Mission Operations Planning: Beyond MAPGEN - IEEE Xplore

Mission Operations Planning: Beyond MAPGEN

John L. Bresina and Paul H. Morris NASA Ames Research Center [email protected] and [email protected] Abstract The MAPGEN system was deployed in the Mars Exploration Rover mission as a mission-critical component of the ground operations system. MAPGEN, which was jointly developed by ARC and JPL, represents a successful mission infusion of planning technology. The MER mission has operated spectacularly for over two years now, and we have learned valuable lessons regarding application of mixedinitiative planning technology to mission operations. These lessons have spawned new research in mixedinitiative planning and have influenced the design of a new ground operations system, called ENSEMBLE, that is base-lined for the Phoenix and Mars Science Laboratory missions. This paper discusses some of the lessons learned from the MER mission infusion experience and presents a preliminary report on these subsequent developments.

1. Introduction The MAPGEN (Mixed-initiative Activity Plan GENerator) system represents a successful mission infusion of planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover (MER) mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the Spirit and Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of the rover’s resources (e.g., power). The MAPGEN system is the result of integrating two legacy systems: APGEN, Activity Plan GENerator, [7] and EUROPA, Extendable Uniform Remote Operations Planning Architecture, [5]. APGEN is an interactive activity plan editor developed by the Jet Propulsion Laboratory in Pasadena, which is used as MAPGEN’s front end. EUROPA is a constraint-based planning framework developed at

NASA Ames Research Center. It is used to provide the core of MAPGEN’s plan representation and reasoning capabilities. A joint JPL-Ames team developed and deployed MAPGEN. The MER mission has now been operating with great success for over two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multi-year deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called ENSEMBLE, that is baselined for the Phoenix and Mars Science Laboratory (MSL) missions. Figure 1 shows the spacecraft from these three missions. After first giving some background information on the MAPGEN system, and its use within the MER mission, we describe some of the lessons learned from this mission-infusion experience. Then we present a preliminary report on the subsequent developments.

2. Background In this section, we first sketch MER’s command cycle, then secondly we give some background on the planning style employed by MAPGEN, and thirdly we explain how the TAPs used MAPGEN.

2.1. MER command cycle The daily commanding cycle in MER’s nominal mission proceeds as follows. The engineering and science data from the previous Martian day (sol) are analyzed to determine the status of the rover and its surroundings. Based on this, and on a strategic longerterm plan, the scientists determine a set of scientific objectives for the next sol. At this stage only rough resource guidance is available. Hence, the scientists are

2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06) 0-7695-2644-6/06 $20.00 © 2006

Figure 1: MER rover, Phoenix lander, and MSL rover encouraged to oversubscribe to ensure that the rover’s resources will be fully utilized. In the next step in the commanding process, the science observation requests are merged with the engineering requirements (e.g., testing the thermal profile of an actuator heater) and a detailed plan of activities is constructed for the upcoming sol. The plan must obey all applicable flight rules that specify how to safely operate the rover and its instrument suite and remain within specified resource limitations. It is in this step that a human operator, called the Tactical Activity Planner (TAP), employs the MAPGEN tool. Once approved, the activity plan is used as the basis to create sequences of low-level commands, which drive onboard execution. This sequence structure is then validated, packaged, and communicated to the rover. This completes the commanding cycle.

2.2. Constraint-based planning MAPGEN is based on a maturing combination of constraint reasoning technology and planning and scheduling technology. In this approach, pioneered in the Remote Agent Experiment on the Deep Space 1 mission [9], planning and scheduling are performed at the same time, using an underlying temporal constraint reasoning system to maintain a consistent schedule that satisfies applicable rules. In constraint-based planning, domain rules are specified in terms of activity/state patterns and constraint schemas. A given constraint schema is applied to any instance matching the associated pattern. Search methods and other techniques for manipulating partial plans then build on this framework. Consistency of the developing plan is maintained using an underlying simple temporal constraint network, or STN [4]. One advantage of STNs is that rather than doing simple consistency checking, they

