a distributed knowledge-based approach for planning and controlling projects

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL 2 3 , NO 6, NOVEMBERIDECEMBER 1993 1537

A Distributed Knowledge-Based Approach for Planning and Controlling Projects

Ai-Mei Chang, Andrew D. Bailey, Jr., and Andrew B. Whinston

Abstract-Classical techniques used in project planning and control generally lack the required functionality to manage large-scale projects characterized by a dynamic environment and uncertain and incomplete information. In such situations, conventional planning and control techniques generally do not provide the necessary functionalities to react effectively to changes or to provide timely feedback. In order to overcome some of these problems, we propose a distributed knowledge- based approach for planning and controlling large projects. We show how a model of truth maintenance can be effectively combined with existing planning and scheduling tools to allow for incomplete plans and for effective updating and control as more knowledge becomes available. Our model is particularly well suited for supporting the extensive collaborative process among project participants that characterizes a large project.

I. INTRODUCTION N recent years, the rapid rate of change in both tech- I nology and the marketplace has created enormous strain

upon conventional ways of managing large projects such as a product development project. Increased competition and changing technology is forcing product development into shorter time-frames. This has increased the emphasis on better management and team work for better technical support. Concepts such as “concurrent engineering” have emerged which focus on reducing the lead time between product conception and its eventual commercial introduction. Rapid changes in environment, coupled with uncertain and incomplete information, are forcing managers to plan and control effectively under time pressure. In such situations, conventional project planning, scheduling, and controlling techniques do not generally provide the functionalities needed to react effectively to changing events or to give timely feedback [ 11. Furthermore, they are not easily amenable for supporting the collaborative process that characterizes project management. Taking into account all these requirements, it is essential that for effective project management newer techniques be developed that tightly integrate planning, scheduling, and control functions in a collaborative environment.

Manuscript received March 17, 1992; revised February 17, 1993. A. Chang is with the Department of Management Information Systems,

A. D. Bailey, I r . , is with the Department of Accounting, University of

A. B. Whinston is with the Department of Management Science and In-

IEEE Log Number 9212931.

University of Arizona, Tucson, AZ 85721.

Arizona, Tucson, AZ 85721.

formation Systems, University of Texas at Austin, Austin, TX 78712.

We propose a distributed knowledge-based approach for planning and controlling projects that overcomes some of the limitations of the conventional techniques. Central to the proposed system is the truth maintenance model which has been effectively combined with existing tools for planning and control to allow for incorporation of incomplete plans in the system and for efficient updating of these plans when more knowledge becomes available. The model proposes a systematic method to take into account the interdependencies between the various project partic- ipantddepartments and increases efficiency of planning and control by allowing for concurrent processing by project participants. The model supports distributed planning and control functions and the negotiation process between project participants in order to resolve conflicts. In what follows, we describe the major characteristics of large projects and how our approach is particularly well suited to support them. In the process, we also outline the previous research in this area and position our efforts with respect to it.

One of the important aspects of a modem-day project is the large number of activities that it comprises. With the number of activities exceeding ten thousand, it is difficult for an individual manager to keep track of all the activities. The representation of such activities using traditional PERT/CPM-based models also becomes cumbersome. In addition, a project involves the participation of a large number of departmentdindividuals, each with differing goals. Cooperation between departments is essential as there are many interdependencies involved in the planning and execution of a project. With the changing environment that is characteristic of today’s marketplace, accurate planning becomes difficult. Changes to plans are frequent and may involve cooperation between many departments. Under such situations, traditional techniques such as PERT/CPM-based models fail. These techniques assume that activities can be identified as entities, with clear beginning and end points for each activity. This is not possible when information is incomplete. They require activity sequence relationships, which are not easy to obtain in the earlier planning stages, as many of them are conditional on others. Their requirement that project control should focus on the critical path is difficult to implement with incomplete information. Making frequent changes are difficult and may cause confusion. In addition, a major criticism is that these models remove a good part of the managers’ power and ability to make decisions

0018-9472/93$03.00 0 1993 IEEE

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relegating them to the status of observers [l] . Although many variations have been suggested to the PERTICPM model [2] most of them share the above common limitations.

The application of artificial intelligence and operations research techniques for manufacturing planning and scheduling problems [3], 141 and to project management [5], have provided new ways of overcoming the limitations of traditional techniques. The design and prototype development of Callisto, an intelligent project manager system 151 provided rule-based project management expertise and knowledge to support managers. The exten- sion of Callisto (Mini-Callisto) developed a framework for distributed project negotiations and comparative analysis. There are many experiments that are being carried out by researchers (e.g., [6]) to validate the constraint- directed negotiation approach in real project situations. Other researchers have proposed similar knowledge-based approaches for managing change in a collaborative environment 171. Within the framework developed by Sathi, Morton, and Roth [5] there are alternative ways to accom- plish the goals of project management. Our research sug- gests one such alternative using a truth maintenance model. Our research also has a more restricted, albeit focused, domain as we develop a model for activity planning and control. Nevertheless, we illustrate how it can be adapted to the Mini-Callisto framework. We also com- pare our approach to other related work in the area of distributed cooperation and negotiation in the latter part of the paper.

Our distributed knowledge-based approach views project management as a collaborative process involving many distributed participants such as organizationally distinct departments (cf. [8], [9], [lo]). Each participant may have differing goals and attitudes. While the traditional PERT/ CPM model views the project as a monolithic network of activities with prespecified durations, our approach views the project as a cooperative process among participants with distinct individual plans and responsibilities. The break up of the project among these participants will depend on the area of expertise of each participant (usually along departmental lines). While slack times are generated for various activities/participants using PERT/CPM techniques, in our approach slack times are allocated using negotiations among participants. It has been observed that, in practice, plans evolve through negotiations and are not prespecified 151. In keeping with this observation, our model prescribes a skeleton plan for each participant organized using a truth maintenance system. Based on negotiations among the participants, the plans are made more concrete.

Development of skeleton plans for each participant, as a basis for subsequent negotiation, is complicated by the presence of interdependencies between participants’ plans. For example, the engineering department requiring a particular component for a task, may need to check with the purchasing department for the supply lead time. The purchasing department may not have this information

readily available until their order plan is ready. In such cases, the skeleton plan development in the engineering department may be delayed until the necessary information is received. Such delays are usually inefficient and are to be avoided. Delays are also common in traditional activity networking with PERT/CPM where incomplete information can cause problems.

