Modeling tasks: a requirements analysis based on attention support services Joona Laukkanen Claudia Roda Inge Molenaar American University of Paris American University of Paris Ontdeknet 147 rue de Grenelle 147 rue de Grenelle Wibautstraat 4 75007 Paris, France 75007 Paris, France 1090 GE Amsterdam +33 1 40 62 06 82 +33 1 40 62 06 50 +31 20 525 1339 joona.laukkanen@aup.fr croda@ac.aup.fr inge@ontdeknet.nl ABSTRACT Modeling user tasks in a manner that is both complete and The task within which a resource is used is a very important operational is far from being an easy undertaking. Based on the element for the definition of Contextualised Attention Metadata. work done in the Atgentive project [2, 16, 17, 19], this paper In this paper we discuss the requirements of a task model that discusses how tasks may be modeled in order to support the allows representing current and potential attention allocation of implementation of attention management services. In the process the user. And we discuss how such model has been implemented we will also highlight another important relation between CAM in the AtGentive system. and task modeling, i.e. the fact that not only (as mentioned above) tasks may be associated to resource access, but also resources Categories and Subject Descriptors may be associated to task descriptions. H.3 Information storage and retrieval, H.5.2 User Interfaces, In the context of the Atgentive system a task represents the target H.1.2 User/Machine Systems, H.1.1 Systems and Information of an attentional focus (e.g. writing a paper, accessing some Theory: value of information, I.6.5 Model Development, resource, ...). Since we aim at applicability in combination with a number of different types of applications, the key design issue General Terms with the definition of tasks has been to make it as application independent as possible. In particular, the questions of task Design granularity, task structure, and task attributes, have been addressed. Keywords In section 2 we give a brief description of the Atgentive system. attention aware systems, attention metadata, contextualized We summarize the goals of the project, introduce the different attention metadata, learning, task modeling. modules of the system, and explain how the Reasoning Module provides functionalities supporting users' attention allocation. The 1. INTRODUCTION analysis of such functionalities has provided us with the most Task models represent a very important element in the definition critical requirements for the AtGentive task model. Whilst the of Contextualized Attention Metadata (CAM). As CAM aims at AtGentive System aims at providing many task-oriented services, tracking resources usage, identifying the specific context in which in this paper we concentrate only on those that support such usage takes place enables a much better understanding of the interruption management and task switching. In section 3 we value of each resource [10]. The task within which a resource is discuss the requirements that these services impose on task used is a very important element of such contextual definition. modeling, section 4 briefly overviews the issues commonly For example, in order to truly understand resource usage it would encountered in task modeling, and section 5 details the AtGentive be important to distinguish whether a user accesses a book review task model. because he is writing a research paper, or because he is preparing a reading list for a course, or because he is selecting gifts from a 2. THE ATGENTIVE SYSTEM wedding list. These three types of usages correspond to access to The objective of the AtGentive project is to investigate the use of the resource book review in the context of different tasks. The artificial agents for supporting the management of the attention of definition of users' tasks is therefore one of the essential elements young or adult learners in the context of individual and for the identification of the context of resource usage. collaborative learning environments. The AtGentive system observes the user's activity and generates interventions aimed at supporting his/her attentional choices. Such interventions may either be designed to help users sustaining their current focus of attention (e.g. help user to find the best way to complete a task), or they may be designed to shift the user's attention to a different focus (e.g. communicate important information that has become available). The main components of an AtGentive system include: one or more (1) applications, and (2) user tracking components providing information about the users activity, both these types of components communicate with (3) a reasoning module – see The reasoning module is designed as an application independent, figure 1. Applications, users, and tracking modules inform the general purpose entity capable of generating suggestions about reasoning module about the state of the user and the environment attention management. Within the AtGentive project the by generating events. The reasoning module supports the user in reasoning module is being tested in the framework of two his attentional choices by generating interventions that are then different applications: AtgentSchool, and AtgentNet. sent to the user. AtgentSchool is an eLearning platform for elementary school Figure 1 – A simple schema of an AtGentive system aged children, and AtgentNet is a virtual community platform supporting knowledge exchange in knowledge communities. 