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Knowledge acquisition for group problem solving
Computers and Industrial Engineering Vol. 23, Nos I-4, pp. 459-462, 1992 Printed in Great Britain. All rights reserved
0360-8352/92 $5.00+0.00 Copyright © 1992 Pergamon Press Ltd
KNOWLEDGE ACQUISITION FOR GROUP PROBLEM SOLVING Kent E. Williams, John Deighan, and ~
Komour
Management Systems Laboratories V i r ~ Polytechnic Imttmte and State University
management in managing complexity. This being the case, the problem solving process serves as a fundamental archiUu:mxe for developing mmputerized aids to m t management in the perfi3rmance of these complex cognitive r o b .
The tasks faced by contemporat7 and future managers are and will be ~ f ~ , l y complex and cognitive in nature. In fact, most m u a g e n are faced with the numagement of the Imowiedge bases of groupe of
The Problem Solvint Model
tuUvtauak p c . w ~ g spemUzed knowJedge in a variety of d o m a ~ ~ representing, organizi~ integraHn b COnStraining ~ evalualtng these mnective knowledge sources are required for accurate problem solvfag and derision making in the mansger'8 eavimnment. Olven the complea~ of these cognitive acttvltl~ computerized systems for group problem solving (GPSS) and group aeasion support (GDSS) have emerged. An exbang problem with these wstetm is the of faenity with which knowledge can be elicited, organized, and integrated for problem relying and declsion m a k ~ activitim. The research presented in this paper d _,~:~es an automated knowledge acquisition capability based upon a cognit/ve mmplexity analysis for genemthu~ problem ~epresenmdom from which deckions and problem mlutions can be made. The p m b k m solving process dem'ibed is m m b t e n t with u information processing model of problem solving which can be applied to group and individual applications and serves as a rare arc~tecture for the development of an information center.
The problem solving model propmed as a fundamental architecture for the design of intenigent management systems is based upon an information processing model of human cognition (Newell and Simon, 1972; and Simon, 1978). Although this model was developed from an analysis of problem solving protocols generated by ind/viduals,we propme, as have others, (Prietula,eeatr,la/r, and Lerch, 1990) that this model general/zesto group problem solvingas welL The basics of this model are qu/te simple. There first exim, external to the ~lividual, a task. The task consbts of a statement of the problem within a context. As an e0mmple, there exists a task that consists of solving the problem of constructing a new nuclear reactor capability within the context of a set of conslraints (i.e., ~ safety, environmental protection,
reUabUity..ecurity, productivity, deign Se/de~e~ ew.). Secondly, there e~ism within the coUecl/ve knowledge of a group of individuals a problem space which consim of an interpretation of all of the intermediate subgoab (i.e., milestones, ob]ea/v~ eta) which must be accomplished; the operators (i.e., task behaviors and activities) by which one moves from one subgoal to another;, the initialconditions of the problem (i.e.,what is currently known about the construction of a nuclear reactor fadl/ty);and the desired goal (Le.,a new facilityon time and within budget). This problem space also comists of comtraints which limit what can and cannot be done. For our mmmple, a number of conmainm (Le., requirements) have been idenefied withtn the context of meeting the goal, such as, safety, quality assurance, environmental protection, budget, schedule, eW. The problem spacc therefore includes the initial conditiom, potential intermediate subgoab which must be achieved, the kinds of moves which allow one to accomplish these intermed/ate subsoals, constraints in terms of l i m i ~ condit/ons (what is and isn'tallowable? what temporal and cmt schedule must be met?) and the desired endstate or goal. As Simon (1978) has descr/bed, finding a solution to a problem is like moving along a path from the initial state through a number of intermediate states to the goal state.
