Decision Support Systems 8 (1992) 255-267 255 North-Holland

Context-sensitive office tasks A generative approach

K n u t H i n k e l m a n n * 1. Introduction

DFKI GmbH, D-6750 Kaiserslautern, Germany With the development of local- and wide-area

Dimitris Karagiannis networks, mail systems, and multi-user databases, FAWUlm, D-7900 Ulm, Germany computers will be more and more used to in-

crease organizational effectiveness. This so-called

Organizational knowledge is one of the most important Computer-Supported Cooperative Work (CSCW) aspects in office systems. Supporting task processing in the can be classified in three categories [EL88]: office domain can be viewed as organizational Computer-Sup- ported Cooperative Work (CSCW). We describe three layers - Collaboration within a g r o u p geographically for modeling and processing office tasks: Conventional sys- co loca ted at the same t ime. terns have predefined static models representing a structured - Real-time collaboration among people who a r e task plan. The generation of a task plan at starting time can geographically distributed. take into account the actual context giving simpler and more - Asynchronous collaboration among teams/ adequate plans. Techniques from traditional office automa- tion are combined with inference mechanisms and Artificial persons geographically distributed. Intelligence techniques for knowledge-based information sys- Another classification is given by [Hun88]: Col- tems. This approach is described in detail. In the third, most laboratit;e CSCW tools improve the productivity flexible approach, called 'acting by opportunity' , the office o f meetings or document creation by increasing worker has the possibility to perform actions independently their efficiency. They are more or less static, i.e. from a task plan. The task processing system has to update the generated task plan according to user-driven deviations, they merely manage information and communica-

tion. Examples are the conference room system Keywords: Office automation, Computer-supported coopera- CoLab developed at Xerox Palo Alto Research

tive work, Artificial intelligence, Knowledge repre- Center and shared screen systems like videocon- sentation, Planning. ferencing. In organizational CSCW the amount

for meetings is reduced, and collaborative docu- ment creation is promoted by automated support of communication and coordination. These sys- tems include programs that track or facilitate the division of tasks, or those that monitor resources IF " and performance. They are actively managing Dimitris Karagiannis holds a degree

in Computer Science at the Univer- tasks, running jobs and telling people to do things. sity of Berlin where he also did his thesis in the field of Artificial Intelli- gence and Databases. Since 1988, he has been working as senior scientist at the Research Institute for Applied Knut Hinkelmann finished the study Knowledge Processing (FAW) in , of Computer Science at the Univer- U l m / G e r m a n y . ~ { ~ sity of Kaiserslautern in 1988. From

1988 to 1990 he worked at the Re- search Institute for Application-Ori- ented Knowledge Processing (FAW) in Ulm where he was mainly engaged

Correspondence to." Dimitris Karagiannis, F AW Ulm, Postfach in the domain of office automation. Since 1990 he is working at the Ger- 2060, D-7900 Ulm, Germany. email: karagian@dulfawla.

bitnet .... man Research Center for Artificial ........... Intelligence (DFKI) in Kaiserslau-

• This work was done while the author was at the FAW in tern. Ulm.

0167-9236/92/$05.00 © 1992 - Elsevier Science Publishers B.V. All rights reserved

256 K. Hinkelmann, D. Karagiannis / Context-sensitit,e office tasks

In our project we are engaged in supporting cuted automatically by the system itself, e.g. the cooperative work in a complex office domain, transmission of messages to internal or external Office work can be decomposed into a number of co-workers using the communication subsystem. actions. A multitude of actions aspiring the satis- Other activities require user interaction. In this faction of one goal are named a task. Thus, a task case a competent office worker - called the agent is a set of actions - also called activities or - is chosen by the system. He is requested to processes - with logical dependencies. A task's perform the activity which is put into his personal activities may be executed sequentially or in par- task agenda. The agent can execute the action allel depending on logical and time constraints. If whenever he likes unless he exceeds a given dead- several workers are involved in one task it is line. When the agent decides to execute a task called a cooperatiue task. Well-known examples activity the system supplies him with all docu- of tasks are the editing of a scientific journal (see ments and information necessary to fulfill his job. [Zis78], [CL88])with actions like "ReviewPaper", As soon as the activity is finished, it is deleted "GetReviewers" or the application for an official from the agent's personal agenda. The system journey (see [WK85]) with actions like "Permis- looks for other activities which are now ready for sionOfJourney", "TravelReimbursement". execution. A task execution stops, if there are no

Tasks often do not follow a strict plan. Their actions to perform anymore. execution is influenced by three criteria: We distinguish three levels of task modeling. A

first approach represents a task as a structured 1. Guidelines of an organization prescribe how a

plan of actions. A task administrator (also called task is to be processed, i.e. which jobs have to

task programmer) analyzes the company's guide- be performed; they clarify competences and lines. Every step of task execution is programmed specify details of the execution. Some very and every branch is explicitly modeled. This ap- general guidelines may be applicable in several proach strictly follows the first of the given crite- tasks, e.g. responsibilities for finances, ria for task processing; however, because of the