The interdependencies between participant plans and incomplete information are handled in our model by incorporating default assumptions as a part of individual participant plans. Thus, the engineering department requiring the lead time details for a part, may simply assume a nominal value based on experience without wait- ing for this information to become available from the purchasing department. Some of these assumptions are default in the sense that the participant generally does ex- pect to receive any evidence to the contrary from the other participants. Thus, incorporation of assumptions obviates the need for frequent interactions between participants and eliminates the consequent delay. It also provides a convenient means of handling incomplete information. While participants can proceed with the development of their plans with the presumption that exception to the default assumptions will not occur, this will not be true for all assumptions. Therefore, a cooperation process between participants is necessary to confirm or negate the assumptions. I When such exceptions do occur, the participants will have to retract their assumptions and update the plans accordingly. Thus, while incorporation of assumptions is necessary and useful for efficient concurrent development of plans and for handling incomplete information, later, the participants will interact among themselves and in the process cover what they consider as critical assumptions with information from other participants.

In addition to making assumptions, participants incor- porate concrete facts (such as resources that are ear- marked for the department’s use), observations (a particular activity is already completed), use rules (precedence constraints) and techniques (such as PERT/CPM models, Gantt charts applied at the participant level), and arrive at certain propositions regarding the completion date of their series of activities. These skeleton plans provide the basis for subsequent negotiations when the slack times are negotiated. During this process, assumptions are also confirmed or negated through the exchange of information. A basis for concrete participant plans emerge from the justifications and assumptions made during these negotiations. (A traditional monolithic PERT/CPM model generally does not provide such support and, hence, is unsuitable for large projects.) We model the process of developing participant level plans using the assumption- based truth maintenance system (ATMS) [ 1 11. The ATMS provides a reasonable means for retracting assumptions when faced with contradictions and for maintaining consistency in a participant plan. It provides a systematic way

‘Some of the assumptions may have a high likelihood of confirmation, hence. the term “default.”

CHANG el a l . : DISTRIBUTED KNOWLEDGE-BASED APPROACH FOR PLANNING AND CONTROLLING PROJECTS

of updating plans based on new information. In addition, the ATMS can maintain sets of assumptions under which a proposition regarding the completion date of an activity can be believed.

Thus, the distributed knowledge-based approach proposed allows participants to develop plans efficiently without complete information and without having to interact frequently with other participants. It also allows the representation of incomplete information in an efficient manner. However, the ultimate integration of the plans requires cooperation among the participants. This cooperation is necessary to: cover the assumptions with infor- matiodfacts from others, identify the specific areas of conflict in plans, negotiate, and lay down justifications and assumptions for further refinement of plans. Depend- ing on the complexity of the project, such cooperative efforts could be a routine affair. The communication and negotiation process can be achieved using the Mini-Cal- listo framework. In addition to presenting the cooperation framework, we detail the technical aspects of implementation. We have developed a preliminary prototype system using the programming language C, for developing plans using the ATMS and for acting as a decision support system allowing the participants to: store knowledge (plans and assumptions), retract assumptions if contra- dicting information is obtained, and update plans.

The distributed knowledge-based approach can benefit project planning and control in the following ways:

It provides for efficient concurrent development of plans by individual participants by organizing the participant knowledge bases in terms of assumptions, facts, and rules in a consistent manner using ATMS; it permits easy retraction of assumptions and update of plans. It allows for incomplete plans to be incorporated at each participant level through assumptions; when more knowledge is made available, these assumptions are either confirmed or negated and plans are updated accordingly. It illustrates the inductive and deductive reasoning necessary to achieve the completion of a critical activity within a given time frame; ATMS can also maintain assumption sets under which a particular scenario can be believed. It formalizes the process of negotiation and conflict resolution and provides formal explanation/justification for the consensus overall plan that is evolved through negotiations. It allows conventional techniques to be incorporated as “solvers” to answer key “what-if” questions such as: what happens if some major assumption is violated? How do we quantify the project’s major elements? etc. It forms a part of the project management information system to support the overall planning, scheduling, and control of projects.

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In the next section, we examine the individual participant’s process of developing a plan using the augmented- ATMS model. In Section 111, we provide some illustra- tions of plan development and plan updating using the ATMS model. Section IV presents issues involved in participant cooperation and the framework for cooperation. In Section V, we relate our application to other similar research in the area of distributed cooperation and negotiation, and conclude with a discussion on our continuing research.

11. ATMS MODEL OF A PARTICIPANT PLAN Before developing a model of a participant plan, it is

necessary to understand the planning process and the key information that is used in developing a plan. We assume for our discussion that a participant represents a department and that each participant knows what its goals and objectives are in relation to the overall project. A participant plan should outline the activities that must be accomplished in order to meet the participant goals. The key information needed in developing a plan are: 1) knowledge of activities and their relationships 2 ) available resources that can be allocated for the different activities, 3) time required to complete each activity conditional on the resources allocated to each activity, and 4) activities that interface with other participants’ activities and their relationships. Prior to negotiating with other participants, much of the above information may be uncertain, incomplete, or unavailable. However, this should not preclude the participant from developing a skeleton plan as a basis for negotiation. Some information may be certain and complete, such as, available resources in terms of manpower and money which may have already been ear- marked for the participant. These are facts that can be relied upon with great accuracy and will be treated as such in the model. For incorporating uncertain information, participants can make reasonable assumptions regarding details that are incomplete or unknown. For example, regarding additional manpower yet to be allocated among the participants, the participant may assume that it will get 25 % of the total available resource. Furthermore, assumptions can be made in situations where the participant may have to wait for significant periods before obtaining information on an activity that interfaces with another participant. For instance, the engineering department may assume, without having to interact with the purchasing department, that the lead time for obtaining a custom- made part needed for an activity is six weeks.

Such assumptions are important for several reasons. First, they affect the plan that is developed. Retracting these assumptions may change the plan. Second, they are a necessary part of concurrent development of plans by the different participants.. Third, all critical assumptions have to be reconciled among the participants before a concrete and final plan emerges. In addition to these assumptions and facts, a participant can use inference rules derived from prior experience, and/or operations research

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techniques, to arrive at propositions regarding the completion of various activities and attainment of goals. For example, based on the resources available (fact), and activity knowledge (fact), and resource allocation (assumption), a participant can arrive at a proposition regarding the number of days it will take to complete the activity. Further, based on these derived propositions, activity sequences (fact or assumption, as the case may be), and assumptions regarding the interface activities, a proposition regarding the completion of all activities can be derived using PERTICPM techniques. Thus, the rule base can consist of rules derived from experience, formulas, or rules derived from conventional operations research techniques (solvers).