INTRODUCTION The problem miring capacity of u or~nt~tion is critical to the s,___~_~ management and accomplishment of its goals and o b j . Many probkms facial contemporary or~ni~tions are extremely complex, spedlkally those encountered by organizaflom which require multiple disc/pl/nesto effect a problem solution. ]~Arinimi~ln_e_the mtel'ial and human cram of managing such compk:xfly b a challenge m be met by the development of mpMslka~l management systems which can guide the problem s o l v ~ a~ivi~es of management teams. ~lanat~ment and Cmmition Most tasks performed by throe engaged in the m n a p m e n t of large projects or programs are cognit/ve in nature, requiring a considerable base of domain knowledge and strategies for generating solutions to problems given this domain knowledge (i.e. knowing how to use the k n o w . p ) . Colpaeve task, performed by m n a j e r a Include such activities as planning, de¢~ion mal~S, dadlpdn~ redesign~b su'a~.'~ns, hypothes~n~ and bralastormlag. These are viewed as involv~g the problem m l v ~ process in whole or Jn part. Therefore, u undentanding of the problem soivln| process can guide the development and implementation of software systems to assist
Sm~.s of Problem Solvinf The information processing model of problem solving is typically parlitioned into two smge~ (1) a representation stage and (2) a solution stage. In the
459
460
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering representation stage the problem solver(s) must chome that knowledge which is, and is not, relevant in terms of initial conditions, i n t e ~ t e subgoais, constminm, operators and any alternatives to each operator if they exist. The solution stage involves decblon-mking, i.e. choceing between alternative operators to ceet effectively approach the goal. The solution stage also includes deciding upon appropriate measures for evuluetlag progress when a choice is made between ulteruau've operators. Of particular importance, however, is the problem representation stage. If an accurate, comprehensive representation of the problem ~nnot be acquired then succemfel solutions cannot be achieved.
Br~msmatt~t Consistent with the model of problem solving. the representation stage involves identification of activities which spedfy the problem space. The subgoais and goal of the problem must be explicitly defined. Any intermediate subgoais must be dem-~ed along with the kinds of methods or operators which can be applied to the problem to move between sub~uls intermediate to the goal state. The representation stage then cerebra of acquiring knowledge units, each unit s p e ~ a sublp~al and a procedure and/or set of alternative procedures for achieving that subgoaL The inteffatlon of these units forms a knowledge base upon which search beuristim or solution heuristics can operate to pmpese alternative problem solutions. Consequently, the representation stage consists of the knowledge acqulslllon process and herein lies the difficulty for a group problem solving system. Kaowlodt,e Acoulsition Methods Numerous methods for acquld~ knowledge have been implemented to facilitate the development of intell/gent so/~vare systems. A recent review of these methodologies by Komour and wmlams, (1991) classify them into three broad mtegodes: (I) those which are manually implemented by the Imowledge engineer, (2) those which are aided by an automated interface to the expert and (3) throe which employ mchine learning techniques. This clasMflcallon is a continuum from manual methods to mchine learning methods. As a result of their review it became evident that only two of these methodologies were actually gsounded by empirical research in cognitive psyc~lo~. This e m p i r e ! base attests to their validity in terms of the manner in which human experm organize procem knowledge of a domain and consequently recall that knowledge. If one is to des/gn and develop a knowledge acquisition capabillly to capture process knowindge, it behooves the des/per to determine how the human expert naturally reports his/her undentanding of domain knowledge, as oppmnd to forc~llg an unnaUIrul structure lncompatfble with the manner in which humans store and retrieve knowledge. The two empirically grounded methodologies identified were (1) the OOMS ~ task analym teckuique for developing mgattive simulation models of procedural domains (Card Moran and Newell, I ~ 3 , and Kleras and Poison, 1985) and (2) the "comtrucfl~ intemctfon" method for enctting a function - m e c h u ~ hierarc~ representing how one undenmnds complex physical devices (Miyalw, 1966). Since the domain of Goncern for our resealv~ effort walt msmasNnent taJgs, requldq procedural procem knowledge, we selected and modified the O01VlS cogaMve task anulys~ technique to automte knowledge ellcttation and ncquisirlon of procedural knowledge un/ts. The GeMS Analysis Technlaue A GeMS analysis provides a model of the knowledge that an individual or group of indivtdnais