2. Short- and middle-term changes in working dynamic office work this approach is too rigid. processes may result if a person is on holiday, Taking into consideration criteria 2, every possi- if resources are not available etc. Reactions on

ble exception would have to be expected in ad- these changes also depend on guidelines, which

vance and included into the plan by conditional are relevant only in exceptional cases, how-

branches leading to swelled, complex plans. Thus, ever. this approach is not practical in real office envi-

3. Office workers involved in a task have individ- ronments.

ual freedom when and how to do a job - as To overcome the deficiencies of this first ap-

long as it is not specified by guidelines, proach, the generation of structured plans has

The aim of our work is to develop an organiza- been evolved. The analysis and interpretation of tion support system integrating (a) task processing guidelines to develop a task plan is performed by with two other basic components of office sys- the system itself as soon as the task is initiated. tems: (b) communication and (c) document and Known exceptions - as referred to by criteria 2 - information management. Concepts from tradi- can be considered in advance at task starting tional office automation are combined with Arti- time, thus leading to much simpler plans. The ficial Intelligence techniques in order to develop result of task generation is a context-sensitive knowledge-based office information systems that plan taking into account the actual situation use new knowledge representation and inference known at the time when the task is started. mechanisms (cp. [Kar89]). Following the above Although this approach is more adequate than classifications supporting task processing in an the previous one it does not necessarily satisfy office belongs to organizational CSCW with asyn- criteria 3. To really satisfy this criteria the task chronous collaborations, initiator as well as any agent of the task must

Using such an organization support system an have the possibility to modify the task structure if office w o r k e r - called the task i n i t i a t o r - deter- it seems appropriate to him or if constraints mines his goals and the system infers the activi- independent from the actual task demand it. For ties to satisfy them. Some activities can be exe- instance, if he knows that a computer will go

K. Hinkelmann, D. Karagiannis / Context-sensitit'e office tasks 257

down in 10 minutes he should start a later action, _ . . . . . . . . ~ ) proposal requiring this computer, although not all of its

~ project preconditions are satisfied already. We will call leader this kind of task processing acting by opportunity.

. • T ~_.application for These task processing approaches will be ex- re jec t lono (_F)invitatio n emplified with the task of inviting a guest lecturer to a research institute. The example together with / • • 7 director the approach of task modeling by structured plans . . . . . . . . . . . ,,~ pe~m,s~s,o, j is described in the next section. The main part of

permission~ ~ . . the article deals with the generation and p r o c e s s - Y f ° r m ~ fl ~_._fl rejection ing of context-sensitive plans (chapter 3). A few considerations integrating acting by opportunity initiator I ]secretary

I are presented in chapter 4. I invitation date appointment 1

(~let ter of

2. Task processing with structured plans secretary ~ - - M c o n f i r m a t j ion secretary

I hotel . . . . . . tion [I announc . . . . t I Most existing task-processing systems use what , ~

[WL87b] calls a 'procedure automation approach'. (~invoice, C-~poster, Cooperative tasks with multiple agents are explic- ticket k"-~/e-mail

itly represented as a structured sequence of office reimbursement o f ~ book-keeper

activities, i.e. the flow of work through an enter- travelexpe . . . . | prise is explicitly modeled. In order to represent , ~ the specification of a task kinds of Petri nets like (~) check

the one in [WK85], augmented Petri nets [Zis78], Fig. 1. Example task: inviting a gues t l ec tu re r .

production system nets [FW89], activity networks [TLF88] and also rule systems are used forming a model of the task. Such a model contains all potential actions and establishes their order of mented as procedures which determine how the execution, branches and conceivable alternatives, actions are to be performed. In these approaches the development and main- Initiating a task means to select the right net tenance of the task model requires a task admin- or plan, instantiate it, i.e. select the persons who istrator [LHWM88], who analyses the general and have to perform actions, specify infrastructural task-specific guidelines, interpretes them and resources, documents, and time constraints etc. forms a task model. He has to be familiar with and start the execution. However, if the guest is the system and the company or enterprise the invited by a project leader or the director himself, system is applied in. some activities in the plan of fig. 1 are superflu-

We will present this approach with a simple ous. Thus, before plan instantiation an extraction example happening in a research institute with phase can be fit in, extracting the relevant sub- various research projects and a management led plan (i.e. the essential nodes) for the actual task by a director. Imagine a researcher working in a (cp. fig. 2). project who wants to invite a guest researcher to This kind of task modeling is rather rigid. give a lecture at the institute. If his project leader Every possible exception has to be included into agrees with the invitation the director has to the plan. If, for instance, in absence of the pro- permit it, because he has the financial compe- ject leader the permission can be given by the tences for honorary and reimbursement of travel director directly, this has to be explicitly modeled expenses. If the permission is given the guest is as indicated by the dashed line in fig. 1. The same invited and the talk is announced. After the talk holds for many other exceptions of how actions the guest will get his fee and his travel expenses can be executed. E.g. while the announcement of reimbursed. This example is presented with a a lecture is conventionally presented at the bul- Petri net in fig. 1. The rectangles can be imple- letin board it must be distributed by electronic

