate the behavior of the lowerlevel OAs. OAs are the lowest level agents thatdirectly manipulate the environment.

BMA (Behavior Management Agent): Each BMAcontrols the behavior of an assigned zone (e.g., aroom, a lobby, etc.) that includes a number of OAs.A BMA keeps track of the states of users, useractivities, and objects within a zone that is underits coordination. A BMA summarizes users anduser activities at a given point of time based onthe state data received from the OAs associatedwith the current user activities. Thus, a BMA can

coordinate the behavior of its OAs based on thedata about users, user activities, and objects in itsassigned zone. By doing so, it can handle conflictsbetween OAs (i.e., object-level conflicts).

EMA (Environment Monitoring Agent): Theprimary role of the EMA is to control the behaviorof a whole environment by monitoring the contextof each zone. The EMA can identify the contextof each zone through the data received from anassociated BMA (i.e., current state of a zone).Based on the context of the zone and its relationto the contexts of other zones, the EMAcoordinates the behavior of BMAs, when themodification may further change setting(s) of otherzone(s). Thus it can deal with zone-level conflicts(i.e., conflicts between BMAs). The EMA is thetop level agent that controls the overall behaviorof the environment.

In general, organizations are expected to improvetheir performance if their organizational structurematches their task structure (i.e., structuralalignment) (Carley, 1999). In this respect, thelayered-agent structure in the proposed model ofan intelligent environment is well aligned with thestructure of the task environment, which ishierarchical in its composition (i.e., Building Level– Zone Level – Object Level) (Figure 6).

Conflict Resolution

Resolution of Object Profile Conflict

To accommodate changes in user activities (e.g.,switching from work to rest), objects must detectthe change, retrieve the stored data from theactivity profile, and apply it in a timely fashion.However, the user’s change of activity is normallyonly evident through one or more objects that areassociated with that activity. Other, non-associatedobjects, may not know what the current useractivity is, nor that it is different from an earlieractivity, because each agent is physically andrationally bounded and can only perceive somepart of the environment.

Thus, as discussed in the earlier section, in orderto overcome such limitations of individual agents,communication channels between agents arerequired. Through communication channels withother object agents (OAs), an OA that is notdirectly associated with a user’s activity can stillbe informed about it. For example, when a user



starts her/his break, the change of activity may betriggered by the chair on which s/he sits (leaningback vs. sitting straight up). The chair could thencommunicate this activity through communicationchannels to other agents, which will change theirsettings for the ‘rest’ activity of the user. But duringthe break, the user may grasp a magazine that s/he bought that morning and start reading it. Atthis point, the “table” (agent) on which s/he putsthe magazine will detect this activity and reasonthat the user stopped her/his break and returnedto work. It will inform other objects of this changeof activity, and they will change their settings fromthe ‘rest’ profile to the ‘work’ profile. However,the “chair” agent may still think that the user isresting. This means that the activity detected bythe table does not match the activity detected bythe chair, generating a conflict between OAs.

Resolution of an Object Profile Conflict can bedone by a Behavior Management Agent (BMA)thatdetermines the user’s current activity bysummarizing the current state of the room basedon information gathered from OAs (e.g., whichobjects are currently involved in which useractivity, the user’s location in the room, etc.) andother resources (e.g., time of day, user’s previousactivity patterns, etc.). Thus, a BMA can beconsidered a coordinator who acts at a level abovethe object agents.

Resolution of User Profile and Activity ProfileConflict

When there are two or more users at the same timein the same environment, two or more differentuser profiles are active simultaneously, which may

Figure 5. Agent Structure and Communication of Intelligent Environment

Figure 6. Structural Alignment of an Intelligent Environment



lead to a User Profile or Activity Profile Conflict,because one user’s preferences for the officesetting may be different from another user’spreferences (e.g., difference of music style,lighting illumination level, temperature, etc.). Thistype of conflict may simply be resolved byreferring it to the users. However, this puts theburden of controlling the setting of an environmenton the human actor, which is precisely what anintelligent environment is intended to avoid.Hence, an attempt to resolve a user or activityprofile conflict should be made by the agentsthemselves. When such a profile conflict isdetected, a mechanism of conflict resolution isinvoked within the BMA at the zone level. In short,the BMA may select one of the two profiles,combine the two, or create new profile as analternative (Figure 7). As the result of this process,a Group Profile is generated for a group of users.

Summary and Conclusions

Current approaches to developing intelligentenvironments are based on the systems approach,where the entire environment is considered a‘machine’ whose output (i.e., the environmentalsetting) must be adjusted to conform to some pre-defined schema. We contend that such amechanistic approach is inadequate, because itfails to recognize the changing behaviors andactivities of the users, and fails to respond toconflicting needs of multiple users. A more flexibleapproach, based on design collaboration, isproposed. It comprises of a hierarchical structureof largely independent agents, which cancommunicate with one another and resolveconflicts much like a (human) team-typeorganization does. The fundamentals of theproposed model can be summarized as follows:

Figure 7. Conflict Resolution: User-Activity Profile Conflict

1. The model is built with a hierarchicalorganization of multiple agents that generateactions through message transmission.2. The agents have both individual goals andshared organizational goals. The individual goalsare typically action-oriented (e.g., lighting agents- keeping illumination level according to useractivity), whereas the organizational goals aremore abstract (e.g., zone-level modification) andcan only be accomplished by the combined resultof agents interaction through the communicationnetwork.3. Bi-directional interaction between layeredagents about a task environment and generatingactions on the environment, augmented by acoordination and conflict-resolving mechanisms,completes the organization of the system.