We model the process of participant plan development and organize the knowledge of a participant using the augmented-ATMS [ 1 11. The ATMS is a representation scheme for storing reasoning knowledge about various propositions that an agent derives. An agent, in this context, could be departmental planners or computers. As- sociated with each proposition is an assumption set, called an environment, that contains the minimal assumptions under which the proposition can be believed. The ATMS uses rules as its elementary units of knowledge and draws conclusions by combining rules to form explanations. If there is a new observation or information that conflicts with a proposition that has been amved at, based on previously held assumptions, the ATMS calls for a revision of the appropriate assumption set in such a way that the result is most plausible and in agreement with the updated information.

Organizing the knowledge of a project participant, using the ATMS is useful because it formalizes the planning process of the participant. Unlike many of the conventional PERTKPM-based models, the ATMS focuses on all important aspects of the project such as resources, relationships with other participant activities, activity duration and its relationship to resource allocation, and the rules used to derive propositions. It forces a participant to take a more complete view of the activities and differen- tiates between facts and assumptions. More importantly, it focuses on the aspects of a project that are negotiable among participants and models the relationships between those aspects and the plan so that participants are always aware of the implications of their compromises and com- mitments on the plan.

Our model consists of the following parts: a rule base, { K } , consisting of causal and logical rules, a premise set, { I } , consisting of concrete facts and empirical information, and an assumption set, { Z } , containing the assumptions of the participant. Based on these model components, the propositions, { P } are derived. For each propositional node in the system, the ATMS maintains a list of minimal sets of assumptions (environments), called the label L ( P ) , under which the corresponding proposition can be proved or explained. The label L ( P ) can yield only three possible truth values for the proposition P : believed, disbelieved, and unknown. If any environment in

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L ( P ) is believed, then P is believed; if any environment in L( 1 P ) is believed, then P is disbelieved; if we can confirm neither L(P) nor L( 1 P ) , then P is unknown. The ATMS can perform three useful functions in organizing a participant’s knowledge:

Producing Explanations: Once a proposition, P , is believed by the participant, the ATMS can retrace the justification paths and identify the argument or proof justifying that belief, as well as the assumptions upon which it is founded. This is a useful func- tion as a participant can readily recall all the assumptions on which a particular completion time of an activity depends. Managing Conflicts: Contradictions between the derived propositions (such as completion time of activities) and what is demanded during the negotiations (such as advancing the completion times of activities), are viewed as signals that the currently held set of assumptions should be modified. New sets of assumptions that are compatible with the negotiated position and maximal (i.e., containing a minimal set of exceptions) are then generated using the ATMS. Guiding the Acquisition of New Information: If a certain proposition is in an unknown state, then the label L ( P ) provides clues as to the information required to render it “believed” or “disbelieved” That is, if a confirmation of assumption Z is all that is missing from one set in L ( 1 P ) , while the confirmation of i Z is missing from some set in L ( P ) , then a test leading to the confirmation or denial of Z should be devised. Therefore, the ATMS provides clear guidelines for gathering additional information to render the plan complete or fully specified.

In the next section, we describe a project which involves cooperation between several departments (participants) and illustrate how an ATMS model can be used to develop participant plans.

111. DEVELOPMENT OF PLANS A . Description of Project

We describe a project adapted from a Harvard Business School case [12] which outlines a plan to launch a campaign to push the sales of an industrial machine. The project involves cooperation among three major departments-Sales, Marketing, and Purchasing and can be roughly broken down into three corresponding categories: 1) the training of sales personnel, 2) consultation with and training of marketing personnel, and 3) preparation of necessary advertising and instruction material for the campaign. Fig. 1 presents the major activities that need to be performed by the different departments. A detailed description of the activities follows.

The training of sales personnel is the responsibility of the Sales Department. It starts with the preparation in Phase 1 of the training program for salesmen. Meanwhile, sales managers will select the sales personnel who are to

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SALES MARKETING

I

I I 1 I

Fig. I . Individual participant activities and interaction.

be trained. Following their selection, the chosen sales personnel must be relieved of their responsibilities in their areas and sent to the company's training center in the home office. This has to be coordinated so that the salesmen arrive just as Phase 1 of the training program is ready. While the salesmen are being trained in Phase 1 of the program, Phase 2 will be prepared. As soon as the salesmen's training in the first phase is completed and Phase 2 of the training program is ready and approved by senior management, sales training in the second phase can com- mence. The senior management cannot give approval for the second phase until the Marketing Department draws up their general marketing approach.

At the conclusion of the two major phases of their training, the sales personnel will be issued a "Customers In- struction Manual" on the new machine, and will spend a short time at the home office becoming familiar with it. The ''Customers Instruction Manual" will be made available by the Purchasing Department. After the salesmen are familiar with the manuals, they will return to their respective territories ready to begin their effort simulta- neously with the national advertising campaign.

The Marketing Department will first determine the general marketing approach. When this has been arranged, the necessary marketing personnel will be selected and brought into the home office. While the marketing train- ees are being selected and brought in, specific training plans for the marketing personnel will be consolidated. After these plans are consolidated, a familiarization course for these personnel will be designed. When personnel and course are ready, the training of marketing personnel will proceed. The advertising plans are consolidated once the general marketing approach has been determined. When this consolidation is complete, a paper is to be prepared and printed in a professional journal. Also, immediately following the consolidation of advertising plans, national advertising must be prepared, approved, and distributed to the appropriate media. The national advertising will be released and carried by the media only after the marketing people are trained, the professional paper is published,

and the advertising is distributed. The campaign will proceed only after the salesmen return to their territories.

After the advertising plans are consolidated, a general brochure will be drafted and prepared by the Product De- velopment Department in consultation with the marketing personnel. This will be approved by senior management. Following the approval, the material will be delivered to the Purchasing Department for vendor selection, layout design, and printing. As soon as the brochure is approved, the Product Development Department will prepare a "Customers Instruction Manual," which, when approved, is turned over to the Purchasing Department for printing. Purchasing will ensure that the copies of the manual are sent to the training center for use by salesmen. They will also ensure that the brochure and manual are printed, packaged together, and delivered to the Market- ing Department for general distribution. Actual implementation of the campaign begins only after salesmen are in their territories, the national advertising campaign is released, and the proper brochures and manuals have been received by the Marketing Department.

B. Planning Process There are three important aspects of a multi-participant

cooperative planning process that need special focus: 1) the way in which the project workload is distributed among the participants, 2) the manner in which participants develop their individual plans, and 3) how individual plans are integrated in a cooperative manner to develop the overall plan. We will examine these issues using the illustrative example we discussed earlier and show how the planning process is formalized through our modeling efforts.