258 K. Hinkelmann, D. Karagiannis / Context-sensitive office tasks

~ ~ task [ instantiated [ s t r u c t u r e 5 X [task i~'ucture [ C5"¢b'O instantiati°--n I ~ I % ~ / ~ ~ ~ .~ execution

.~ :." " " " : ' ? ~ extraction\- - -~ , i,~yi~.ion }

instanfiafion planl

structured plan approach 2

Fig. 2. Generative task processing: Generating plan 1 or plao 2.

mail, if the time between announcement and the task plan are many conditional branches. lecture is very short. When executing a task one of these branches is

In addition to the detailed representation of chosen depending on the actual state of the task. task plans, [LHWM88] gives office workers the Many of these decisions, however, can be made opportunity to add their own aspects by complet- already at starting time. Therefore the exact spec- ing or modifying tasks. However, the disadvan- ification of a task plan should be delayed until tage of this structured plan approach is not re- there is enough information to make it optimally moved, because the plan remains very complex adequate. A context-sensitive plan can be gener- and the system itself can not react on exceptional ated at the time when the task is started taking situations, into account the actual situation known at this

time. It should be rather indefinite and will be made more precise during execution when addi-

3. A generative approach for task processing tional information is available. A system roughly following this scheme is

The a priori generation of accurate structured POLYMER [CL88], which uses interactive plan- task plans (chapter 2) is not feasible in complex ning. A task manager specifies what has to be real world domains. The predefined task struc- done, how it should be done, who should do it ture should be as flexible as possible. Included in and when it must be done. He is responsible for

K. Hinkelmann, D. Karagiannis / Context-sensitice office tasks 259

planning and performance of the task. A plan- non-human resources and actual data-like ning system refines the initial plan and resolves schedules, inconsistencies arising from an incomplete or - knowledge about office activities which can be wrong domain model or from user-driven devia- composed to tasks, tions from the initial plan. - knowledge about guidelines for office work.

As already described in chapter 1 task process- Control is undertaken by two subsystems for ing depends on guidelines, the actual situation

(1) task generation (also called task planning) and and individual freedom of the office worker. The

(2) task execution. Task generation forms a task way in which to perform a task is prescribed by the guidelines of the company. These guidelines plan out of predefined activities. Activities are

selected by goals which they contribute to reach. do not specify a concrete task plan, but they Guidelines of various specificy (general, corn- determine rules how to process a job. The guide- pany-specific and task-dependent) determine lines, however, are not strictly assigned to one task. On the contrary, there are very general generation as well as execution of tasks. Depend- guidelines which are applicable in many different ing on changes in current organizational knowl- tasks, edge identical goals may result in different task

plans. In our generative approach we will concen-

trate on the automatic generation of a task plan. Instead of having a task administrator ([LHWM 3.1.1. Organizational knowledge 88]) or task manager ([CL88]) to be responsible For representing the organizational knowl-

edge, object-oriented tools are used; these are for specifying a task plan we will present a system interpreting the guidelines of the company with also proposed by [TFGN87] and [WL87a]. Since respect to the actual situation and thus generat- our system is implemented in KEE, 2 the objects

are called units. Objects consist of two parts: a ing a context-sensitive task plan by itself. The

control part defining methods and a conceptual term 'context-sensitive' means, that unlike the plans of chapter 2 the task plan does not include description. The conceptual description is given all task variations. It is an adequate model in by definitions of the object's properties which are

called slots. Objects are described by classes and accordance to the actual context. A similar ap- proach is developed in the area of machine learn- organized in inheritance hierarchies. A simplified ing, called 'sloppy modeling' [Mor89]. To obtain conceptual description for a class R e s e a r c h -

p r o j e c t could look like this possibility the guidelines themselves have to be represented in the system, c lass : ResearchProject

Superclass: Department

3.1. Representing office knowledge ProjectLeader: (of class Researcher) ProjectMember: (of class Researcher)

Theme: unknown

In analogy to declarative programming lan- Organization: (of class Organizational-

guages our generative task processing approach s t r u c t u r e) separates control from domain knowledge. An " '"

adequate conceptualization of domain knowledge Representations of specific real-world things plays a dominant role as [Wim89] argues, where a are instances of these classes, e.g. a project occu- general framework for knowledge representation pied with the development of organization sup- is given. Domain knowledge includes three port systems: sources of knowledge (cp. [KK87]):

Unit: OSSY-Project

- organizational knowledge: knowledge about c lass : ResearchProject projects and responsibilities of persons and e r o j e c t L e a d e r - John

departments; infrastructural knowledge about P r o j e c t M e m b e r : P a u l , G e o r g e , R i n g o Theme: " "Organization Support Systems "

Organization: LineStructure

i A c t i n g by o p p o r t u n i t y ( c h a p t e r 4 ) wil l i n t e r l e a v e t a s k g e n e r -

a t i o n a n d e x e c u t i o n . 2 K E E T M is a t r a d e m a r k o f I n t e l l i C o r p Inc .

260 K. Hinkelmann, D. Karagiannis / Context-sensitiue office tasks

StatementOfAccount ......

System A u t o m N o t i f i c a t i o n ~ _ _ _ . Activitie~

TimePlannlng z~ ....