3. TASK ORIENTED SERVICES IN ATTENTION AWARE SYSTEMS In the context of the AtGentive project we have identified several task-oriented services aimed at supporting learners and knowledge workers in environments characterized by frequent interruptions and multi-tasking. These include: interruption management, support to task switching, orienteering within resources (e.g. searching and ranking), and self and community awareness. For sake of brevity, in this section we only discuss the first two services with the aim of detecting the characteristics that a task model should have in order to enable the implementation of such services. Interruption management Interruption management services are services that may either Events generated by the application either describe the user automatically select the time and mode of the interventions that activity (e.g. the user has started working on a certain task) or have been generated , or may provide notification services that relevant changes in the environment sensed by the application help the user making the decision on when to attend newly (e.g. new information is available that the user could access). available information. Events generated by users may describe their preferences (e.g. With respect to interruption management, the task model should "don't interrupt me when I am working on this task”), or provide a enable reasoning about cost/benefits of interruptions and allow direct feedback on the reasoning module's interventions. determining the most appropriate time for interruption. Finally, The tracking devices monitoring the user physical state reasoning about cost/benefits of interruptions and activity may generate events describing for example the user In order to decide whether to interrupt a user, the system must be keyboard activity, the level of noise in the room, or the presence able to consider the costs / benefits of the interruption. (or absence) of the user from the screen. For example, the system could decide to intervene and suggest On the basis of these events the reasoning module (which is that the user attends some newly available resource if: implemented as a multi-agent system) tracks what the users current focus is, creates a list of possible alternative foci, and [a] the resource is relevant to the task currently in focus or finally, evaluates those alternative foci and, using interventions, [b] the resource is relevant to an inactive or suspended task communicates those foci (if any) that seem to be most beneficial with a high priority to support the user attention. Note that a resource may be relevant to a task both if it is relevant While processing events, the reasoning module maintains an to the task or if it is relevant for a sub-task of that task. optimized list of foci that have been identified as most relevant Further considerations may intervene if enough knowledge about for the user. Each focus is composed of a target, a priority, and a the user tasks is available. For example, in case [b] notification state. Possible states are: current, inactive, or suspended. may be delayed if the user is about to complete the current task. Normally one of the foci is active (this is the user's current focus). Observatory studies report that returning to long term projects is Suspended foci are inactive foci that have been previously active. particularly challenging and makes such tasks potentially more Inactive foci are those that the reasoning module has evaluated as vulnerable to the harmful effects of interruptions, compared to interesting for the user but the user has never activated (e.g. the more common, shorter tasks, such as writing e-mails [7]. The focus associated to an email that the user has not yet read). The expected time that a task on average takes to complete, the priority is an estimate of how important/urgent the task associated number of subtasks, and the number of windows and resources to the focus is for the user. The target of the focus is either a user that need to be available, could help determining if a task is a task or a message. A user task is an instance of a generic task for long term project and hence, interrupting it is more costly than the specific user in the specific situation (see section 5.1). A interrupting some shorter task. message is something that needs to be communicated to the user without any concrete actions related to it, e.g. some motivational Following the above considerations, in order to reason about feedback for a learner who has completed an assignment. costs/benefits of interruptions, a task model should allow identifying: [REQ 1]The user's current task Restoring task context [REQ 2]The priorities of the current task and of other When task switches and interruptions are frequent, the activities (inactive or suspended) tasks required to restore the task context of a resumed task can be expected to result in a significant increase in cognitive load. A [REQ 3]The resources that may be relevant to a task diary study tracking the activity of knowledge workers to [REQ 4]The advancement state of tasks execution investigate these effects, reported that: (1) participants rated switching to tasks that were previously interrupted to be Most appropriate time for interruption significantly more difficult than to others, that (2) the resumed Several studies have demonstrated that the exact time when an tasks were in fact twice as long as other, more short-term projects interruption is presented may make a very significant difference and that (3) they required significantly more resources than other on both how easily the information presented is acquired by the tasks [7]. Automatically providing access to such resources when user, and on how much disruption it generates in the task being a task is resumed would represent a significant help to users. interrupted [1, 6]. Providing such service requires that: In order for the system to determine the most appropriate time for [REQ 6]The task model associates to interrupted tasks interruption, the task model should support the information describing the resources in use when the [REQ 5]Description of task hierarchies. task was interrupted. As noted by Bailey et al. [4] when tasks are organized into Task reminders hierarchies the task model can be used to infer "breakpoints" i.e. Another problem related to switching tasks is one encountered times when interruptions are less disruptive for the user. Bailey frequently when a task needs to be performed at a specific