App ears in the Pro ceedings of the Fifteenth National Conference on Articial Intelligence AAAI pp Learning to Predict User Op erations for Adaptive Scheduling Melinda T Gervasio and Wayne Iba and Pat Langley Institute for the Study of Learning and Exp ertise Staunton Court Palo Alto California fgervasioibalangleygisleorg Abstract of those abilities Machine learning can help address these problems by providing adaptive systems that au Mixedinitiative systems present the challenge of nd tomatically tailor their b ehavior to dierent users ing an eective level of interaction b etween humans In this pap er we present an empirical study of learn and computers Machine learning presents a promis ing user mo dels in an adaptive scheduling assistant ing approach to this problem in the form of systems We b egin by describing our application a synthetic do that automatically adapt their b ehavior to accommo main that involves resp onding to hazardous materials date dierent users In this pap er we present an em incidents We also describ e Inca the interactive as pirical study of learning user mo dels in an adaptive sistant we are developing for this domain Traces of assistant for crisis scheduling We describ e the prob lem domain and the scheduling assistant then present user interactions with Inca provided the data for our an initial formulation of the adaptive assistants learn learning exp eriments We formulate the scheduling as ing task and the results of a baseline study After this sistants task of predicting user op erations as a classi we rep ort the results of three subsequent exp eriments cation problem and we discuss the results of a baseline that investigate the eects of problem reformulation study which showed some b enet from learning but and representation augmentation The results suggest also left ro om for improvement The three subsequent that problem reformulation leads to signicantly b et exp eriments investigate the eects of problem represen ter accuracy without sacricing the usefulness of the tation and formulation on p erformance Their results learned b ehavior The studies also raise several inter show that problem reformulation can lead to much b et esting issues in adaptive assistance for scheduling ter adaptation without sacricing the usefulness of the learned concepts The exp eriments raised a numb er of issues which we consider in our closing discussion of Intro duction related and future work In recent years there has b een a surging interest in the mixedinitiative paradigm where multiple agents Scheduling for Crisis Resp onse sp ecically humans and software systemsshare con A dominant theme in crisis resp onse is urgencyan trol by partitioning the resp onsibilities in problem solv agent is comp elled to act to avert an undesirable sit ing This trend holds not only for the ubiquitous appli uation in a limited amount of time Finding an ecient cations on the Web but also in domains such as plan level of interaction b etween the human user and the ning and scheduling where the traditional AI approach computer is thus particularly imp ortant Our resp onse has involved autonomous systems Ideally a mixed to urgency relies on machine learning to acquire user initiative system b enets from the individual strengths mo dels to facilitate this interaction To illustrate our of its constituentsthe exp ertise of the human user and ideas and to lay the groundwork for the exp eriments in the computational p ower of the computer For this syn this pap er we will discuss them in the context of a syn ergy to o ccur however the division of resp onsibilities thetic hazardous materials domain HazMat and the must b e mutually b enecial and the two participants INteractive Crisis Assistant Inca that we develop ed must b e able to work together eectively Meeting these for this domain Inca provides assistance for b oth plan requirements with a single static system will b e dicult ning and scheduling but we will fo cus on the scheduling b ecause dierent users will have dierent abilities and task here preferences In addition as software systems b ecome more p owerful they may outgrow the users ability to HAZMAT Resp onse Using INCA communicate tasks or requests that b est take advantage In developing the synthetic HazMat domain we con sulted the North American Emergency Resp onse c Copyright American Asso ciation for Articial In Guideb o ok Transp ort Canada et al a hand telligence wwwaaaiorg All rights reserved b o ok for rst resp onders to hazardous materials inci scription or overallo cation of any resource Using the dents A HazMat problem consists of a spill and p os ve available op erators the user mo dies the schedule sibly a re involving one of typ es of hazardous ma until it is feasible and he considers it acceptable terial There are classes of HazMat incidents varying in the typ e and amount of material involved Inca currently assists the user in scheduling by pro the lo cation of the incident and the characteristics of viding an initial candidate schedule retrieved from a the spill and any re Incidents also have asso ciated re case library by suggesting heuristically determined de and health hazards that resp ectively characterize the fault values for resources durations and start times probability of a re and the danger to p eoples health and by checking the feasibility of schedules By inte grating learning into Inca we hop e to improve its as There are typ es of actions and typ es of re sistant capabilities by letting it adapt its b ehavior to sources available for resp onding to a HazMat incident In any given problem only a subset of the actions will individual users b e applicable as indicated in the Guideb o ok Each ac tion addresses particular asp ects of a HazMat problem Acquiring and Applying User Mo dels and requires some minimum set of resources The sp e A user will have p ersonal b eliefs ab out what actions cic resources available and their asso ciated capacity are appropriate for a problem what actions are more and quantity constraints vary with every problem imp ortant than others and what actions should b e p er Inca is an interactive system that provides planning formed rst These preferences are reected by the op and scheduling assistance for HazMat resp onse In erators that the user selects and their resultswhat the planning phase the user interacts with Inca to actions are included and how they are ordered in the cho ose the schedulable actions a subset of the appli schedule how resources are allo cated to dierent ac cable actions to the problem The input to the schedul tions and how dierent conicts are resolved The goal ing phase is this set of schedulable actions the set of of learning in Inca is to extract such information from available resources and an initial p ossibly empty can traces of its interactions with the user and to use this didate schedule provided by Inca information to adapt its b ehavior accordingly For ex In a departure from traditional scheduling the task is ample Inca might use this preference information to to cho ose some subset of the schedulable actions and to suggest resources durations and start times that are assign them to resources Moreover actions may b e al similar to the users previous choices when adding the lo cated a variable numb er of resources and arbitrary du action Or it might apply scheduling op erators based on ration Consider the action of extinguishing a re with this information to sp ecically tailor initial candidate a hose Allo cating more resources reghters hoses schedules By making suggestions that the user is more hydrants etc to the action as well as simultaneously likely to accept Inca can make the HazMat resp onse scheduling other extinguishment actions eg extin pro cess more ecient thereby directly addressing the guish with dry sand will put out the re more quickly urgency asp ect of crisis resp onse This interdep endence among actions resources and ef As our rst step in integrating learning into Inca fects makes it dicult to completely determine resource we fo cused on the prediction of scheduling op erations requirements and action durations prior to scheduling Sp ecically Incas user mo deling task was Given a With eective heuristics b eing dicult to engineer for particular scheduling stateas characterized by the the resulting underconstrained problem machine learn problem available resources schedulable actions and ing b ecomes even more attractive current schedulepredict the users next scheduling op Scheduling an action involves four decisions the eration This can b e translated into a standard classi numb er of resources to allo cate to the action the sp e cation task with the class b eing the scheduling op er cic resources to allo cate the start time and the dura ation and the instance b eing the scheduling state for tion Scheduling in Inca takes place in a repair space which the prediction is b eing made where the op erators include adding and removing ac tions from the schedule as well as mo difying parame Baseline Study Eects of Learning ters of scheduled actions Sp ecically the user interacts We extracted the data for our learning exp eriments with Inca through a graphical interface that provides from the individual traces of two users each interacting the user with ve scheduling op erators add a new ac with Inca over HazMat problems Each schedul tion remove an action shift the start time of an action ing op eration p erformed while solving a problem corre change the duration of an action and switch an action sp onds to a training