Activities FillingForm A p p l i c a t i o n ~ - - -

ReadingForm ZZi22[

Person / permissiOn~%i[

Activities ComposedAct ~Agreement :cJi]

/SendingDocument ~ ' C I ~

Co~unication-- SendingText ZZZS[2

Calling :Z22~ Fig. 3. A hierarchy o f activit ies (detail)•

3 . 1 . 2 . G u i d e l i n e s i F . . .

Labour in a company or institute is directed by (a n A c t i v i t y o f c u r r e n t T a s k i s a large set of guidel ines. They can be found in ? P e r m i s s i o n)

(an Activity of CurrentTask is t h e organizat ional h a n d b o o k o f t h e c o m p a n y a n d ? D a t e S e t t i n g )

lay down, for instance, who is responsible for . . .

which jobs; they can de termine which single ac- THEN ( a S u b s e q u e n t A c t i v i t y o f ? P e r m i s -

tions are necessary to make an official journey, s ion i s ? D a t e S e t t i n g )

who is a l lowed to permit this journey, how much (a Precondition of ?DateSetting is (the Goal of ?Permission))

m o n e y is re imbursed etc. For the representat ion of these guidel ines product ion rules are appropri- "Before permiss ion the date has to be fixed" ate, because they are similar to their convent ional

- rules for task execution: description. Three kinds of rules can be distin- guished: 3 IF (an Object of CurrentTask is

?lecture)

- generat ion rules to determine activities (by ( t he l e c t ur e r o f ? l e c t ur e i s

declaring goals) ? g u e s t ) (the TravellingDistance of

IF (an Object of CurrentTask is ?guest is far)

?lecture)

(?lecture is in class Lecture) THEN (the Reimbursement of Expenses is THEN (a Goal of CurrentTask is 100)

"(the Permission of ?lecture is

? y e s . o r. n o) ) "If the travelling distance of the guest lecturer (a G o a l o f C u r r e n t T a s k i s is very far, he will get re imbursement of his

( t h e D a t e o f ? l e c t u r e i s ? d a t e ) ) travel expenses of 100 per c e n t "

"A lecture has to be permit ted and the date The guidel ines vary from spec ia l ized to very

must be appointed." general ones . As a consequence , s o m e guidel ines - sequencing rules to plan the sequential order

have to be used in different tasks. For example , of action execution: the guidel ine, that a permiss ion has to be given

on the application form is applicable in nearly all permiss ion procedures . Rules represent ing the

3 Variables are marked by a quest ion mark, e.g. ?lecture. guidelines can be collected to rule classes whereby

K. Hinkelmann, D. Karagiannis / Context-sensitil~e office tasks 2 6 1

the same rule can belong to several rule classes, by [CL88] or [Kem88]. The actions are also struc- Referring to a rule class selects all the rules of tured in an object hierarchy, part of which is the class. Thus, the general rules, which belong to shown in fig. 3. There are system activities, which multiple classes, can be used for many different are executed like programs without any user in- tasks, teraction, and activities being activated and exe-

Many objects in our knowledge b a s e - espe- cuted by one or multiple persons. The latter cially activity descriptions - have a slot G u i de- person's activities can be either atomic or com- l i n e s referring to rules (see also P e r m i s s i o n posed of subactivities. in fig. 4): Since activities are dynamic objects the defini-

tion of the control part is very important. For C l a s s : L e c t u r e each activity there are predefined methods for

Superclass: Event L e c t u r e r : u n k n o w n activation and execution control. Other methods Theme: unknown can be triggered by the user if he wants addi- Date: unknown tional information or if he wants to delay the P e r m i s s i o n : ( o n e . o f g r a n t e d r e j e c t e d c o n - execution or if he wants to cancel or reject an

s u t t a t i o n ) action because he feels not responsible. The con- Guidelines: Permi ssionGuide lines,

T r a v e l l i n g G u i d e l i n e s , ceptual part of an action is defined by properties L e c t u r e G u i d e l i n e s like involved persons and resources, conditions

. . . . . for their activation, sequencing relations to other actions etc. To each activity definition its seman-

These rules are added to the slot Guide- tics is d e s c r i b e d - in our current version of the l i n es of those activity instances, which affect system by definition of goals that the activity can the object. They are applied when executing the reach. Goals are atomic formulas which are true activity. General rules are referenced by many when the activity has been finished. The class objects, while for specialized jobs there are spe- description of Pe r m i s s i on is presented in fig.

cialized rules. 4. Most of the slots are still uninstantiated. They

will be instantiated shortly before execution time 3.1.3. Actiuities when the activity instance is activated. This is the

The representation of office tasks and actions time when information is most detailed and reli- also fits into the object-oriented concept as shown able.

class: Permission Superclass: ComposedActivity Agent: unknown Documents: unknown Object: unknown Guidelines: PermisslonGuidelines Goal: (the Permission of ?x is ?y) Preconditions: (the Application of ?x is proposed) SubsequentActlvities: unknown Subgoals: (the Applicant of ?x is notified) SubactlvityClasses: FilllngForm Subactlvitles: unkown Start: <method>

Activate: <method>

Delay: <method> Inform: <method> Cancel: <method> Reject: <method>

F i g . 4 . C l a s s d e s c r i p t i o n ~ r a c t i v i t y p e r m i s s i o n .