and his colleagues [1, 5] represent tasks as two level hierarchies moment (at an absolute time or in response to some event). composed of coarse events further split into fine events (for Prospective memory failures, which occur when something example, a coarse event would be the selection of the email cannot be remembered at the right time, may account for up to application, which would then be further decomposed in selecting 70% of the memory failures in everyday life [14]. This has been the email application, typing in the username, and typing in the shown to have a very eminent effect on performance in work and password). The authors then measure the impact of interruptions learning environments. Also, these memory failures intervene as they occur at various points within these hierarchies and differently in different age groups. demonstrate that the best times for interruptions correspond to coarse breakpoints. The availability of such a hierarchical task Providing services that remind users of important dates and model enables the system to infer the best time for interruption. In deadlines, or notify them of certain events could be used to the AtGentive system, when there is a switch in the users current alleviate this threat of prospective memory failures daunting so task, the magnitude of the break in attention is evaluated on the many activities planned to take place in the future. Further, task basis of the depth of the task in the task hierarchy. Further, a shift reminders could prove particularly useful to help users remember to the next subtask can be identified as a low strength break in the tasks that they have suspended earlier as a study has reported that users attention whilst a jump to a task that is not a child or a in fact over 40% of tasks that have been interrupted, are not parent of the current task may be interpreted as marking a resumed again [15]. For example if a user suspends a task T1 to stronger break in the users attention. work on another more urgent task T2, the system could remind him of the interrupted task T1 once T2 has been completed. If tasks are organized so that lower level tasks divide a higher level task into logical sub-steps, the level at which a task switch to In order to provide support with task reminders, it will be a next subtask happens may be used to infer the magnitude of the necessary to allow: break in the users attention, possibly with a more accurate value. [REQ 7]Associating to tasks information either about the A switch at a lower level marks a smaller change in attention than time when the task should be executed, or about the at a higher level. This would however need the task model to events that should trigger the execution (or resumption) allow specifying if a task does indeed refine the parent task or if of the task the parent task exists just to group subtasks, as could be in the case of a math exercise in a learning environment authored as a Task continuation and prioritization task hierarchy like [Task T1, “exercise 1”, T1.1, “exercise 1.1”, When there are several tasks that the user is working on in T1.2, “exercise 1.2”, ..., Task 1.2.n, “3 + 9 / 3 = ” ]. In the latter parallel or there simply are several tasks to choose from, for case a task switch to a next subtasks would actually mark a example when a task has been completed, it could be beneficial smaller break in user attention on a higher level task than when for the user if there were services that could take off some of the the switch to the next subtask happens at the level of the concrete cognitive load that is related to choosing the next activity. leaf tasks with the actual cognitive work. Especially so when the user might not have much knowledge about the relevant properties of the different tasks (e.g. how long Support to task switching a task is expected to last). Major motivation for services supporting task switching comes On the basis of the task structure it is possible to find some from the observation that people can only focus on one thing at a potential and logical, yet arguably more or less simple, time and as several authors have indicated [e.g. 18], switching continuation options for the user. More complex and useful from a task to another is costly. Services supporting the user with guidance can be achieved by applying some timing strategy in the task switching operations such as restoring task context, task evaluation, maybe by preferring tasks that may be completed reminders, and support for task continuation require a before their deadline. If the evaluation also considers the priorities comprehensive task model. of different tasks or gets otherwise more sophisticated, the document, submit form. In order to support the user in his reasoning could be expected to have a real effect on the cognitive attentional choices tasks should be described at a level that effort required from the user. corresponds better to the user's objectives, (e.g. write a paper, When a user completes a task, enabling a smooth transition to the complete an exercise). This type of task description has been next activity may entail restoring the context where the choice to suggested by some researchers [11, 13] and corresponds to the start the now completed task was made. This could amount to one used in the AtGentive project. In order to achieve this reminding the user of the task that was suspended when the user objective we require that: moved to the current task or, reminding him of the current task [REQ 9]The task model should allow different types of sequence (e.g. the next subtask, the next required task or the applications to define their own tasks and task structure parent of the task in the hierarchy). [REQ 10]The task model should allow describing tasks at In both situations the requirement for a hierarchical task structure any level of granularity ([REQ. 5]) is reinforced. Further, elements that will intervene in the evaluation of valid Task attributes Failing to provide