work by eliminating inconsistent values from variable domains. Specifically, they maintain arc-consistency, which for STNs is equivalent to full consistency. In effect, they maintain a family of related solutions, called a flexible solution, rather than just a single grounded solution. A flexible solution provides flexibility because it can often merely be refined, i.e., further restricted, in response to additional constraints instead of requiring search for a new solution. The temporal constraints in MAPGEN fall into three categories: model constraints, problem-specific constraints, and expedient constraints. Model constraints encompass domain definitions and mutualexclusion flight rules. For example, do not move the arm while the rover is moving or more than one activity cannot simultaneously point the rover’s mast. The problem-specific constraints comprise relations between specific activities in a planning problem instance. In MER, these constraints are used to ensure that science objectives are satisfied and that the data collected are scientifically useful; thus, for this domain, we also refer to these constraints as science constraints. The scientists use two types of problem-specific constraints: temporal bounds and temporal ordering relations. The temporal bounds are typically constraints on when an activity can start due to, for example, lighting conditions or temperature. The typical ordering relations are constraints between the end of one activity and the start of another. For example, a hazcam documentation image of an arm placement must be taken at least 2 minutes after the arm is placed (to ensure vibrations have subsided) and before it is moved again. Another more complex example is that a pancam imaging activity must be within 30 minutes of its associated calibration activity, but they can occur in either order. Expedient constraints are typically added during search in automated planning. For example, a model


constraint might specify that two activities, A and B, are mutually exclusive. Thus, either A must precede B or B must precede A. The choice between the two orderings is made arbitrarily.

2.3. Using MAPGEN In the collaboration with MAPGEN, the primary role of the TAP is to direct and focus plan construction and to provide qualitative evaluation of plans. The intended interaction between user and system is that the system handles constraint enforcement constantly in the background, while automated plan construction operations are user initiated. MAPGEN provides a spectrum of automated planning services with different degrees of automation and human guidance. The TAPs tend to build plans in an incremental fashion, checking the energy resource usage by invoking an external power-thermal detailed modeler every now and then. The science requests that are not currently in the plan are kept in a separate display window, called the hopper. Due to this incremental approach, the TAPs often apply the planselected operation. With this operation, the user can select a set of observation requests not in the plan and request that these be inserted anywhere into the current partial plan, such that all constraints are satisfied. The user can exercise even more control over the planning process via the place-selected operation, which is applicable only to individual activities. This operation allows the user to select an activity in the hopper and then choose an approximate temporal placement for it in the plan. The planning algorithm then treats the user-chosen time as heuristic guidance and searches for a plan where the selected activity is as close to the desired time as possible. The system also supports an activity movement operation, called constrained-move, which takes advantage of the flexibility in the STN. As long as an activity is moved only within the flexibility range defined by the domain in the underlying arc-consistent flexible plan, the result is necessarily another consistent instantiation. During a constrained move, the system actively restricts the movements of the selected activity to stay within the permitted range. Then, once the user places the activity, any dependent activity is automatically updated as necessary to yield a new valid plan instance. Note that the consistency enforcement takes into account all the constraints that determine the flexible plan, including expedient constraints that arbitrarily order activities. Although MAPGEN constructs flexible plans, the plan that is displayed to the user is a grounded

solution; i.e., a specific consistent instantiation of the underlying flexible plan. This is selected to be as close as possible to an internally maintained reference schedule that is initially set by the user. The reference schedule is used to support a minimum perturbation approach, where planner-initiated changes to the previous plan are minimized. This is accomplished by continually updating the reference schedule to reflect the evolving plan. This means that changes made by the TAP, to eliminate problems or improve the quality of the solution, are respected and maintained if possible. In addition, an activity can be pinned to guarantee that it will not be moved at all. The planning process is an incremental one in which the user interleaves automatic plan generation and plan editing phases. Each planning operation is done in the context of the current plan and its constraints; thus, previous planning decisions affect what future operations are possible and what additional activities will fit in the plan. This incremental commitment helps MAPGEN achieve a fast response time; a satisfactory plan is available at an early time, with additional time devoted to improving the plan quality. Another advantage is that the human planner better understands a gradually developed plan.

3. Lessons learned Infusing technology into an actual mission requires a certain degree of conservatism. Much of the novelty in MAPGEN lies in the architecture of the system rather than in developing radically new methods and techniques. However, this work leads to insights into what kinds of techniques and capabilities are needed in the future. It became clear that a mixed-initiative system was the right choice for reasons beyond those that led to its adoption. The human component provided for adaptability and flexibility in the use of the tool that allowed us to cope with evolving and changing requirements. Moreover, the ground operations process is not perfect, and the mixed-initiative framework provided scope for workarounds to deal with shortcomings, perhaps temporary, in other areas. Another lesson we learned is that planning in the traditional sense is not necessarily the most prized feature from the user’s perspective. At least as important (perhaps more so) are features like unplanning, replanning, active constraint enforcement, and constrained moves. Moreover, some relatively mundane planning, such as determining when the CPU must be on, may be more valuable to the user than complex, context-sensitive goal achievement.