262 K. Hinkelmann, D. Karagiannis / Context-sensitice office tasks

The most impor t an t slot for task p lann ing is t i n e s , r r a v e t t i n g G u i d e t i n e s and L e e - the slot G o a t . In the act ivi t ies ' class def in i t ions t u r e G u i d e l i n e s are r e fe r r ed to. The genera - this slot is f i l led with un ins t an t i a t ed fo rmulas t ion rules of these rule classes infer goals that de t e rmin ing the purpose , which the activity is to mus t be sat isf ied dur ing task execut ion. The goals be app l i ed for. Act ivi ty instances are g e n e r a t e d d e t e r m i n e mi les tones of the task but do not say dur ing task genera t ion . Then the slots p r e c on- anything about how they are to be reached . F o r ditions and SubsequentActivities are example the gene ra t ion rule given in sect ion 3.1.2 ins tan t i a ted l inking activity ins tances and thus will der ive the goals

bui ld ing up a task plan. They d e t e r m i n e the logi- ( t h e P e r m i s s i o n of Lec tu reDagober t i s cal d e p e n d e n c i e s and sequent ia l o r d e r of activity ?yes . o r . n o )

execution (the Date of kectureDagobert is ?date)

The slots SubactivityClasses and Sub- Now the task in i t ia tor has the chance to ma- g oa t s specify, which kind of subact ivi t ies con-

t r ibute to an activity ins tance which is c o m p o s e d n ipu la te the goal list. H e can add fu r the r goals or de le te some of the goals if they seem not neces- of subact ivi t ies . They play a role in the phase of

fine p lann ing dur ing task execut ion, e e r m i s- sary or if he wants to do the job in his own way. s i on is an activity consis t ing of two subact ivi t ies: As soon as the goal list is fixed, the system has to

f ind activi t ies that can he lp to satisfy the goals. It Fi l l ing in a form and notifying the app l i can t

i t e ra tes over the list of goals and t r ies to unify whe the r the pe rmiss ion is given or not. As can be seen f rom this example , act ivi t ies t hem with the fo rmulas in the 6 o a l slot of the

can be r a the r gene ra l so that they can be used for activity classes. Fo r each successful unif ica t ion a new ins tance of the activity class is c rea ted . Fo r var ious d i f fe ren t tasks. Thus, the P e r m i s s i o n

activity may be used for official journeys as well example the goal ( t h e P e r m i s s i o n o f L e c- t u r e D a g o b e r t i s ? y e s . o r . n o ) is u n i f i a b l e

as hol iday permiss ions or even invi ta t ion of guest r e sea rcher s (see below), with the fo rmula in the G o a t slot of the activity

class P e r m i s s i o n with unif ier cr and b ind ing ? x to L e c t u r e D a g o b e r t . Consequent ly , a new

3.2. Planning context-sensitiue tasks ins tance of this class is c r e a t e d - call it P e r m i s-

3.2.1. Generating activities s i o n - 1 - and all fo rmulas in the slots P r e c o n- d i t i o n s and S u b g o a l s of this new activity are

To s tar t a task an office worke r - ca l led the in t an t i a t ed according to ~r. The new activity P e r-

task in i t ia tor - descr ibes wha t he wants to do. In m i s s i o n - 1 now has the following new slot en-

the s imples t case he p re sen t s one or more goals tries: to the system and gives re fe rences to re levant objects. He can also give def in i t ions of new ob- Unit: Permission-1

jects , e.g. the pa r t i a l desc r ip t ion of a lec ture if he z n s t a n c e 0 f : P e r m i s s i o n

in tends to invite a guest lec turer : Ob jec t : Lec tu reDagober t Goal: (the Permission of LectureDagobert

Unit: LectureDagobert is ?y) InstanceOf: Lecture Preconditions: (the Application of Lec-

Lecturer: DagobertDuck tureDagobert is proposed)

Theme: "'How to increase your money "" Subgoals: (the applicant of LectureDagob-

Audience: 50 err is informed)

Then , for instance, a goal p r e s e n t e d by the Acco rd ing to this scheme ins tances of the ac- in i t ia tor could be tivity classes H o t e l R e s e r v a t i o n and D a t e A p -

p o i n t m e n t a re gene ra t ed . (the Hotel of LectureDagobert is ?hotel)

F u r t h e r activity ins tances can be de r ived in ter - The task in i t ia tor ' s speci f ica t ions d e t e r m i n e p re t ing the p recond i t ions of g e n e r a t e d act ions as

access to re levant par t s of the o rgan iza t iona l goals, too. The p recond i t ion of an ac t ion A knowledge base lead ing to the se lec t ion of appro - means , that A can not be execu ted be fore the p r i a te guidel ines . Thus, in the class descr ipt ior l of p recond i t ion has been satisfied. Hence , the re Le c t u r e the rule classes P e r m i s s i o n G u i d e- must be activit ies to satisfy these p recondi t ions .

