Timing Interruptions for Better Human-Computer Coordinated PlanningDavid Sarne, Barbara J. Grosz

Division of Engineering and Applied SciencesHarvard University, Cambridge MA 02138 USA

IntroductionRecent developments in the area of automated systems forplanning and scheduling demonstrate the potential for au-tonomous agents to collaborate with and support teams ofpeople operating in highly dynamic environments (Schurr etal. 2005). A wide variety of mission-critical domains ex-hibit such highly dynamic environments, ranging from firstresponders (Wagner et al. 2004), through exploration of re-mote planets and cleanup of hazardous sites to terror the-aters and military conflicts (McClure 2000). In such envi-ronments, autonomous agents (which we will refer to as co-ordinators1), that are designed to efficiently process the richset of information affecting the planning problem (Myers etal. 2002), have the potential of suggesting alternative coursesof action (given limited time for decision making and chang-ing plans) which more effectively achieve mission objectives.Such coordinators can take on responsibility for changes intask schedules, thus freeing people involved in doing tasksfrom being unnecessarily overburdened, especially in timesof crisis when plans need to be adapted to new circumstances(Schurr et al. 2005; McClure 2000).

While intuitive human-computer interfaces and intelligentteam coordination are important, critical failures will oc-cur unless human-agent interaction is appropriately managed(Schurr et al. 2005). A fundamental requirement for anysystem that is to collaborate with people is to have the appro-priate division of labor (Shieber 1996); the coordinator mustknow which decisions it can make and when to defer to orconsult people on the team (or other agents). Furthermore, tobe effective, coordinators must be able to adapt appropriatelyto the skills and knowledge of their human teammates; thatis, they must have adjustable autonomy.

Given the increased complexity and dynamic stochasticnature of events and changes in such domains (as well as thepotential for schedule failures to be costly or catastrophic),many proposed implementations of coordination and plan-ning systems rely on a distributed design, emphasizing scal-ability and responsiveness (Wagner et al. 2004). In thesesettings, individual coordinators are associated with specificparticipants (team leaders, units commanders, which we willrefer to as owners) in the organizational hierarchy.

The design of coordinators must take into considerationseveral important factors. First, the goal of coordinators isto maximize an overall team-objective and pre-defined util-ity rather than to maximize the specific preferences or utilityof an individual owner. Consequently, the focus of interac-

1We adopt the name from the DARPA COORDINATORs pro-gram, aimed to design, implement, and demonstrate a computa-tional framework for distributed planning.

tions is on facilitating a collaborative effort towards reach-ing the defined objectives. Second, the people involved inthese tasks and the coordinators themselves typically havelimited resources. Since each interaction between a coordi-nator and its owner incurs costs for both parties, the processof initiating and managing an interaction with the user needsto take into account the cost-effectiveness of the interaction.Costs include both the potential degradation in the task theperson is currently executing caused by the interruption andthe loss of utility that could be achieved if the computationalresources deployed for the interaction were used instead byother system components. On the utility side, there is the po-tential increase in (actual) performance obtained as a resultof the interaction. Thus, coordinators must be able to man-age efficiently partial, perhaps non-optimal, but shorter andless resource consuming interactions with the user.

We argue that a coordinator’s performance can be signif-icantly improved by making use of three principle sourcesof value added by the abilities of the user to (1) reduce thesearch space (e.g. by eliminating paths leading nowhere);(2) rank alternatives produced by the system, estimating theprobability of successfully implementing them in the envi-ronment; and (3) supply additional information thus relax-ing constraints imposed by the system’s limited knowledgeof the environment, when generating alternative plans. Eachof these added-values draws on knowledge the person hasthat is beyond that available to the system. In this context wepresent a high-level overview of our design of a CoordinationAutonomy (CA) module responsible to intelligently initiateand manage the necessary interactions with the owner as partof the cMatrix project2.

Designing an Efficient Intelligent CA ModuleCommon to all three sources of user added-value is the factthat users’ experiences play a significant role in their abilityto be of help to the system. Using their experience peoplecan successfully analyze scenarios that they have not previ-ously directly faced, deduce and adapt appropriate solutionsto slightly different problems and anticipate the results of ac-tions taken. Furthermore, an owner may possess informationthat the system may not have. Though part of this informa-tion is based on accumulated experience (e.g. understand-ing the influence of weather on the probability of successof executing an action), most of this information is gatheredfrom parallel (formal and informal) communication channelswhich people naturally scan. Typical sources of such infor-

2The cMatrix (for Coordinated Multi-Agent Team Reasoningand Incremental Execution) is an R&D project under the DARPACOORDINATORs program, built to support response to events thataffect the ongoing military plan.

mation are occasional coordination meetings (e.g., used forreporting status of task execution), open communication theuser passively listens to (e.g. when leaving the radio open,one gets to listen to messages associated with other teamsin the area) and direct communication used for coordinationwith other people acting on a joint task (throughout whichindividual often informally learns about the status and exis-tence of other actions being executed by others). As auto-mated coordinators and planners evolve, their ability to scanvarious communication channels will significantly improve,and thus the added-value from user interactions will derivemostly from accumulated experience.