In developing the individual participant plans we assume that the participants have already met previously to discuss the overall objectives of the project. The distribution of workload may not pose special problems if the workload is so distributed that it matches the expertise of each department.2 In our example, we assume that the workload is distributed along the departmental lines and that each department is aware of its goals in meeting the overall objectives. They also have knowledge of the interactions they may have with other participants. Depend- ing on the nature of the project (size and complexity), each participant may have complete or incomplete knowledge of all the activities it has to perform. Whatever the actual case, we assume that at the very least, each participant knows the major activities it has to perform to ac- complish its goals. Thus, each participant should be able to list the major activities (without pre-specifying any sequence) it needs to perform, along with the possible interactions it may have with other participants as shown in Fig. I .

'If there are no clear lines of demarcation of responsibilities and activities, a special project team (a participant), consisting of different departmental personnel, may be created to handle such issues. Since such organizational concerns are beyond the scope of our work, we assume that the responsibilities are clearly delineated.

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The planning process begins with the development of individual participant plans. Fig. 2 is an ATMS representation of the Sales Department's plan. The ATMS dis- plays all major facts, observations, assumptions, and rules that are used in arriving at the skeleton plan. For example, proposition P I , that preparation of the Phase 1 program will be completed in eight days from start (time 0), depends on: the fact that there are four training staff available to do the job (ZJ, the fact that past training material is available to make the job easier (Z,), and the important assumption ( Z , ) that the Product Development Depart- ment will provide two personnel to assist in developing the program. The Sales Department considers it likely that they will get assistance from Product Development and are building their plan on that assumption, without wait- ing to get the confirmation from Product Development. This assumption avoids delay in formulating the plan. Similarly, the proposition P 3 , that selected personnel can be delivered to the home office within six days from start (time 0) depends on the proposition Pz, that selection of personnel can be done in two days and on the assumption Z 3 , that the selected personnel can be relieved of their responsibilities in their territories immediately upon selection. Assumption Z3 is an important assumption that affects P 3 . While, unlike Z , , it can be confirmed or negated totally within the Sales Department; the confirmation can be done only after the personnel are selected. Thus, the information which is incomplete at this point in plan development, is handled using an assumption.

Proposition (P6) , that the Phase 2 training program will be completed by 19 days from time 0 depends on: the required staff being available (Z,), Phase 1 training program being completed by day 17 (P4) , preparation of Phase 2 being completed in 17 days (P5), senior management approving the second phase (&) , and the general marketing approach being completed prior to the start of Phase 2 (Z7) . Assumption Z7 has to be confirmed with the Marketing Department. However, this activity is a part of Marketing's plan and the completion time will not be known until Marketing develops its plan. Similarly, completion of the salesmen familiarization program within 24 days (P7) depends on Purchasing delivering the Customer Instruction Manual before day 20 ( Z 8 ) . Assumption Z , is the subject of the Purchasing Department's plan, which itself is very much dependent on Product Development's and Marketing's plans. Thus, interdependencies between participant plans are effectively handled using assumptions which allow the participants to put together their skeleton plans conditional on these assumptions. Later, when the participants negotiate on resource sharing and the slack times for their activities, and cooperate on arriving at an overall plan, these assumptions will be covered with information from other participants. The Sales De- partment, finally, arrives at proposition P,, that the trained salesmen will be in their territories and ready for the campaign within 25 days from time 0.

The rules that are used in the ATMS representations are either formulas or derived from experience or operations

::E: Fig. 2. ATMS representation of Sales Department's plan

research techniques. For example, rule K , takes into account the available resources and the work content of the Phase 1 training program and arrives at the number of days required for the activity. The participant could use a formula to determine this value or simply estimate the value from experience. Since the skeleton plan, conditional on the assumptions, is quite detailed, we could use PERT/CPM techniques to arrive at the completion time of activities and to determine the overall completion time of the last activity. Thus PERT/CPM can be used to derive the subsequent rules and the critical activities. The slacks determined using this method can be used for subsequent negotiations. Thus, once the ATMS representation is developed, OR techniques could be effectively used as "solvers" to arrive at further propositions (such as, slack time for activity A is two days).

Another interesting aspect of the ATMS representation is that resources are explicitly included as input to the plan. Therefore, "solvers" can be used to relate the effects of changes in resource levels to changes in activity duration (effects of crashing). More importantly, it is useful to know what changes are necessary in the assumption set to believe a proposition that an activity can be completed in "X" days. An advantage of the ATMS is that it can maintain different assumption sets (contexts) under which a proposition can be believed, rather than forcing the knowledge base to be consistent by immediate updating as in a conventional truth maintenance system. Thus,

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when a proposition regarding the completion time of an activity is confronted by a different demand during the negotiations, solvers can be invoked to generate a new set of assumptions under which the new completion time is possible. Using the ATMS, it is possible to generate different scenarios for the plan and each scenario would be supported by its set of assumptions and justifications. Thus, it is useful for evaluating alternatives.

In developing the ATMS representation of the Sales Department, we have implicitly assumed that we have knowledge of resource allocation between activities (hence, Z2, Z3, Z4, Z,, etc.). This need not be so. The resource allocation itself could be uncertain at the time of plan development. Similarly, we have assumed that precedence relationships among activities are known (hence, P I is an input to rule K4, etc.). These relationships could also be unknown at the time of development of the plan or could be conditional on the resource allocation plan. Such uncertainties can also be handled in the ATMS representation, by using additional assumptions. Thus, P4 in Fig. 2 could depend on an assumption 2 ; which might state that training in Phase 1 follows preparation of the Phase 1 program. Likewise, default assumptions can also be made with respect to resource allocations. It should also be noted that, sometimes, it may not be desirable to have a final detailed plan. When the number of assumptions involved is large, it may not be very useful to have a detailed plan conditional on so many assumptions. In such cases, ATMS can present only the aggregate relationships between facts, assumptions, rules, and propositions on activity completion without actually providing the time reference. As more knowledge becomes available the network can be updated.

Fig. 3 is the ATMS representation of the Marketing Department’s plan. It can be seen that this plan has the fewest interdependencies with plans of other participants (assumption Z, interfaces with Product Development’s plan; Z, interfaces with senior management). The development of proposition P9, that the last activity “release of national advertising” will be completed in 34 days, depends primarily on assumptions that can be confirmed internal to the department.