K. Hinkelmann, D. Karagiannis / Context-sensitive of]ice tasks 263

As the p r econd i t i on of p e r m i s s i o n - 1 is unifi- du ra t ion of the action, and the re fo re must not be

able with the goal fo rmula of the activity class m a d e false by any concur ren t activity. However , A pp l i c a t i o n a new ins tance of this class is for reasons of simplici ty these are omi t t ed in this gene ra t ed . As a resul t of our gene ra t i on phase we paper . have four activity instances: In pr inc ipa l the re are two ways to d e t e r m i n e

P e r m is s io n-1 the o rde r be tween two activities: Firs t , A -< B for App l i c a t i on-2 every activity A c rea t ed to satisfy a p recond i t ion DateAppointment-3 of a previously g e n e r a t e d activity B. Since the H o t e l R e s e r v a t i o n - 4 activity App l i c a t i on - 2 should satisfy the pre-

condi t ion of P e r m i s s i o n - 1 it has to be exe- 3.2.2. Sequencing activities cu ted first. T h e r e f o r e P e r m i s s i o n - 1 is in-

To get a task p lan also the o r d e r of execut ion se r t ed as a value to the slot S u b s e q u e n t A c t i v- has to be a r ranged . Execu t ion o r d e r is a par t ia l i t y of the app l ica t ion activity. order . Fo r two activit ies A and B we say that The second way to f ind a rule o rde r ing is via A < B, if A has to be execu ted be fore B. A task sequencing rules. App ly ing the sequencing rule in p lan is r e p r e s e n t e d implici t ly by the slots S u b s e- sect ion 3.1.2 der ives that P e r m i s s i o n - 1 -< q u e n t A c t i v i t i e s and P r e c o n d i t i o n s link- O a t e A p p o i n t m e n t - 3 . Fig. 5 shows a graphica l ing two subsequen t activities. If A -< B, then the r ep re sen t a t i on of the resul t ing task plan for our goal of activity A is a d d e d to the p recond i t ions litt le example . In the object descr ip t ions only the list of B and the name of B is inser ted into the slots de t e rmin ing the execut ion o rde r are pre- slot s u b s e q u e n t A c t i v i t i e s of A. Concur - sented.

rency is a basic p rope r ty of office work, which has It should be especia l ly emphas i zed that the to be taken into account dur ing task p lanning , as task genera t ion phase cons iders the ac tual s i tua- desc r ibed in [SR86]. So some condi t ions - ca l led tion. D e p e n d i n g on the context at s tar t ing t ime 'p reva i l condi t ions ' - must hold for the whole tasks are p l a nne d and execu ted in d i f ferent var ia-

Unit: Application-2 Goal: (the Application of LectureDagobert is proposed) SubsequentActivities: Permission- 1

Unit: Permission-1 Goal: (the Permission of LectureDagobert is 7yes.or.no) Preconditions: (the Application of LectureDagobert is proposedl

I SubsequentActivities: DateAppointment-3, HotelReservation-4

mt~zoatne~iPoP~n.Yl~:pe3rmission of LectureDagobert is permitted)

Unit: HotelReservation-4 Preconditions: (the Permission of LectureDagobert is permitted)

Fig. 5. Activity ordering by P r e c o n d i t i o n s and S u b s e q u e n t A c t i v i t i e s .

264 K. Hinkelmann, D. Karagiannis / Context-sensitice office tasks

tions. Fig. 6 shows two distinct plans of the same tion time more actual data are present. Thus, as task for inviting a guest lecturer. Both tasks had an advantage of this hierarchical planning we get been initiated by a project leader so that the first an increase in flexibility. By the way the task step of the task (see fig. 1) was superfluous, planning is simplified, because fewer actions have Additionally the task initiator of plan (b) did to be analyzed for execution ordering. some actions by himself before starting the sys- tem. As the lecture should take place within 5 3.4. Executing context-sensitiue tasks days the task initiator personally contacted the director for giving his permission. As a conse-

When the task generation is finished, those quence the activities of permission and date ap- pointment are not included in the task. Another actions with satisfied preconditions will be acti- simplification of plan 2 has its origin in the fact vated. First, outstanding information is derived: that the lecturer had a short journey and thus the the office worker responsible for execution - the action for hotel reservation was dropped, agent - is determined, the documents and forms

are made available, the deadline can be com- puted etc.

3.3. Hierarchical planning

3. 4.1. Fine planning In the first phase of task generation only activ- When a composed activity has to be executed

ities immediately contributing to predefined goals the subactivities are computed in a fine planning had been created. Subactivities of composed ac- phase: tivities are not yet created. This is delayed until fine planning while executing the activity. Gener- 1. A new instance is created for every activity ating activity instances as late as possible will class listed in the slot Sub a c t i v i t y - result in more suitable plans, because at execu- C t a s s e s .

Proposal-30

I Permission-29

/ \ Invitation-43 DateAppointment-17

/ \ HotelReservation-54 Announcement-63

I Reimbursement-45 (a)

Invitation-83

/ \ Reimbursement-87 Announcement- 98

(b)

Fig. 6. Two plans for the task of inviting a guest lecturer.