One thing that is clear is a need for the automated reasoning component to provide better explanations of its behavior, particularly explanations of why the planner could not achieve something, such as inserting an activity in the plan at a particular time, or moving an activity beyond the enforced limit. Such a facility would have greatly helped during training, in addition to increasing the TAPs effectiveness during operations. The MAPGEN system provides a mechanism for actively enforcing temporal constraints needed to coordinate scientific observations. However, in many cases, the scientists also had temporal preferences that could be overridden if necessary. In MER, the human operator of MAPGEN had the responsibility of balancing these preferences, which could be timeconsuming. Thus, there is a clear need for automated assistance in this area. In addition to temporal preferences, users may have preferences regarding the global characteristics of the solution, such as plan structure preferences or resource usage preferences. Many constraints can have absolute validity limits and a preference on the legal values. For example, the limits on the energy usage may be determined by minimum battery levels, but it is preferred that the battery be left charged above a certain level at the end of the plan. As with temporal preferences, the main issues are how to combine local preferences into global evaluations functions and how to then control the search towards preferred plans. Despite its benefits, the incremental approach caused some difficulties in MAPGEN because commitments (prematurely) made in a previous automatic planning phase could prevent a new activity fitting into the plan. For example, if the planner has previously satisfied a mutual exclusion requirement by ordering activity A before activity B, this might prevent a later insertion of activity C if it needed to come after B but before A. In MAPGEN, the underlying plan is always kept consistent. This allows propagation to take place at any time, which in turn enables active constraint enforcement, constrained moves, and other propagationbased capabilities. However, users sometimes desire to “temporarily” work with plans that violate rules or constraints. One response to an inability to plan an activity due to a flight rule violation might be to waive the flight rule under specific circumstances. It became clear there was occasionally a need to temporarily disable obsolete or mistaken flight rules pending updates to the model. The MAPGEN system is part of a larger set of loosely coupled tools. The desire for greater

unification of these tools has led to a requirement for a constraint-maintenance system to shoulder additional responsibilities. In particular, it needs to have the option of passively detecting but tolerating flight rule and constraint violations in addition to the current capability of actively preventing such violations.

4. Beyond MAPGEN The post-MAPGEN developments reported in this paper have occurred in the context of two subsequent projects. One involves developing a new groundsoftware framework called ENSEMBLE for the upcoming MSL and Phoenix missions. A joint JPL/Ames team is developing this system. The second new project is a research effort entitled, “MixedInitiative Tactical Mission Planning” (MITMP). This project is a collaboration between Ames and SRI that explores the use of explanations and preferences in mixed-initiative planning (see [1]). One of the principal drivers for the ENSEMBLE work is a desire to unify the disparate tools used in MER, while providing a better user interface. It also incorporates improved versions of the components, and uses ECLIPSE to integrate the separate modules. The ENSEMBLE project is a large effort that involves post-MER systems in general. We confine the discussion in this paper to issues involving constraint reasoning and planning. From the planner viewpoint, the MSL and Phoenix applications of ENSEMBLE have a similar architecture to MAPGEN, where the GUI features of APGEN have been transferred to a new module called SPIFe. With regard to planner capabilities, one of the significant developments has been the inclusion of EUROPA2, the next generation version of the EUROPA planner used in MAPGEN. This new core planner supports the modeling of state and other resources. This is key to handling passive detection of flight rule violations and, furthermore, providing the culprits that contribute to a violation at a particular time. The EUROPA2 core tool also provides a search engine that is customizable in terms of goal-selection and order-selection heuristics. Compared to MAPGEN, this has greatly simplified the interface module that connects the user-oriented plan-editing tool to the core plan reasoning system. It has also allowed the code to be application independent. For example, the tool can be switched between MSL and Phoenix applications by simply substituting a different declarative model. The ENSEMBLE planner module partially sidesteps the failure explanation issue discussed earlier by


automatically redoing planner decisions in order to fit more activities into the plan while simultaneously respecting previous solutions via heuristics. The MITMP project more directly confronts the research issues involved in explaining plan-insertion failures, incorporating soft science preferences, and performing an optimizing search. This work has been done using prototypes derived from the MAPGEN system, as well as standalone modules interfaced to Europa2. In the remainder of this paper, we describe more fully the new capabilities being explored in these projects.