Fig. 4 is the ATMS representation of the Purchasing Department’s plan. The ultimate goal of delivering the brochure and the manual to the Marketing Department within a specified time period (proposition P5), depends on several important assumptions such as: “brochure approved and delivered to Purchasing department by Prod- uct Development in ‘X’ days” (Z,) and “Customer In- struction Manual approved and delivered in ‘X’ days”(2,). Both of these assumptions, in turn, depend on Product Development’s plan. Also, since vendor selection itself depends on the kind of brochure that is designed by the Product Development Department, Purchasing as- sumes that the brochure is of a standard kind (Z , ) and proceeds with the vendor selection. Depending on whether the brochure is of standard kind or not, the prices may have to be renegotiated later. Since the plan is very much

Fig. 3 . ATMS representation of Marketing Department’s plan.

dependent on Z3 and Z,, the final time frame is not indicated. However, it is possible to arrive at a final completion time conditional on assumptions Z3 and Z,.

An attractive feature of ATMS is that it is possible to determine a measure of believability for each proposition that is arrived at, by associating believability measures 1131 with each assumption. For example in Fig. 4, we have associated the following believability measures with assumptions Z , and Z,: BeZ(Z,) = 0.9, indicating that the probability that assumption ZI is true is 0.9, and BeZ(Z,) = 0.99. Therefore, the believability of a proposition can be derived as Bef(P,) = 0.891, using laws of probability and assuming that the assumptions are independent of each other. In Fig. 4, we have derived the believability of ter- minal proposition P5 as 0.5, based on the believability values of the assumptions as indicated. The resulting measures indicate the degree of confidence that planners can place on the propositions. Participants can develop different scenarios with associated believability measures to arrive at a distribution of completion times to support negotiations. This method of obtaining probability as- sessments of plans may be more accurate than traditional PERT analysis. The reason is that in ATMS models, participants are forced to directly measure believability of assumptions (which are the sources of uncertainty in the plans), rather than estimate the distribution of completion times of activities, which can be more difficult as the as-

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Rro‘lrurc and hlPl“d dellvcred S O (0 hlkig L k y l - day5 0

Fig 4 ATMS representation of Purchasing Department’s plan

sumptions are implicit in PERT and not very easy to identify.

In the next section, we discuss the framework for multiparticipant cooperation and show how individual plans are integrated to develop the overall plan. We also provide the technical details of the components of the distributed cooperation system.

IV. MULTIPARTICIPANT COOPERATION FRAMEWORK A . Overview of the Framework

The framework for cooperation that we propose is similar to that of Mini-Callisto [ S ] and RAD [14]. Consider a group of participants working on a project. Each participant synthesizes its individual plans in terms of facts and assumptions using rules derived from experience and/or operations research techniques, and arrives at propositions regarding completion of activities and attainment of goals. This knowledge is organized in an ATMS which functions as a cache for the propositions along with the facts, assumptions, and rules. Associated with the ATMS is a library of “solvers.” Solvers are basically PERT/ CPM and resource leveling programs, which when given the appropriate input, provide information on critical ac-

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tivities, slack times for activities, start and completion times of activities, and details on “crashing.” Thus, solvers perform some of the inferencing in the system. Such inferences can be stored as rules in the rule base of the ATMS. Each participant has only a limited knowledge of the global overall plan to be developed and has to cooperate with other participants in developing the overall plan. Interaction is also necessary for confirming or ne- gating the assumptions and for updating the plans.

Each participant has an ATMS system that is net- worked across all participants through a communication interface to facilitate cooperation on the project. The communication interface acts as an intermediary between the participants (see Fig. 5). Participants can communi- cate with each other directly or through the intermediary. Communication consists of messages which can either be a query requesting a response or an assertion which may or may not result in a response. The communication interface has two important functions. First, it has a data base for storing information about the participants (metaknowledge): their identification, address, subplans they are working on, their goals, and key words associated with each subplan. This knowledge is used for senior management control of the project and to track the progress of each participant. Second, it acts as a blackboard where individual participant plans are stored and integrated to develop the overall plan. Once the plan is finalized, the interface can be used to control project execution. In case of revisions in individual plans, the overall plan stored in the interface can be updated appropriately. The interface can also have an ATMS system for this purpose.

After the development of skeleton plans, each participant sends queries to other participants using the interface to request confirmation (or negation) of the critical assumptions that were made in developing their plans. Some of these assumptions may have a high likelihood of confirmation, hence, the term “default. ” This process may result in responses from other participants depending on the development of their plans. Each participant keeps a record of the queries it receives from other participants and the answers it fumishes based on its plan. In case its plan is revised and the answer is changed, this change is communicated to the other participants appropriately. Similarly, based on the answers, each participant confirms or negates the assumptions with a pointer showing the identity of the participant furnishing the information. This process may be useful in dealing with routine assumptions that only entail information exchange for sat- isfactory resolution. However, it may not be sufficient in matters of resource sharing or slack times for activities, where the objectives of participants may conflict.

Resolution of assumptions which involve mutual trad- ing, mediation or arbitration by the senior management may require face-to-face negotiations between the project participants. The cooperation framework can be worked into a group decision support system (GDSS) framework to support this form of negotiation. During the negotiations, the ATMS model can be used to perform sensitivity

CHANG el a l . : DISTRIBUTED KNOWLEDGE-BASED APPROACH FOR PLANNING AND CONTROLLING PROJECTS I545

Participant Systcms

I ‘,COMMUNICATION I -<.

I ! \ , , , , , , % , , ! ’* - I ’ I ‘ , I I I ’

, ,

Meta-Level System Knowledge

I I I

I I

Communication Protocols

Fig. 5 . Multiparticipant cooperation system.

analysis by analyzing the impact of changes in assumptions (resources, completion dates) on the individual plans. “Solvers” can be invoked to calculate changes in slack times as a result of changes in assumptions. The ATMS model can thus be effectively used by each participant to analyze different scenarios and to support their arguments during negotiations. Based on the individual plans, the overall plan can be developed (the interface acting as a “blackboard”) and, depending on the management overall objectives, some of the individual plans may be changed to accommodate new or revised objectives. Several such sessions may be necessary during the course of the project to develop, refine, and update plans based on new information and to control the project execution.

Within the cooperation framework, it is easier to implement proper control procedures to keep the project on track. The network of ATMS systems along with the communication interface can be used as a project decision support system to review, in detail, the status of each project work package. The control is implemented at two levels-at the overall plan level and at the individual participant plan level. At the top level, the senior management can obtain a detailed appraisal from each participant as to time to complete, cost to complete, and work to complete for the major activities that show up in the overall plan. At the individual participant level, each participant clearly understands, based on the ATMS model, the factors under its control and the factors that are interde-

pendent. Therefore, each participant can implement control procedures to review progress internally within the department and implement joint procedures for controlling interdependent activities. Again, the network and the ATMS model can be used to formalize and document these procedures.