must have in order to carry out procedural tasks. Kieras (1~8) refers to this as "how to do it" knowledge as opposed to "how it works" imowiodge. GeMS models were first employed by Card, Moran, and Neweil (1983) to develop production systems which model how an individual would "use" a s ~ dev/ce. Modeling how to use a device is highly procedural in nature and therefore was a good test case for their experimentation. However, upon further analysis it became obvious that much of our knowledge is procedural in nature and consequently the GeMS analysis extends far beyond the modehg of device use. GOMS stands for Goals, Operators, Methods and Selection rules. The GeMS model describes a whole series of descriptions called Methods which when implemented accomplish a specific Goal or subgeais. Each Me~od in turn is made up of a series of steps consisting of Ouerutors that are performed or executed in order to accomplish a Goal or subgoaL If there is more than a single method by which the goal or subgoal can be ach~s,ed, then a ~lectlon R ~ is chosen to determine under what conditions one method and its operators is selected over another. What is so elegant about this teehn~ue is that buman~ naUlrally represent their knowledge in these so called goal plan hierarchies (Black and Bower, 1979, Blach and Bower, 1980, and Black, Kay and Soloway, 1987), and it is intuitively simple to think about steps to accomplish something. A practical implementation of the GeMS analysis technique begins in a top down breadth first runner. That is, first a top level goal is specified by the user(s) as something which the user(s) wishes to accomplish. Next the user(s) is requested to specify a method which consists of a series of steps (Le. operators) to be esecuted in order to accomplish the top level goal At this point the series of steps are of a high level and consequently this top level method must further be decomposed. The user(s) is also constrained to specifying not more than seven steps, since the human information proceming system typicah'y can chunk only seven + 2 elemeam (Miller, 1956). If the user(s) specifies more than seven steps be/sbe is requested to combine some of the steps into a higher level step which is further decomposed at a later t/me. Having specified a top level method the user is requested to specify any alternative methods which would accomplish the same goal. If an alternat/ve method is specified the user identifies the method steps under the same constraints as before. Following the specification of all alternative methods for accomplishing the top level goal, the user must then develop a selection rule or a set of selection rules. Each selection rule identifies a set of preconditions which discriminate between methods triggering which method is to be selected under differing conte0ttual conditions to accomplish the goal Having specified a top level goal, a top level method, its alternatives, and a selection rule(s), the system then convem each step in the top level method into a subgoaL A set of steps comprising a method is then requested of the user for each subgoaL If alternative methods for accomplishing a subgoal exist then a selection rule(s) must be specified as before. This proceu cruates a goal - subgoal hierarchy along with appropriate sekctlon rules. Method steps are continuously transiliolted to subgolds requlr~g lower level methods and selection rules. The process terminates when all steps for each method have been dedgnated m primitives. A primitive step can be identified by the user at any time indicating that the step does not require any further decomposition as a subgoaL Upon completion of this analysis a set of procedures (i.e. rules) have been defined which can be executed by a production system.
WILLIAMS et al.: Group Problem Solving
The GOMS analysis techn/que as described yields a strict hierarchy of production units. That ~, traversing subgoal nodes to other subtrens in the hierarchy is not accommodated. Consequently, to extend the generalizability of this knowledge acquisition technique to any procedural domain, we have integrated a capability for the user to designate (a) global exception rules and Co) impasses at primitive level steps. These extensions force the user(s) to seek a wider range of alternatives to be considered in creating the problem representation. Exploration of a wider range of alternatives reduces the potential for failure as a result of inadequate consideration of other points of view. Consequently, these extensions facilitate brainstorming (Van De Ven and Delbecq, 1974). Global exception rules are generated after the user has completed the GOMS analysis to the point of spec/fying methods made up of all primitive steps for each of the lowest level subgoais klnntified. An e~ceptlon rule is generated in a ~ m ~ r fashion as a selection rule. Selection rules operate within a specific goal or subgoal in order to select the appropriate method to accomplish the goal A selection rule takes the form of IF THEN form. Exception rules h~wise take the same form but allow the execution of the system to abandon work on a specific subgoal and shift attention to another subgoal at any level within the knowledge base. The form of an exception rule is IF THEN These global exception rules can be triggered at anytime during the execution of a traditional GOMS model in order to accommodate general purpose production system models. The addition of global except/on rules also motivates the user to assess the completeness of his/her model relative to the specification of subgoais. It has been our personal experience, in the interview process employing GOMS for modeling procedural knowledge of noninterface domains, that experts typically