contextual information within a task is another continuations include prioritization (already listed as requirement pitfall of several task modeling efforts. Contextual information [REQ. 2]) and timing: such as relevant resources and users, deadlines, complexity, [REQ 8]Allow the definition of task deadlines priority, state of advancement, and location of the task in a task On a more sophisticated level also expected duration of tasks, is hierarchy is something that is clearly needed for many services required, this is listed below as [REQ 14]. supporting attention management. The inclusion of some of these attributes is represented by several requirements already listed above, further task attributes we have identified include: 4.ISSUES IN TASK MODELLING Diaper quotes Shepherd [20] as saying that “'Task' is seldom [REQ 11]Keywords may be associated to tasks. defined satisfactorily” and continues suggesting that this might Keywords provide a way to relate tasks to resources (e.g. by actually never be the case [8]. Some difficulties in defining tasks, keyword matching) such as the specification of application independent task [REQ 12]Maximum allowed idle time may be associated to taxonomies, have been repeatedly encountered and seem tasks inherently difficult. Some other issues may be easier to address but need a comprehensive approach. For example whilst it would The Maximum allowed idle time specifies the time limit within not be difficult to provide adequate contextual information for which the user is expected to act to avoid being recognized as idle tasks, this information is often missing from task models. This by tracking devices. This information is used both to identify section briefly overviews what we consider the main open issues breakpoints and to provide help or solicitations to users who seem in task modeling. to have difficulties continuing a task. [REQ 13]Task difficulty levels may be associated to tasks Task taxonomies One clear problem when modeling tasks is the difficulty of Indications on the difficulty of a task may help in the evaluation defining a sufficient taxonomy. It would be useful to classify of the cost/benefits of interruptions, as well in the selection of the tasks, for example, by type of operation. Finding generic actions help to be provided to users. or operations independent of application types has however [REQ 14]Expected duration of the task may be associated to proven very difficult [8]. One of the few generic tasks that Diaper tasks. & Johnson [9] were able to identify in their work on TAKD (Task This attribute specifies the average expected time to complete the Analysis for Knowledge Description) was insert. TAKD is a task. Combined to the task advancement indication ([REQ. 4]), it method capable of modeling tasks in a wide range of applications enables a better evaluation of the best time for interruption. and within this work insert was found common for a number of Further, task continuation services may implement strategies in different objects in different application domains (namely which, under certain conditions, tasks with certain durations (e.g. microelectonics, automated office applications, and computer tasks that can be completed quickly) are preferred over other programming). Inserting could here mean either inserting text in a tasks. word processor or a program editor or alternatively inserting components on a Printed Circuit Board. Whilst it could be [REQ 15]Actors relevant to the task may be associated to possible to identify some actions possibly totally independent of tasks application domains, such as insert, the set of such actions seems Relevant actors could for example include a teacher in the case of to be simply too small. Whilst Diaper [8] does not see the a learning environment or the creator of a resource when the task development of task taxonomies as totally impossible, it is is simply to attend some resource. In general actors relevant to obvious that we are far from having such a tool and probably the tasks will be defined within a social network associated to the definition of ontologies allowing the integration of several such user model. This information is both useful to evaluate the taxonomies is the most promising direction of research. relevance of newly available information, and to provide community awareness services. Task descriptions Traditionally tasks have been described at the level of the [REQ 16]Support tasks may be associated to tasks (see application, i.e. tasks correspond to very specific users' actions section 5) within a specific application, e.g. create document, attach Currently we assume that most of these parameters are manually A main task can then be described to consist of a number of finer entered (e.g. by the user himself, or by a teacher setting up a level tasks. Task T1, Writing a paper, could for example consist learning sequence - as is done in the AtgentSchool application), in of the more concrete tasks T1.1 (do research) T1.2 (write the future we expect that the system may be capable of generating abstract), ..., T1.n (discuss future work). The hierarchical estimates of parameters such as maximum allowed idle time, task organization of main tasks allows for varied granularity when difficulty, expected duration time, etc. by observing how several defining tasks; nothing forces one to define tasks at a finer level users act on the task, and by inferring the possible behavior of a so for example writing a paper could in some environments be specific user. modeled as a single high level task if the task is, perhaps, known to be already well understood by the target users. In another Recognizing tasks environment the same task could be represented as one with a Whilst defining tasks, their structure and resources presents, as number of subtasks (possibly on several levels). Besides allowing described above, a series of challenges, a further, possibly more granular description of task execution, subtasking can be