B. The Assumption-Based Truth Maintenance System Each participant’s reasoning system can be conceptual-

ized as shown in Fig. 6. The ATMS acts as a cache for rules, propositions, assumptions, assumption sets, and truth maintenance rules, The ATMS interfaces with the solvers, communication rules and protocols, and the user. The ATMS creates and maintains inference histories for each proposition using a data structure known as “node” (proposition nodes), assumption nodes, and assumption sets. Each proposition node has the following form:

[Proposition-ID, Justification, Label-Pointer, Status,

Communication-Set-Pointer]

Proposition-ID is a unique identifier of the proposition, Justification is the inference history for the proposition and points to the rule from which the proposition was derived, Label-Pointer points to the assumption sets, at least one of which must be consistent for the corresponding proposition to be believed, Status indicates whether the proposition is believed or not, and the Communication- Set-Pointer points to the list of participants to whom the proposition has been communicated. Any assertion that a participant receives from other participants (a communicated assertion), be it a proposition or a premise, is always stored in that participant’s system as a proposition, with the Justification marked as External and the Label- Pointer pointing to the participant from whom the communication was received.

The assumptions base stores each assumption node with a unique identifier, a status label indicating whether the assumption is believed or not, a justification label indicating any basis for the assumption, and a communication-set-pointer. In our implementation, premises or observations are also stored as assumptions (assumptions which have a likelihood of being true close to one). The assumptions base also contains the assumption sets for the propositions. An assumption set for a proposition points to the assumptions upon which the believability of the proposition (Status) depends. The status of an assumption set could either be Consistent or Inconsistent. An assumption set is Consistent if all the propositions derived from the assumption set are consistent with each other and with the assumptions (i.e., all the propositions can be believed at the same time). If all the propositions derived from an assumption set cannot be believed at the same time, i.e., if they are inconsistent, then the status of the assumption set is Inconsistent. If an assumption set is marked Inconsistent, then any superset of it is also marked Inconsistent. A proposition has the status “Believed” if at least one of its assumption sets is Consistent and status

1546 IEEE TRANSACTIONS ON SYSTEMS. MAN, AND CYBERNETICS, VOL. 23, NO. 6, NOVEMBERIDECEMBER 1993

1

Fig. 6 . Participant reasoning system

“Disbelieved” if all its assumption sets are Inconsistent. A participant communicates with a reasoning system

through a user interface, which provides dialog boxes to enable the input of assumptions (including premises), rules, and propositions. The input is guided through a series of queries such as “Is the input an assumption (a, a proposition ( P ) or a rule (K)?” Depending on the input, other details such as Status and Justification are recorded. A rule cannot be input until all the relevant assumptions are entered. In fact, propositions and rules have to be entered serially for proper storage of the information. Once the rules are entered, the system creates the appropriate assumption sets for the propositions.

Rules embody the inferences performed by a participant in developing a plan. For example, in Fig. 2

Rule K , : Z, A I , A Z2 --t P I

implies that if Product Development provides assistance (Z,) and past training material is available ( I , ) and four training staff are available (Z2), then Phase 1 will be completed in eight days ( P I ) . This inference could have been performed by the participant based on experience, or using a resourcing formula. Alternatively, such an inference could have been arrived at using the “solvers.” Solvers are subroutines, which, given an input argument, provide an output argument. Thus, if the participant were to provide the work content in Phase 1 in number of hours and the resource available to the solver, the solver would provide the completion time. In our implementation, we in- corporate two such subroutines: a PERT/CPM program which would provide information on start and completion times, slack times, and critical activities, and a resource leveling program for details on ‘‘resource leveling” and “crashing. ” The solvers could be invoked directly by the participant for inferencing and the resulting propositions stored in the ATMS along with the rule such as the following:

During the inferencing cycle, the ATMS generally uses rules in the rule base to perform inferences. If the rules are not adequate, the ATMS can invoke the solvers to perform required inferencing. For example, in Fig. 2, if training staff (Z2) were reduced to two, and if this new information is input into ATMS, then in the inference cycle, the rules in ATMS may not be able to generate a new proposition. In this case, the resourcing solver could be invoked to generate the new proposition involving the completion time of Phase 1.

The above discussion has, thus far, focused on the creation of assumptions and propositions which once entered in the system are never deleted. Depending on their believability, their status is changed using nonmonotonic reasoning. We use truth maintenance rules for changing the status. Any new assertion that is input into the system (either entered by the participant or communicated from other participants) may contradict the existing data base. The contradictions are of two types: contradictions of assumptions and contradictions of propositions. First, the assertion is matched against the existing assumptions to check whether it contradicts any of the assumptions. This is accomplished using pattern matching rules. If there is a contradiction, then the status of the assumption involved is changed from “Believed” to “Disbelieved” and the corresponding assumption set( s) are made Inconsistent. This will, in turn, change the status of corresponding propositions. Most contradictions in our application are of this type. Sometimes, the assertion may contradict a proposition or a set of propositions taken together. In this case, the assumption set which is the union of the assumption sets of all the propositions involved is marked as Inconsistent. As noted earlier, if an assumption set is inconsistent, it implies that one or more of the assumptions in the set is to be “Disbelieved.” In the present implementation, if such a situation occurs, the system will require the participant to intervene and choose the appropriate assumption(s) to “Disbelieve.” Thus, the truth maintenance rules contain procedures to change the status of assumptions, assumption sets, and propositions. They also pass messages to the communication interface to propagate the changes in beliefs to other interested participants.

C. Communication Base The communication base contains the communications

protocol and a data base which records the descriptive knowledge about other participant plans (in terms of key- words), and the details of messages exchanged with other participants. The communications protocol is a set of rules that govern how messages are transmitted and received. Messages can be of two types: queries and assertions. The query procedure can be invoked directly by the participant during the development of a plan in order to confirm or negate critical assumptions made about other participant plans. Queries can also be raised directly by the ATMS

CHANG et ai.: DISTRIBUTED KNOWLEDGE-BASED APPROACH FOR PLANNING AND CONTROLLING PROJECTS 1547

(as part of the truth maintenance cycle) if additional information is needed in resolving contradictions. If the recipient of the query is known, then the query is sent directly to that participant. Otherwise, it is routed through the Communication Interface which will help determine the appropriate recipient based on its metaknowledge.