The propose approach also provides a muchneeded overall organizational methodology todeveloping intelligent environments. It is easilyscalable, and is expected to be more responsive toits occupants needs than mechanistic approaches.



References

Arens, E., Xu, T., Miura, K., Zhang, H., Fountain,M., and Bauman, F. (1997). A Study of OccupantCooling by Personally Contolled Air Movement.Energy and Buildings, Vol. 27, No. 1, 45-59.

Benne, B. (2004). Techno-organizational modelsto support construction industry work processes.Ph. D. Dissertation, University of California,Berkeley.

Boman, M., Davidsson, P., Skarmeas, N., Clark,K., and Gustavsson, R. (1998). Energy Saving andAdded Customer Value in Intelligent Buildings. InProc. PAAM98 (Nawana and Ndumu eds.), pp.505-517.

Booch, G. (1994). Object Oriented Analysis andDesign with Applications. Benjamin/CummingsPublishing Company, Inc.

Boulding, K. E. (1962). Conflict and defense. NewYork: Harper.

Carley, K. M. and Gasser, L. (1999).Computational Organization Theory in MultiagentSystems. In A Modern Approach to DistributedArtificial Intelligence (Weiss, G. eds.), Cambridge,MA: MIT Press.

Coen, M. H. (1998). A prototype intelligentenvironment. In Proc. 1st International Workshopon Cooperative Buildings (CoBuild 98).

Colley, M., Clarke, G., Hagras, H., Callaghan, V.,and Pounds-Cornish, A. (2001). Intelligentinhabited environments: Cooperative robotics &buildings. In Proc. ISR 2001, Seoul, Korea.

Corkill, D. D. L. and Susan, E. (1999). Diversityin agent organizations. Object Magazine, 8, 41-47.

Galbraith, J. R. (1977). Organization design.Reading, MA: Addison-Wesley.

Hobbs, R.W. (1996). Leadership throughcollaboration. AIArchitect Vol.3, pp. 11.

Holmes, A., Duman, H., and Pounds-Cornish, A.(2002). The idorm: Gateway to heterogeneousnetworking environments. In Proc. InternationalITEA Workshop on Virtual Home Environments.Huberman, B. A. and Clearwater, S. (1995). Amulti-agent system for controlling building Schön,A. D. (1983). The reflective practioner: Howprofessionals think in action. New York: BasicBooks, Inc.

Intel Research (2005). http://www.intel-research.net/berkeley/Seminars.ASP?Archive=1

Jennings, N. R. (1999). Agent-oriented softwareengineering. In Proc. 12th Int. Conf. on Industrialand Engineering Applications of AI, Cairo, Egypt.

Kalay, Y. E. (2004). Architecture’s new media:Principles, theories, and methods of computer-aided design. Cambridge, MA: MIT Press.

Krogh, P. and Grønbæk, K. (2001). Roomware andintelligent buildings. In Proc. ArchitecturalResearch and Information Technology, ÅrhusSchool of Architecture.

Le Corbusier (1927). Towards a new architecture.New York: Praeger.

March, J. S. and Simon, H. A. (1958).Organizations. New York: Wiley.

Mozer, M. C. (1998). The neural network house:An environment that adapts to its inhabitants. InProc. AAAI Spring Symposium on Intelligentenvironments.

Negroponte, N. (1975). Soft architecturemachines. Cambridge, MA: MIT Press.

Newell, A. and Simon, H. A. (1972). Humanproblem solving. Englewood Cliffs, N.J.: Prentice-Hall.

Pohl, J. and Myers, L. (1994). A DistributedCooperative Model for Architectural Design. InKnowledge-Based Computer-Aided ArchitecturalDesign (Carrara, G. & Kalay, Y. E. eds.), ElsevierScience Publishers, Amsterdam, The Netherlands.



Rittel, H. W. J. and Webber, M. M. (1973).Dilemmas in a general theory of planning. PolicySciences, 4.

Rowe, P. G. (1987). Design thinking. Cambridge,MA: MIT Press.

Russell, S. and Norvig, P. (2003). Artificialintelligence: A modern approach (2nd edition).New Jersey.

Schön, A. D. (1983). The reflective practioner:How professionals think in action. New York:Basic Books, Inc.Sharples, S., Callaghan,V., and Clarke, G. S.(1999). A self-learning multi-agent architecturefor intelligent building monitoring and control.International Sensor Review Journal(19:1).

Simon, H. A. (1984). The structure of ill-structuredproblems. In Developments in designmethodology (Cross, N. eds.) (pp. 146-166): JohnWiley & Sons, Inc.

Streitz, N., Geißler, J., and Holmer, T. (1998).Roomware for cooperative buildings: Integrateddesign of architectural spaces and informationspaces. In Proc. CoBuild 98.

Weiser, M. (1993). Some computer scienceproblems in ubiquitous computing.Communications of the ACM. Vol. 36, No. 7, 75-84

Weiss, G. (1999). Multiagent systems: A modernapproach to distributed artificial intelligence.Cambridge, MA: MIT Press.

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