recall standard or normal operations prior to considering any exceptional circumstances which would interfere or disrupt standard procedures. Querying the user for exception rules at the completion of a typical GOMS analysis allows for his attention to shift in order to focus upon exceptions which may result in the specification of before nnmnntion~ subgoais and their methods. With the same intent to motivate the user toward eliciting complete models with varied alternative methods, we have also implemented a dialogue window which queries the user concerning impasses in the execm/on of primitive steps. Upon completion of the standard GOMS analysis and having progressed through the exception rule dialogue, the user is presented with the primitive steps of a lowest level method. The user is asked to indicate if it is likely that any step or steps may fail If yes, then the user is asked to indicate the step or steps and then asked if an existing identified alternative method can overcome the failure(b). If yes, then the user is asked to identify the existing method and a selection rule will automatically be generated to select that method given the failed step. If no, then the user is asked if an as yet unidentified alternative method can be generated to overcome the failure. If yes, then the user is presented with a method dialogue window in order to develop the new method. A selection rule would then be automatically generated deacn~ing the step failure and the new method name to be selected. If the failure cannot be overcome then the goal cannot be a~leveA and the production system process would fail at this point. If it is not I/kely that any of the steps of the method may fail, then the user is presented with another lowest level method and its primitive steps. This process is iterated until all lowest level methods and their steps have been reviewed by the user. The process is similar
461
to Laird, Newell, and Rosenbloom's (I987) "universal subgealin~O. That is, when an impasse is encountered it is set as a sub~p~al which must be overcome by an alternative procedure. The group having been guided through t l ~ process by a fac/limtor interacting with the knowledge acquisition tool will have developed a comprebemive problem representation with all decklon rules having been specified at the various choice points in the goal subgoal hierarchy. Solution Staee The solution stage in the problem solving process can now take place by providing for each method a measure for evaluating its efficiency and or effectivenem in accomplishing each spec/fied subgoaL The solution is described as selecting the best moves to make relative to these measures while forging a path through the problem representation. There are numerous methods for deterlnniniqg how to select methods, some of which are specific to particular problems white others are more general in nature. A popular general purpose solution method called hneans-ends" analysis was idenCfied by Neweil and Simon (1972). A solution method employing means-ends analysis consists of two steps which are applied repeatedly. The first step consists of identifying the differences between the outcome of each alternative method and the goaL The second step consists of selecting and applying the method which allows one to move in such a fashion as to maximally reduce the difference or distance between the current state and the goal state. This technique is typically applied when working in a forward direction from initial conditions to the goal (i.e. bottom-up) and is typically used in planning when one is considering what operations will move you toward the final goal most effectively and efficiently. In snmmal~j~ ~ problem solving model serves as the core computational architecture for the development of an intelligent management system which can be applied to both group and individual settings. The knowledge acquisition process for generating problem representations inherently employs features to promote brainstorming. The selection rule component prov~es slructure to the decision mating act/vitins of managers. Solution beur~tics provide alternative plans for accomplishing the top level goaL A selected plan can later be used as a reference to monitor performance given real world data. Lastly, if unforeseen i m l n m ~ arise new alternative methods can be spliced into the problem representation to assem their impact upon modifying the problem solution such that replanning can take place having fore knowledge of potential problems in cost and schedule pm|ections. J-B. d r G.H. (1 . " & g L ~ m L l l a Q , . i f ~ 0 L voL" le, pp. ~'y-~in.
BLtck~J.B.~Kav. D.S.~and Solowav. E.M. (19873. Goals ~{1 Kno~] ~.~ • " to
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Proceedings of the 14th Annual Conference on Computers and Industrial Engineering
utl~eastern'cllapmr, pp. ~oz~t~.
VoL 17",pp. 60~-621.
0360-8352/92 $5.00+0.00 Copyright © 1992 Pergamon Press Ltd
KNOWLEDGE ACQUISITION FOR GROUP PROBLEM SOLVING Kent E. Williams, John Deighan, and ~
Komour
Management Systems Laboratories V i r ~ Polytechnic Imttmte and State University
management in managing complexity. This being the case, the problem solving process serves as a fundamental archiUu:mxe for developing mmputerized aids to m t management in the perfi3rmance of these complex cognitive r o b .