Recognizing the particular sources of user added-value,and given environmental properties in which coordinatorsoperate, the design of a successful coordination autonomysystem should emphasize such functionalities as (1) presen-tation of plans; (2) interruptibility; and (3) user involvement.The above functionalities enable the system to take advan-tage of human strengths in a responsive process which eval-uates utilities and costs from a global point of view.

UserModulation

UserProfiling

BasicPlanning

UIConcepts

UI

User

cMatrix

EnvironmentModulation

CADB

LearningAlgorithms

cMatrix Interface

Current/future/alternative plans

What/ifqueries

BattlefieldData

User’s state

InteractionManager

CAOutput

Interactionflow

Figure 1: Data-flow diagram

Our proposedCA module hastwo main inter-faces, shown infigure 1. Thefirst connects tothe other cMatrixmodules, whichare used bothas a source forlearning aboutthe changingenvironment andfor evaluatingand analyzing the quality of different plans of action. Thesecond interface is with the user (the UI layer), supportingall the required different kinds of presentation methods anduser-machine interaction functionalities. This layer is basedon basic and established UI planning-related informationrepresentation concepts (e.g. clustering of related activities,drill-down capabilities, navigation within a plan). Theinformation to be presented using these functionalities, itsextent and detail level, the order by which it is presented andthe transition between the different representation modes,are all influenced by other, hidden elements of the module.These components include the Environment Modulation(responsible for maintaining a continuous representation ofthe environment as known to the cMatrix and the resultingcomplexities in the current plan accordingly), the UserModulation (responsible for maintaining a continuousrepresentation of the environment, including monitoring theuser’s state in order to assess the costs of interrupting him),the User Profiling (responsible to monitoring the interactionswith the specific user and refine the interaction methodsused according to an accumulated repository of knownbehaviors), learning algorithms that guarantee improvementover time and, finally, an Interaction Manager which filtersand integrates the inputs received from all the other objectsinto an optimal interruption strategy, defining the method,order and structure of the interaction with the user.

As part of the process of assessing gain in interrupting theuser, the system takes into consideration the information theuser already has about the planning problem, the complexityof the problem, the user’s capabilities and the extent of theplanned interaction. Once the Interaction Manager evalu-ates the gain to be greater than the expected cost of the inter-ruption, it initiates an interruption. The interaction processhas three principle subsequent stages as shown in figure 2.Estimating the gap between the user’s familiarity with theplanning problem and its actually extent, the CA module ef-ficiently navigates the user throughout the relevant aspects ofthe problem, focusing the user’s attention on the newest andmost relevant information received. Then, the CA modulesupports a cooperative planning process through a combinedsequence of different interaction mechanisms, until it finallyfeeds back to the cMatrix planning elements the user input.Interaction Process

ProblemDescription

WorkingCooperatively

• New information• Alternatives rating (DSS)• “What-if” analysis tools• Presentation methods• Classifications

New informationNew schedules

Problem presentation(including beliefs concerningthe environment)

Action Items

Multi-Session

• New information• Suggested\Selected\

Eliminated schedules• Schedules ranking• “Don’t know” \ “We’ll manage”

Co

ntin

uo

us P

rofilin

g

Figure 2: CA-User interaction flow

The CA mod-ule integrateslearning al-gorithms fordemonstratingimprovement overtime. The learn-ing process istwo-fold. First, itcaptures specificuser characteris-tics (emphasizingissues such asstrengths and weaknesses, preferences, capabilities and spe-cific behaviors). Second, it supports the process of modelingthe environment the user is operating in, in particular theway the user perceives it.

ConclusionsThe high operations tempo and growing complexity of plan-ning (and re-planning) in various mission-critical domainssuggest an approach in which systems act as primary plan-ners rather than assisting the user in planning. A criticalfactor in such systems success is their capability to initiate,maintain and manage intervention sessions with the com-manding user. User involvement may offer great benefit,but it is a costly process that needs to be managed wisely.The suggested design of the CA module, as part of the cMa-trix system, serves as an innovative framework for managingsuch user involvement.

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