4.1. Constraint enforcement In the AI literature, planning and scheduling systems generally enforce constraints actively. That is, they look for solutions where the constraints are satisfied. However, space operations personnel typically desire more control over the planning process, and request an optional operational mode where certain constraint violations are permitted temporarily. It is crucial, however, that such violations are detected and reported to the user. We refer to this detect-and-report mode as passive constraint enforcement. For example, suppose a drive activity is moved to a time where it overlaps with the current positioning of a communications activity, and suppose a flight rule forbids such overlap. With passive enforcement, the system merely flags the violation; on the other hand with active enforcement, either the drive or the communications activity or both will be moved to avoid the overlap. The Europa2 system does not directly support passive enforcement of constraints. However, it provides a mechanism for representing and reasoning about numerical resource transactions. Furthermore, it has the capability of detecting and reporting flaws that are points in the plan where the capacity of a resource is potentially exceeded. For the ENSEMBLE work, we are interested in two types of passive violation detection, corresponding to typical Mars Rover flight rules. First, we wish to support claims where only one activity can use a rover facility, such as the arm, at a given time. This translates in a straightforward way to a unit capacity reusable resource in Europa2. Second, we wish to support state requirements (e.g., preconditions) and transitions between states. For example, a rover drive activity requires that the state of the rover arm be stowed throughout the drive’s duration, and the two activities which can cause a transition on this state are STOW and UNSTOW. States are represented by using

a separate resource to keep track of the presence or absence of each state. Each of these is implemented as a multi-capacity resource and effectively simulates a sharable resource that can be switched on and off. While passive enforcement is a desired option, an active enforcement option is also desired. In the active enforcement mode, more fine-grained controls are desired for disabling and enabling flight rules. In Europa2, the activation of constraints can be made conditional on the values of activity parameters or global variables. We have used this feature to provide a means for the user to control which flight rules are enforced by the planner. Specifically, the user can disable a specified flight rule for all activities or all flight rules for a specified activity.

4.2. Explanations of inconsistencies When science constraints are created in MER (using a separate Constraint Editor tool), an immediate consistency check occurs and the user is prompted to correct any problems. This ensures that the science constraints are consistent with each other. However, they are not necessarily consistent with the expedient constraints, which are created during planning. When the user asks for a new activity to be inserted into the plan, it is therefore possible that newly activated science constraints on the inserted activity will conflict with the constraints (including expedient constraints) on activities already in the plan. In MAPGEN, this causes a planning failure and the TAP is sometimes left frustrated by not understanding why. When a failure of this kind occurs, MAPGEN extracts a temporal nogood, or minimally inconsistent set of constraints, which may be regarded as a low-level “explanation” of the failure. However, such nogoods are complicated and often contain hundreds of constraints, making them of little use to a timepressured TAP. In the MITMP project, we have explored ways of automatically rendering an understandable explanation for such failures. Part of the reason the nogoods from MER are so lengthy is because constraints on highlevel activities are implemented in terms of constraints on lower-level sub-activities, and the nogood involves all these low-level constraints. Thus, one step in our explanation process is to compress the nogood by combining low-level subchains. For raw MER examples, the compression step typically achieves close to an order of magnitude reduction in size. The next step selects key features of the nogood and presents them to the user.