K. Hinkelmann, D. Karagiannis / Context-sensitice office tasks 2 6 5

2. Generating activities for subgoals proceeds in also want to see his personal calender for enter- the same way as described in section 3.2 for ing the lecture data etc. the activity's main goal. As soon as the execution of an activity is

All these subactivities are recorded in the slot finished, its subsequent activities are analyzed S u b a c t i v i t i e s of the superior activity and the whether their preconditions are satisfied. All sub- execution order is committed. Activating the first sequent activities with satisfied preconditions are

activated. Subsequent activities with precondi- subactivity can recursively start another fine plan- ning phase, if it is a composed activity itself, tions that are still unsatisfied are delayed. In the

task generation phase, however, for every precon- Looking at our example, the fine planning for the activity P e r m i s s i o n - 1 could find the following dition in every activity instance a particular action

had been generated. Additionally all actions are informations with new subactivities F i l l i ng- linked by the slot S u b s e q u e n t A c t i v i t i e s .

Form-5 and Notification-6: Therefore for every precondition of an activity A

Unit: Permission-1 there exists an activity B < A and A is a Subse - InstanceOf: Permission Agent: John quentAct i v i ty of B. As a consequence, aria-

Documents: Form-125 lyzing only the subsequent activities of finished Guidelines: PermissionGuidelines actions will ultimately find all executable activi- S u b s e q u e n t A c t i v i t i e s : u n k n o w n ties. Task execution stops, if all activated activi- Object: LectureDagobert ties are executed and no further activities with G o a l : ( t h e P e r m i s s i o n o f L e c t u r e D a g o b e r t

is ?y) satisfied preconditions do exist.

Preconditions: (the Application of Lec-

tureDagobert is proposed)

Subgoals: (the applicant of LectureDago- 4 . Acting by opportunity bert is informed)

SubactivityClasses: FillingForm S u b a c t i v i t i e s : F i l l i n g F o r m - 5 , Following the approach of generating a n d e x e -

No t i f i c a t i o n - 6 cuting c o n t e x t - s e n s i t i v e o f f i c e t a s k s a s d e s c r i b e d

in the previous chapter, an office worker has the 3.4.2. Actiuity execution possibility to perform his tasks whenever he wants.

For activities we have to distinguish between However, this does not give him full individual system activities and jobs that are to be done by freedom in doing his work as it is possible in the office workers. System activities are carried out real office domain. The generated activities are like programs as soon as possible. If the activity fixed, they can not be deleted or replaced. As has to be executed by a human agent, this person soon as an agent starts a job the corresponding is sent a request. Every office worker has a per- method is executed. This method is a predefined sonal agenda containing all his actual jobs. In sequence of activity steps that are to be per- principal he can perform his jobs at a time he formed with all the drawbacks of the first ap- likes. Only if there is a deadline for an activity proach. The office worker may only interrupt and the system will send the agent a reminder. When delay them to procure needed information. The the agent has decided to attend to an activity, the advantage of this approach compared to the de- start method of the activity object is executed, tailed definition of the whole plan structure

In our example, John is the agent of the (chapter 2) is that the exceptions have to be action Per m i s s i o n - 1. Therefore the action is anticipated only at the level of activities instead added to his agenda. As soon as he starts the of the level of the whole task. action a text system presenting the application To give an office worker full individual free- form F o r m - 1 2 5 will be opened. In this case dom in doing his work he should be able to J o h n has the possibility to ask for information change the order of activity execution in a task, to about the current status of the task and about all replace a whole set of actions, to add new activi- the data necessary to do his job. In the case of ties to achieve new goals etc. This presupposes, P e r m i s s i o n - 1 it may be necessary for J o h n to however, that the system has any knowledge about know, how much fee the guest lecturer should the semantics of activities. The definition of get, how many other lectures are planned, he may methods alone does not suffice. If the office

266 K. Hinkelmann, D. Karagiannis / Context-sensitive office tasks

worker does his own var ia t ions of the g e n e r a t e d office. This forced us to the th i rd layer ca l led task plan, it may h a p p e n that this p lan will be- ac t ing by oppor tun i ty , whe re any office worke r come incons is ten t with the sys tem's in terna l s tate , can in te rvene in the execut ion of a task unless he In this case the system must de t ec t whe the r some passes his compe tences . p recond i t ions have a l r eady been sat isf ied by a user and then modify its p lan to reach a consis-

ten t state. This can be achieved by r eo rde r ing References activit ies, re jec t ing prev ious assumpt ions , or

d r o p p i n g goals or actions. [CL881 W.B. Croft and L.S. Lefkowitz. A Goal-Based Repre- A s s u m e tha t the task p lan of inviting a guest sentation of Office Work. In W. Lamersdorf, editor, Of-

l ec turer as shown in fig. 1 had been gene ra t ed , fice Knowledge: Representation, Management, and Uti- Now imagine tha t the d i rec to r may in te rvene in lization. Elsevier Science Publishers B.V. (North Holland),

IFIP, 1988. the execut ion: To pe rmi t the invi ta t ion he may offer the l ec tu re r an overal l amoun t for fee and [EL88] D. Engelbart an.d H. Lehtman. Working Together.