On receipt of a query, a query handling routine searches the assumption and proposition nodes to check whether an assertion in response to the query can be given. If possible, the appropriate assertion is transmitted with the communication-set pointer indicating the participant to whom the assertion was sent. If not, a response indicating failure to find an answer to query is sent. On receipt of an assertion, the assertion handler routine stores the assertion in a working space. On the next truth maintenance cycle, the stored assertions are accepted as propositions, with the Justification marked as Extemal and the Label- Pointer pointing to the participant from whom the assertion was received.

D. Truth Maintenance Cycle Nonmonotonic reasoning is achieved in the system

through the execution of truth maintenance cycles, the frequency of which depends on how often the system is consulted. The cycle begins with the acceptance of in- coming assertions and the creation of new proposition nodes. Next, the truth maintenance rules are executed to detect contradictions and revise assumptions, assumption sets, and proposition status. The changes are communicated to relevant participants. Next, the current set of beliefs are checked against all inference rules and appropriate rules are executed. This leads to the creation of new propositions and assumption sets. If no inferencing is possible, solvers are invoked for assistance in creating new nodes. Then, queries from other participants are taken up for response and appropriate assertions are sent.

We now return to the project example outlined in Sec- tion I11 and illustrate the application of the cooperative framework to the problem.

E. Example Let us assume that all participants have developed their

skeleton plans as shown in Figs. 2 , 3 , and 4. We focus on the reasoning system of the Sales Department and illustrate how the truth maintenance system works. The inference rule base, proposition nodes, assumption nodes and assumption set corresponding to Fig. 2 are as follows:

Rule Base

Proposition Nodes

Prop.- Label- Comm.Set. ID Justification Pointer Status Pointer

p, Rule K, 21 Believed pz Rule Kz 22 Believed PD, MKt P, Rule K , 23 Believed . . . . . . . . . . . . . . .

Assumption Nodes

Assump.-ID Status Justification Comm.Set.Pointer

I , Believed I? Believed Zl Believed

ZX Believed . . . . . .

. . . . . .

Assumption Set

ID Assumption Set Status

Consistent Consistent Consistent

Consistent Consistent

. . .

. . .

The Sales Department sends queries to Product Devel- opment to confirm their assumptions 2, , Z5, and Z1 (Fig. 2) as to receiving assistance from them for the training programs. Product Development sends assertions confirming these assumptions. These assertions are entered as new proposition nodes in the ATMS (P9) . Next, they query the Purchasing Department regarding the assumption Z8: will Purchasing deliver the Customer Information Manual before day 20? In order to answer this query, Pur- chasing needs to know when Product Development can get the manual approved and delivered (assumption 2, in Fig. 4). Thus, Purchasing queries Product Development on this matter, which in turn queries Marketing as to when the advertising plans will be consolidated. Marketing re- plies that it will be done on day 16. Based on this, Product Development furnishes the date of approval and delivery of the manual to Purchasing as 26 days. Purchasing as- serts a date of 32 days to Sales. This assertion is entered as a new proposition node (Pl0) .

Proposition Nodes

Prop. - ID Justification

p, Rule K , p, Rule K2 PI Rule K3

PY External PI0 External

. . . . . .

Label- Pointer

21 22 23

PD Purchase

. . .

Comm.Set. Status Pointer

Believed Believed PD, MKt Believed

Believed Believed

. . . . . .

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During the truth maintenance cycle, P9 confirms assumption Z1, while P l 0 contradicts Z,. Consequently Z, is * ‘Disbelieved” and its corresponding assumption set (ID:27) is made “Inconsistent.” Assumption set ID:28, which is a superset of ID:27, is also made “Inconsist- ent.” Thus, propositions P7 and Px are “Disbelieved.”

Assumption Nodes

Assump.-lD Status Justification Comm .Set. Pointer

I , Believed 12 Believed

Believed p ,

Disbelieved PI,,

. . . Z ,

ZU . . .

. . . . . .

Assumption Set

ID Assumption Set Status

. . . . . . . . . 21 {ZU. z9. L(P6)l Inconsistent

Inconsistent . . . 28 {Z,”? L(&l

I . . . . .

Proposition Nodes

Prop.- Label- Comm.Set. ID Justification Pointer Status Pointer

~~

. . . . . . . . . . . . P, Rule K7 2 1 Disbelieved

Rule K , 28 Disbelieved . . . . . . . . . . . p3 . . .

the activity “Consolidation of Advertising P1an”can be shortened by four days, if it receives assistance from Sales. Sales and Marketing negotiate over this and since Sales has more slack in some of its activities, it decides to lend assistance. Similar processes occur between other participants. The ATMS model and the “solvers” are used to determine the implications of revised assumptions, plans, and the new critical activities and slack times. The overall plan, developed in stages on the “blackboard, ” is updated and refined as the negotiations progress and more information becomes available. Dur- ing the face-to-face negotiation process, the cooperation process is not automated, rather, it depends on human reasoning.

The advantages of the ATMS model are readily appar- ent in the cooperation process. First, it focuses the atten- tion of each participant on the assumptions that they have made in developing the plans and emphasizes the resolution of the assumptions as a means to develop an overall plan. This results in a consistent overall plan with each participant being aware of the justifications and assumptions behind the plan and their own responsibilities in meeting the plan. Second, the planning process is formalized and properly documented. The ATMS provides the inductive and deductive reasoning necessary to achieve the overall objective. The impact of a change can be easily understood and communicated to affected participants. Third, ATMS helps in developing different scenarios as a basis for supporting arguments during negotiations. Switching between scenarios (contexts) is easily achieved through truth maintenance. In addition, ATMS helps in identifying the critical points for negotiations and makes the process effective by providing a structure for negotiations.

Based on the new proposition P,,, inferencing is performed to update plans. Solvers may or may not be invoked for this p r o ~ e s s . ~

Similar processes occur in each participant’s system, all prior to any face-to-face negotiation. Once information is exchanged and some of the assumptions confirmed or negated (and plans updated accordingly), the participants meet to develop an overall plan. Details of individual plans are communicated to the interface. Let us assume that senior management needs to start the sales campaign on day 30. A look at Marketing’s plan (see Fig. 3 ) reveals that using the present plan, this goal cannot be accomplished. This implies that one or more of the critical activity times needs to be crashed. Marketing identifies that

’Although in the example discussed, it seems that a solver is necessary to perform inferencing, in the actual implementation the propositions involve additional details such as slack times, critical activities, etc. Thus, in most cases, the existing rule base is sufficient to perform simple what- if analysis.