We consider one example in detail to illustrate the explanation process. Here, the original MER nogood contains 49 edges (i.e., active constraints). The compression step reduces it to 9 edges. We can summarize the compressed nogood as indicating the following coordinated sequence of activities, involving two types of spectrometers, APXS and MB, on the rover’s arm for contact science. Plan-StartAPXS_1APXS_2MB The nogood also contains an expedient constraint that orders MB_ON, a sub-activity of the high-level MB, to end before the start of a UHF activity (i.e., communication with an orbiter). The nogood further indicates that both Plan-Start and UHF are pinned in such a way that the sequence is squeezed into a region smaller than its total duration, hence the conflict. This nogood arose when the TAP tried to bring APXS_1 into the plan. Its science constraints conflicted with constraints associated with the other activities in the plan. We note that a previous planner decision ordered MB_ON before the UHF communication, because they cannot overlap. This has the effect that there is not enough room for the APXS_1, which must come before the sequence of APXS_2 followed by MB. In generating the summary explanation, the system emphasizes the offending expedient constraint and also the newly activated constraints on the activity whose insertion is being attempted: For APXS_1 to fit in the plan, the planner would need to slide Start APXS_2 to no earlier than 31159 seconds after Plan_Start, because of science constraints requiring Start APXS_1 to be no earlier than Plan_Start and Start APXS_2 to be no earlier than End APXS_1. Currently, Start APXS_2 is barred from going later than 14794 seconds after Plan_Start, because of planner orderings involving End MB_ON before Start UHF together with science constraints or pins. The system provides a number of user operations that can remove expedient constraints. In addition to the explanation, it generates a recommendation on how the user might correct the problem, for example by unplanning certain activities and then replanning. In the MSL application, we have adopted a different tack in dealing with the expedient constraints. Instead of explaining why they cause inconsistencies and leaving it to the user to select an appropriate remedy, we have empowered the planner to redo the expedient

decision-making afresh with each new invocation of the planner, while at the same time trying to minimize changes from the prior plan. This has both the advantage and disadvantage of involving the user less in the corrective decision-making. In terms of our example, this means that when the user asks for APXS_1 to be planned, the first step the system takes is to delete all the expedient constraints, including the one ordering MB_ON before UHF. It then tries to insert APXS_1 and the other activities as close to their previous positions as possible. The order in which it plans activities depends on their priorities. If it plans the other activities before MB, it will then determine immediately that MB must come after UHF. However, suppose that it first plans the MB and again tries to place the MB_ON before the UHF. In that case, the subsequent failure of the APXS_1 or APXS_2 will cause backtracking that will lead to the MB_ON being placed after the UHF. We note that revision of the MB_ON ordering can occur here because the undesirable ordering was made during the current planning episode and not in a previous episode.

4.3. Commitment and foresight One interesting contrast between the approaches of the MSL application and MAPGEN is in the area of f o r e s i g h t (see [2]). Due to the incremental commitments made during the collaborative planning process in MAPGEN, care must be taken that commitments made in earlier planning episodes are not premature; that is, that they do not preclude getting other activities into the plan during subsequent planning episodes. The foresight technique addresses this issue through the reference schedule, which biases the scheduling decisions made by the planner via the minimum perturbation mechanism. In MAPGEN, the reference schedule takes into account the science constraints associated with all the activities, including those in the hopper. The intention is to bias the ordering of activities to avoid precluding activities that may be planned later. However, one side-effect of using this reference schedule is that it may arrange the earlier-planned activities in a way that looks strange to the TAP; for example, leaving large gaps between activities, because the schedule now leaves room for all the hopper activities. This is particularly bothersome if some of the activities are not, after all, included in the final plan due to, for example, limitations on the power budget for the sol. The premature commitment problem does not occur in the MSL application because the previous expedient constraints are flushed and the ordering decisions are


made afresh in each new planning episode. However, a more limited version of the foresight technique is used, as a performance enhancement to avoid excessive backtracking, within a single planning episode that plans several goals simultaneously. It is more limited because the reference schedule is based on only the science constraints associated with the set of activities to be currently planned; the activities in the hopper have no influence on the reference schedule.

4.4. Preferences and optimization In MAPGEN, the user’s only language for specifying their desires is to create a set of absolute (hard) temporal constraints, which represent what is necessary for the observation requests to be scientifically useful. These constraints can specify ordering among the activities and observations (along with temporal distances required) and can specify that an activity or observation must be scheduled within a particular time window. For example, the scientist can specify that three atmospheric imaging activities have to be a minimum of thirty minutes apart and a maximum of six hours apart. However, the scientist cannot specify that they prefer the largest possible spacing between the three activities. Likewise, they cannot specify that a particular spectrometer reading must occur between 10:00 and 15:00 but it is preferred to be as near to 12:00 as possible. It is clear that both absolute constraints and temporal preferences are needed to generate a highquality science activity plan. Unfortunately, the latter are not explicitly modeled or enforced in MAPGEN, although the TAP can try to satisfy temporal preferences using move operations. The reference schedule mechanism itself may be viewed as a primitive temporal preference facility, but it only handles unary preferences; i.e., the absolute placement of a single activity, not its relative placement with respect to other activities. Furthermore it relies on a greedy local optimization process with no global quality guarantees. Moreover, the precise outcome of the process depends on the arbitrary order in which the local optimization occurs. The TAP can also establish more complex preferences by an iterative process of relaxing or tightening hard constraints. This method can be very time-consuming. We are currently investigating a number of alternative, automated approaches to incorporating temporal preferences more explicitly into MAPGEN. We have extended the Constraint Editor to allow specifying temporal preferences on an activity’s start or end time, as well as on distances between start/end time