used to complex challenge is represented by the automatic recognition of distribute support more accurately where it is needed. This could tasks. This requires that, on the basis of the observation of user's in fact be one way to author tasks; first identify how the entire actions, the system is capable of matching actions sequences to task needs to be supported (e.g. support for doing research, specific tasks. The problem here is that if simple sequences of support for writing the abstract, ...) and divide the task in subtasks actions are observed (such as typing some characters on the accordingly. keyboard) the system may not have enough semantic information In defining task structure we have identified further requirements to associate the action sequence to a specific task. In fact a very for the task model these include: large number of higher-level tasks may be associated to simple action sequences. Within AtGentive we base task recognition on [REQ 17]The task model may include a requirement level three possible inputs. First, an application, which has a much for a task better knowledge of the semantics associated to simple user [REQ 18]The task model may include task ordering actions may recognize that the user is working at a specific task and communicate this information to the reasoning module. [REQ 19]The task model may include task visibility Second, AtGentive may use its knowledge about a small subset of These properties are tightly related to the execution of tasks at a all possible user tasks that are most likely to be performed by the given moment and are useful to support task continuation (see user at a given time, and use this information to recognize that a 3.2.3), they are briefly described below. simple action sequence is actually contributing to a specific task. Task requirement level Third, the user may explicitly indicate that he is performing a certain task. Tasks may be defined as optional or required. Required sub-tasks are necessary (i.e. they must be executed) for the completion of the parent task. Tasks defined as optional allow the user to skip 5.ATGENTIVE TASK MODEL certain sub-tasks in the execution of a main task. In a learning The task model implemented in AtGentive's Reasoning Module environment some exercise for example, may be marked as distinguishes between two different categories of tasks: main optional. tasks and support tasks. Main tasks are in essence anything the user may decide to do. Support tasks are aimed at helping the user Task ordering perform a given main task and manage his attention within that The order in which a task's subtasks need to be performed could task. either be specified as free for the user to choose, or mandated. In a learning environment an assignment might for example consist of Generic Tasks versus User tasks reading a book and then writing a summary about it. Here it Both main tasks and help tasks represent abstract task properties. would make sense to mark the ordering of the assignments Whenever main tasks, or help tasks are activated concrete subtasks to be mandated. instances are created as user tasks. This results in creating a hierarchy of user tasks corresponding to the hierarchy of the main Note that, if ordered execution is required, optional subtasks can tasks and support tasks. User tasks instantiate all the properties for still be skipped. the concrete execution of that task, such as a deadline, Task visibility progression etc, for one particular user. For example, one may Tasks may either be visible or invisible. Invisible tasks are always have a main task "prepare lecture" which has abstract properties inner nodes in the task hierarchy and allow describing abstract such a title, and an average expected duration, and is organized in tasks that, although not executable, are useful to conceptualize a a hierarchy of sub-(main)-tasks such as "collect resources", certain grouping in sub-tasks. A group of root tasks that are not "create draft", etc. each having their abstract properties. For each related to each other could for example be grouped under one user, there would then be a corresponding user task structure to common invisible root task. Invisible tasks could also be useful if actually execute the task, with for example individual deadlines there is a need for a more complex ordering than what otherwise for those users. would be allowed by the task model (without adding mundane tasks that only include selections between subtasks). Main tasks Main tasks (and the related user tasks) represent actions the user The task model does not support certain task sequencing might perform, e.g. write a paper, prepare for a meeting, complete constraints. For example its is not currently possible to specify the an assignment. These tasks can be formed into hierarchies as requirement that the user completes a certain number of subtasks pleased as all main tasks could have other main tasks as subtasks. (say for example 2 tasks out of 3). Support tasks objectives of many of the task models presented in the field of Support tasks help the user in performing various types of human-computer interaction, some of them may be used to guide activities that the user might attend at different stages of a tasks future development of our model. execution. For example, a support task might help a confused user gaining a better understanding of the task at hand, another support 7.ACKNOWLEDGMENTS The work described in this paper was partially sponsored by the task could provide some motivational feedback such as statistical EC under the FP6 framework project Atgentive IST-4-027529- information about the users time usage after a task is already STP. We would like to acknowledge the contribution of all completed. project partners. Support tasks differ from main tasks mainly in two ways. First, they cannot be organized into hierarchies and they do not have 8.REFERENCES further support tasks themselves. 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