V. CONCLUSION

Our framework for multiparticipant cooperation consists of two representations. First, we develop a representation of distributed individual ATMS models. These models allow us to introduce parallelism in developing interdependent individual plans and also contribute to a cooperative exchange of plan details with a view toward developing an overall plan. These models also maintain different planning scenarios to support the process of negotiation. The second representation is the Commu- nication Interface acting as a blackboard model. The Communication Interface supports the face-to-face negotiations. In the negotiation process, the individual ATMS models act as decision aids for switching contexts.

While the cooperation mechanisms prior to the face-to- face meeting stage are automated, automation of the negotiation process is far more complex. There are a number of issues to consider, e.g., organizational hierarchy and the allocation of slack times and resources where the utility of the different outcomes vary for each of the par-

CHANG et ai.: DISTRIBUTED KNOWLEDGE-BASED APPROACH FOR PLANNING AND CONTROLLING PROJECTS 1549

ticipants. Cooperative behavior cannot be assumed in such cases and compromise positions may have to be arrived at using senior management as mediators. Research efforts on multiagent compromise, such as that of Sycara [15] and Zlotkin and Rosenschein [16], are relevant in this context. Sycara [15] uses case-based reasoning to guide current negotiations that may prove very useful in project planning. Algorithms for constraint-directed negotiation as discussed by Sathi and Fox [7], can also be used to support automation once the negotiation process is well understood.

Our approach provides a convenient mechanism for developing skeleton plans under uncertain and incomplete information. There have been many other similar attempts to develop mechanisms to handle uncertainty and incomplete information. Dean and McDermott [18] present a computational approach to deal with temporal data bases, where assertions could change over time. They replace the notion of an assertion with a “time token” which allows one to refer to an interval of time during which a given fact is supposed to be true. Time tokens can be effectively used to represent assumptions and provide functionality similar to our system. Activity precedences can be easily represented and a whole project can be represented as a “time map.” However, building time maps are generally effort intensive and especially so in cases where multiple scenarios of a plan are built. Context (scenario) switching is especially easy in our ATMS implementation but could be cumbersome in a temporal data base.

Other researchers [ 101, [ 141, [ 191 have focused on developing distributed truth maintenance models which provide nonmonotonic reasoning capabilities. Huhns [ 141, [ 191 has developed a truth maintenance algorithm for distributed single-context truth maintenance systems that guarantees local consistency for each participant and global consistency for data that is shared by the participants. Our application demands that there be global consistency among all plans. The cooperation process we discussed may lead to excessive communication overhead if dependencies between plans are high, but the process should eventually lead to consistency between the individual plans because there are no circular dependencies between participant plans. Mason and Johnson [20] have developed a similar distributed ATMS for multiagent reasoning in seismic monitoring. In their system. agents exchange facts, assumption sets, and inconsistent assumption sets (Nogoods). Unlike our application, the communicated facts can affect only the shared database and not the private, individual beliefs. Their model also allows participants to disagree on shared data. Thus, while there is local consistency for each participant, there may not be consistency over shared data. Yokoo and Ishida [2 11 present a distributed ATMS model that guarantees global consistency.

Although a consistent overall plan can be developed in our application, the ease with which it is obtained is de-

pendent on the assumptions that participants make. The system does allow for the development of multiple contexts which are useful in storing different scenarios and for switching contexts if assumptions are found to be un- tenable. However, our system cannot overcome problems created by “bad” assumptions. As with any other decision aid, the tool is only as good as its users. For example, if the assumptions are not “reasonable” (i.e., have a high likelihood of being true), they may contribute to the need for frequent revisions of beliefs leading to significant computational and communication overhead. An- other significant problem arises in belief revision. When an inconsistent assumption set is identified it implies that one of its assumption is “Disbelieved.” How does the ATMS decide which assumption to retract? In our current implementation, the system requests human intervention. Alternatively, it is possible to associate with each assumption a believability measure. The ATMS can then choose to retract the assumption that is least believable. However, if the importance of the assumption is context dependent, the issue of revision is not so simple. These are a few of the issues needing further refinement.

We have implemented our system in C and are pres- ently applying our model in an auditing environment to assess the reliability of an internal control system in a distributed environment [22]. It is a less complex application than a traditional project, as we can assume that participants have similar utilities for outcomes and, hence, are more cooperative. We are in the process of experimenting with our system to determine its relative efficacy with respect to traditional project planning techniques. We in- tend measuring different constructs of utility for the systems compared. The constructs will include level of comfort, challenge, perceived ease of use, perceived use- fulness, perceived compatibility, perceived outcome quality, satisfaction with outcome, and satisfaction with the process [23]. The experimental results should provide validity for the substantial benefits to be had from this approach. In our ongoing research we are focusing on increased intelligence and capabilities for each participant and on making the cooperation process more sophisti- cated.

111

121

[31

I41

151

L61

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Ai-Mei Chang received the B.S. degree in computer science and mathematics and the Ph.D. in management information systems, from Purdue University.

She is Assistant Professor of Management In- formation Systems in the College of Business and Public Administration, University of Arizona. Her research interests include decision support systems-theory, design and development, computer supported collaborative systems, distributed artificial intelligence, and expert systems.

Andrew D. Bailey, Jr. received two degrees at the University of Minnesota before receiving the Ph.D. degree at Ohio State University, in 1971. Most recently the Arthur Young Professor of Ac- counting at Ohio State University, he has been a faculty member at the Universities of Maine, Minnesota (department chair), Iowa, Purdue, and Ohio State University. He has also been a visiting professor at the University of Queensland, Aus- tralia, and Otago University, New Zealand. For- merly with Touche Ross & Co., he is a CPA, CIA,

and CMA. He received honors on each of the certification examinations including the National Gold Medal on the CMA Examination. He is pres- ently the Deloitte and Touche Professor of Accounting and Management Information Systems and Head of the Department of Accounting at the University of Arizona.

Dr. Bailey has been active in many professional organizations, was a vice president of the American Accounting Association (AAA), and a co- editor of Auditing: A Journal of Practice & Theory, the AAA Auditing Section joumal, and is past Chairman of the Auditing Section of the AAA. His research interest include auditing and statistics and auditing in a com- puterized environment. He has published extensively in The Journal ofAc- counting Research, The Accounting Review, The Journal of Information Systems, Management Accounting, Auditing: A Journal of Practice & The- ory, and other periodicals.

Andrew B. Whinston is the Hugh Roy Cullen Chaired Professor at the University of Texas at Austin and the Jon Newton fellow at the IC2 In- stitute. His research focuses on organization information systems, economics of information systems, and decision support systems. He has published over 200 papers and 16 books and is the current Editor-in-Chief of Decision Support Sys- tems and Journal of Organizational Computing.

a distributed knowledge-based approach for planning and controlling projects

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