The tasks faced by contemporat7 and future managers are and will be ~ f ~ , l y complex and cognitive in nature. In fact, most m u a g e n are faced with the numagement of the Imowiedge bases of groupe of
The Problem Solvint Model
tuUvtauak p c . w ~ g spemUzed knowJedge in a variety of d o m a ~ ~ representing, organizi~ integraHn b COnStraining ~ evalualtng these mnective knowledge sources are required for accurate problem solvfag and derision making in the mansger'8 eavimnment. Olven the complea~ of these cognitive acttvltl~ computerized systems for group problem solving (GPSS) and group aeasion support (GDSS) have emerged. An exbang problem with these wstetm is the of faenity with which knowledge can be elicited, organized, and integrated for problem relying and declsion m a k ~ activitim. The research presented in this paper d _,~:~es an automated knowledge acquisition capability based upon a cognit/ve mmplexity analysis for genemthu~ problem ~epresenmdom from which deckions and problem mlutions can be made. The p m b k m solving process dem'ibed is m m b t e n t with u information processing model of problem solving which can be applied to group and individual applications and serves as a rare arc~tecture for the development of an information center.
The problem solving model propmed as a fundamental architecture for the design of intenigent management systems is based upon an information processing model of human cognition (Newell and Simon, 1972; and Simon, 1978). Although this model was developed from an analysis of problem solving protocols generated by ind/viduals,we propme, as have others, (Prietula,eeatr,la/r, and Lerch, 1990) that this model general/zesto group problem solvingas welL The basics of this model are qu/te simple. There first exim, external to the ~lividual, a task. The task consbts of a statement of the problem within a context. As an e0mmple, there exists a task that consists of solving the problem of constructing a new nuclear reactor capability within the context of a set of conslraints (i.e., ~ safety, environmental protection,
reUabUity..ecurity, productivity, deign Se/de~e~ ew.). Secondly, there e~ism within the coUecl/ve knowledge of a group of individuals a problem space which consim of an interpretation of all of the intermediate subgoab (i.e., milestones, ob]ea/v~ eta) which must be accomplished; the operators (i.e., task behaviors and activities) by which one moves from one subgoal to another;, the initialconditions of the problem (i.e.,what is currently known about the construction of a nuclear reactor fadl/ty);and the desired goal (Le.,a new facilityon time and within budget). This problem space also comists of comtraints which limit what can and cannot be done. For our mmmple, a number of conmainm (Le., requirements) have been idenefied withtn the context of meeting the goal, such as, safety, quality assurance, environmental protection, budget, schedule, eW. The problem spacc therefore includes the initial conditiom, potential intermediate subgoab which must be achieved, the kinds of moves which allow one to accomplish these intermed/ate subsoals, constraints in terms of l i m i ~ condit/ons (what is and isn'tallowable? what temporal and cmt schedule must be met?) and the desired endstate or goal. As Simon (1978) has descr/bed, finding a solution to a problem is like moving along a path from the initial state through a number of intermediate states to the goal state.
INTRODUCTION The problem miring capacity of u or~nt~tion is critical to the s,___~_~ management and accomplishment of its goals and o b j . Many probkms facial contemporary or~ni~tions are extremely complex, spedlkally those encountered by organizaflom which require multiple disc/pl/nesto effect a problem solution. ]~Arinimi~ln_e_the mtel'ial and human cram of managing such compk:xfly b a challenge m be met by the development of mpMslka~l management systems which can guide the problem s o l v ~ a~ivi~es of management teams. ~lanat~ment and Cmmition Most tasks performed by throe engaged in the m n a p m e n t of large projects or programs are cognit/ve in nature, requiring a considerable base of domain knowledge and strategies for generating solutions to problems given this domain knowledge (i.e. knowing how to use the k n o w . p ) . Colpaeve task, performed by m n a j e r a Include such activities as planning, de¢~ion mal~S, dadlpdn~ redesign~b su'a~.'~ns, hypothes~n~ and bralastormlag. These are viewed as involv~g the problem m l v ~ process in whole or Jn part. Therefore, u undentanding of the problem soivln| process can guide the development and implementation of software systems to assist
Sm~.s of Problem Solvinf The information processing model of problem solving is typically parlitioned into two smge~ (1) a representation stage and (2) a solution stage. In the
459
460
Proceedings of the 14th Annual Conference on Computers and Industrial Engineering representation stage the problem solver(s) must chome that knowledge which is, and is not, relevant in terms of initial conditions, i n t e ~ t e subgoais, constminm, operators and any alternatives to each operator if they exist. The solution stage involves decblon-mking, i.e. choceing between alternative operators to ceet effectively approach the goal. The solution stage also includes deciding upon appropriate measures for evuluetlag progress when a choice is made between ulteruau've operators. Of particular importance, however, is the problem representation stage. If an accurate, comprehensive representation of the problem ~nnot be acquired then succemfel solutions cannot be achieved.