BYTE, December 1988. r e i m b u r s e m e n t of t ravel expenses , but u n d e r the [FW891 H. Fleischhack and A. Weber. Rule Based Program- condi t ion , tha t the guest has to reserve and pay ruing, Predicate Transition Nets and the Modeling of the hote l room by himself. In this case the ac t ions office Procedures and Flexible Manufacturing Systems. Hotel Reservation and Permission are c o m b i n e d Bericht TI 3, Universit~it Oldenburg, 1989.

[Hun88] L. Hunter. AI Attitude and Techniques Informing a l though they are not subsequen t in the plan. c s c w . Artificial Intelligence Research, 1988. The system has to de tec t this var ia t ion and de le te [Kar89] D. Karagiannis. Flexible BiJrosysteme (FBS) - Ar- the ac t ion Hotel Reservation f rom the plan. chitektur und Einsatzm6glichkeiten. In S. Fuhrmann and

T. Pietsch, editors, Praktische Anwendungen moderner Biirotechnologien, Band 12. Erich Schmitt-Verlag, 1989. in German.

5. Conc lus ion [Kem88] C. Kemke. Darstellung von Aktionen in Vererbung- shierarchien. In W. H6ppner, editor, Kiinstliche Intelli-

In this p a p e r we p r e s e n t e d th ree layers to genz, GWAI-88. Springer-Verlag, 1988. in German. achieve full f lexibil i ty in sUpport ing task process- [KK87] A.K. Kaye and G.M. Karam. Cooperating ing in a real office domain . The s imples t ap- Knowledge-Based Assistants for the Office. ACM Trans-

actions on Office Information Systems, 5(4), 1987. proach is the p rede f in i t i on of a s t ruc tu red p lan [LHWM88] F.H. Lochovsky, J.S.. Hogg, S.P. Weiser, and A.O. for each task to be suppor ted . This a p p r o a c h has Mendelzon. OTM: Specifying Office Tasks. In R.B. Allen, poor possibi l i t ies to reac t on any except iona l editor, Conference on Office Information Systems, Palo cases, because it seems imposs ib le to an t ic ipa te Alto, California, 1988. all of t hem at def in i t ion t ime. G e n e r a t i n g a con- [Mor89] K. Morik. Sloppy Modeling. In K. Morik, editor,

Knowledge Representation and Organization in Machine text-sensi t ive p lan when s tar t ing a task can take Learning. Springer-Verlag, 1989. into cons ide ra t ion all the except iona l in fo rmat ion [SR86] E. Sandewall and R. R6nnquist. A Representation of known at that t ime. A system has been imple- Action Structures. In Proceedings of the National Confer- m e n t e d as a " d e m o n s t r a t o r " accord ing to this ence on Artificial Intelligence (AAAI-86), 1986. methodology . It was deve loped on a works ta t ion [TFGN87] D. Tsichritzis, E. Fiume, S. Gibbs, and O. Nier-

strasz. KNOs: KNowledge Acquisition, Dissemination, and unde r U N I X by using the Knowledge Eng inee r - Manipulation Objects. ACM Tansactions on Office Infor- ing Env i ronmen t KEE. 4 F o r this system a simu- mation Systems, 5(1), 1987. la t ion tool has also been rea l ized . Thus, we got [TLF88] M. Tueni, J. Li, ad P. Fares. AMS: A Knowledge- empi r ica l resul ts about the u t i l iza t ion fac tor of based Approach to Task Representation, Organization the working p laces and resources in the office and Coordination. In R.B. Allen, editor, Conference on

Office Information Systems, Palo Alto, California, 1988. envi ronment . Ana lyz ing these resul ts we found [Wim89] K. Wimmer. An Approach to the Representation of that giving the office workers more f r e e d o m in Offices. PhD thesis, Bundeswehrhochschule Miinchhen, doing the i r jobs would be more effective and 1989. could be more satisfying for them, thus also lead- [WK851 G. Woetzel and Th. Kreifelts. Die Vorgangssprache ing to a be t t e r accep tance of the system in a real CoPlan. WlSDOM-Forschungsbericht FB-GMD-85-92,

Gesellschaft fiir Mathematik und Datenverarbeitung mbH, St. Augustin, 1985. in German.

[WL87a] S.P. Weiser and F.H. Lochovsky. OZ+: An Object- 4 KEETM is a traden]ark of IntelliCorp Inc. oriented Database System. In F.H. Lochovsky, editor, Of-

K. Hinkelmann, D. Karagiannis / Context-sensitiue office tasks 267

fice and Data Base Systems Research '87. University of editors, Office Systems: Methods and Tools. IFIP, Elsevier Toronto, 1987. Technical Report CSRI-195. Science Publishers B.V. (North-Holland), 1987.

[WL87b] C.C, Woo and L.H. Lochovsky. Integrating Proce- [Zis78] M.D. Zisman. Use of Production Systems for Model- dure-Automation and Problem-Solving Approaches to ing Asynchronous Concurrent Processes. In Pattern-di- Supporting Office Work. In G. Bracchi and D. Tsichritzis, rected Inference Systems. Academic Press Inc., 1978.