Br~msmatt~t Consistent with the model of problem solving. the representation stage involves identification of activities which spedfy the problem space. The subgoais and goal of the problem must be explicitly defined. Any intermediate subgoais must be dem-~ed along with the kinds of methods or operators which can be applied to the problem to move between sub~uls intermediate to the goal state. The representation stage then cerebra of acquiring knowledge units, each unit s p e ~ a sublp~al and a procedure and/or set of alternative procedures for achieving that subgoaL The inteffatlon of these units forms a knowledge base upon which search beuristim or solution heuristics can operate to pmpese alternative problem solutions. Consequently, the representation stage consists of the knowledge acqulslllon process and herein lies the difficulty for a group problem solving system. Kaowlodt,e Acoulsition Methods Numerous methods for acquld~ knowledge have been implemented to facilitate the development of intell/gent so/~vare systems. A recent review of these methodologies by Komour and wmlams, (1991) classify them into three broad mtegodes: (I) those which are manually implemented by the Imowledge engineer, (2) those which are aided by an automated interface to the expert and (3) throe which employ mchine learning techniques. This clasMflcallon is a continuum from manual methods to mchine learning methods. As a result of their review it became evident that only two of these methodologies were actually gsounded by empirical research in cognitive psyc~lo~. This e m p i r e ! base attests to their validity in terms of the manner in which human experm organize procem knowledge of a domain and consequently recall that knowledge. If one is to des/gn and develop a knowledge acquisition capabillly to capture process knowindge, it behooves the des/per to determine how the human expert naturally reports his/her undentanding of domain knowledge, as oppmnd to forc~llg an unnaUIrul structure lncompatfble with the manner in which humans store and retrieve knowledge. The two empirically grounded methodologies identified were (1) the OOMS ~ task analym teckuique for developing mgattive simulation models of procedural domains (Card Moran and Newell, I ~ 3 , and Kleras and Poison, 1985) and (2) the "comtrucfl~ intemctfon" method for enctting a function - m e c h u ~ hierarc~ representing how one undenmnds complex physical devices (Miyalw, 1966). Since the domain of Goncern for our resealv~ effort walt msmasNnent taJgs, requldq procedural procem knowledge, we selected and modified the O01VlS cogaMve task anulys~ technique to automte knowledge ellcttation and ncquisirlon of procedural knowledge un/ts. The GeMS Analysis Technlaue A GeMS analysis provides a model of the knowledge that an individual or group of indivtdnais
must have in order to carry out procedural tasks. Kieras (1~8) refers to this as "how to do it" knowledge as opposed to "how it works" imowiodge. GeMS models were first employed by Card, Moran, and Neweil (1983) to develop production systems which model how an individual would "use" a s ~ dev/ce. Modeling how to use a device is highly procedural in nature and therefore was a good test case for their experimentation. However, upon further analysis it became obvious that much of our knowledge is procedural in nature and consequently the GeMS analysis extends far beyond the modehg of device use. GOMS stands for Goals, Operators, Methods and Selection rules. The GeMS model describes a whole series of descriptions called Methods which when implemented accomplish a specific Goal or subgeais. Each Me~od in turn is made up of a series of steps consisting of Ouerutors that are performed or executed in order to accomplish a Goal or subgoaL If there is more than a single method by which the goal or subgoal can be ach~s,ed, then a ~lectlon R ~ is chosen to determine under what conditions one method and its operators is selected over another. What is so elegant about this teehn~ue is that buman~ naUlrally represent their knowledge in these so called goal plan hierarchies (Black and Bower, 1979, Blach and Bower, 1980, and Black, Kay and Soloway, 1987), and it is intuitively simple to think about steps to accomplish something. A practical implementation of the GeMS analysis technique begins in a top down breadth first runner. That is, first a top level goal is specified by the user(s) as something which the user(s) wishes to accomplish. Next the user(s) is requested to specify a method which consists of a series of steps (Le. operators) to be esecuted in order to accomplish the top level goal At this point the series of steps are of a high level and consequently this top level method must further be decomposed. The user(s) is also constrained to specifying not more than seven steps, since the human information proceming system typicah'y can chunk only seven + 2 elemeam (Miller, 1956). If the user(s) specifies more than seven steps be/sbe is requested to combine some of the steps into a higher level step which is further decomposed at a later t/me. Having specified a top level method the user is requested to specify any alternative methods which would accomplish the same goal. If an alternat/ve method is specified the user identifies the method steps under the same constraints as before. Following the specification of all alternative methods for accomplishing the top level goal, the user must then develop a selection rule or a set of selection rules. Each selection rule identifies a set of preconditions which discriminate between methods triggering which method is to be selected under differing conte0ttual conditions to accomplish the goal Having specified a top level goal, a top level method, its alternatives, and a selection rule(s), the system then convem each step in the top level method into a subgoaL A set of steps comprising a method is then requested of the user for each subgoaL If alternative methods for accomplishing a subgoal exist then a selection rule(s) must be specified as before. This proceu cruates a goal - subgoal hierarchy along with appropriate sekctlon rules. Method steps are continuously transiliolted to subgolds requlr~g lower level methods and selection rules. The process terminates when all steps for each method have been dedgnated m primitives. A primitive step can be identified by the user at any time indicating that the step does not require any further decomposition as a subgoaL Upon completion of this analysis a set of procedures (i.e. rules) have been defined which can be executed by a production system.
WILLIAMS et al.: Group Problem Solving
The GOMS analysis techn/que as described yields a strict hierarchy of production units. That ~, traversing subgoal nodes to other subtrens in the hierarchy is not accommodated. Consequently, to extend the generalizability of this knowledge acquisition technique to any procedural domain, we have integrated a capability for the user to designate (a) global exception rules and Co) impasses at primitive level steps. These extensions force the user(s) to seek a wider range of alternatives to be considered in creating the problem representation. Exploration of a wider range of alternatives reduces the potential for failure as a result of inadequate consideration of other points of view. Consequently, these extensions facilitate brainstorming (Van De Ven and Delbecq, 1974). Global exception rules are generated after the user has completed the GOMS analysis to the point of spec/fying methods made up of all primitive steps for each of the lowest level subgoais klnntified. An e~ceptlon rule is generated in a ~ m ~ r fashion as a selection rule. Selection rules operate within a specific goal or subgoal in order to select the appropriate method to accomplish the goal A selection rule takes the form of IF
461
to Laird, Newell, and Rosenbloom's (I987) "universal subgealin~O. That is, when an impasse is encountered it is set as a sub~p~al which must be overcome by an alternative procedure. The group having been guided through t l ~ process by a fac/limtor interacting with the knowledge acquisition tool will have developed a comprebemive problem representation with all decklon rules having been specified at the various choice points in the goal subgoal hierarchy. Solution Staee The solution stage in the problem solving process can now take place by providing for each method a measure for evaluating its efficiency and or effectivenem in accomplishing each spec/fied subgoaL The solution is described as selecting the best moves to make relative to these measures while forging a path through the problem representation. There are numerous methods for deterlnniniqg how to select methods, some of which are specific to particular problems white others are more general in nature. A popular general purpose solution method called hneans-ends" analysis was idenCfied by Neweil and Simon (1972). A solution method employing means-ends analysis consists of two steps which are applied repeatedly. The first step consists of identifying the differences between the outcome of each alternative method and the goaL The second step consists of selecting and applying the method which allows one to move in such a fashion as to maximally reduce the difference or distance between the current state and the goal state. This technique is typically applied when working in a forward direction from initial conditions to the goal (i.e. bottom-up) and is typically used in planning when one is considering what operations will move you toward the final goal most effectively and efficiently. In snmmal~j~ ~ problem solving model serves as the core computational architecture for the development of an intelligent management system which can be applied to both group and individual settings. The knowledge acquisition process for generating problem representations inherently employs features to promote brainstorming. The selection rule component prov~es slructure to the decision mating act/vitins of managers. Solution beur~tics provide alternative plans for accomplishing the top level goaL A selected plan can later be used as a reference to monitor performance given real world data. Lastly, if unforeseen i m l n m ~ arise new alternative methods can be spliced into the problem representation to assem their impact upon modifying the problem solution such that replanning can take place having fore knowledge of potential problems in cost and schedule pm|ections. J-B. d r G.H. (1 . " & g L ~ m L l l a Q , . i f ~ 0 L voL" le, pp. ~'y-~in.
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