points of two activities. In particular, we have enhanced the Constraint Editor tool to allow specification of a sweet spot in addition to a base constraint. The sweet spot is an interval of maximum preference. Outside the interval, the preference drops linearly from its maximum value. There are three key issues involved in utilizing temporal preferences in planning. The first is the common problem of combining local preferences into a global evaluation function. The second issue is finding a globally optimal instantiation of a given flexible plan. The third key issue is searching for a flexible plan that yields a globally preferred instantiation. In recent basic research [6; 8], the basic STN model has been extended to incorporate temporal preferences. This provides a potential opportunity to explicitly handle user preferences in future MAPGEN-like tools. The STN extensions provide solutions to the first and second issues in the form of utilitarian optimization, which seeks to maximize the total (summed) preference, and stratified egalitarian optimization, which seeks to satisfy competing preferences as equally as possible. Stratified egalitarian optimization may be well suited for specialized applications, such as ensuring that a sequence of events is close to being equally spaced. We are incorporating these preference-optimization methods into MAPGEN and plan to employ them for a number of purposes. One use is to apply the optimization, as a post-process, to the family of solutions represented by a flexible MAPGEN plan in order to display the most-preferred solution to the user. These methods can also be employed in a preprocessing step to compute the reference schedule as a globally optimal solution to the specified temporal preferences. In order to continue to incorporate the original feature of minimal perturbation, we can represent this by means of a set of additional temporal preferences, where the reference time is treated as the sweet spot. The relative influence, on the reference schedule, of the science preferences and the minimalperturbation preferences can be adjusted via the preference weights. With this enhanced reference schedule, the minimalperturbation method biases the planning to stay close to the globally optimal schedule. We are also investigating heuristic methods that include explicit consideration of the preferences when making search decisions; thus, addressing the third key issue. Although the temporal preference computation is tractable (non-exponential), the performance overhead is greater than for standard constraint techniques.


Experiments are underway to determine the performance/quality tradeoff in using these techniques in preprocessing or post-processing steps, or throughout the planning process. In the above discussion, we focused on work that seeks to optimize temporal preferences in isolation. Additional issues arise when we combine temporal preferences with other preferences such as maximizing the number and priority of observations. In this case, a tractable optimization algorithm is not available and search is necessary to find a solution. To find a good solution, a greedy algorithm may suffice. In that case, the existing MAPGEN planning algorithm can be combined with heuristic choices that reflect the temporal preferences, using the optimization techniques described in the previous section. If it is desired to find a true optimal solution, some form of optimizing search such as branch-and-bound is required. We are currently exploring an optimization search that is an any-time approach where better and better solutions are available over time, and eventually a provably optimal solution is reached. Furthermore, at each any-time step, in addition to the best solution found thus far, the algorithm returns an optimality bound indicating how close to optimal the solution is. In the future, we plan to empirically compare the various methods of handling preferences to help determine an appropriate trade-off between computational effort and solution quality.

5. Concluding remarks Our mission-infusion experience with deploying and operating MAPGEN in the MER mission has been invaluable in the lessons learned and in suggesting a number of potential areas for improvement. These issues have been explored both in the context of engineering a next-generation tool and, at a deeper level, in providing a stimulus for longer-term research. We have presented an interim report on this work, which is still in progress at the current time. It is hoped that this opportunity of using actual mission experience to generate new insights will lead to fruitful advances in both theory and practice.

5.1 Acknowledgements The authors would like to acknowledge the MAPGEN team, which includes: Mitch Ai-Chang, Len Charest, Brian Chafin, Adam Chase, Kim Farrell, Jennifer Hsu, Ari Jónsson, Bob Kanefsky, Adans Ko, Pierre Maldague, Kanna Rajan, Richard Springer, and Jeffrey Yglesias. Secondly, we would like to

acknowledge the entire ENSEMBLE team, and in particular those who played a role in the planning facilities: Andrew Bachmann, Kevin Greene, and Michael McCurdy. Thirdly, we would like to acknowledge the research contributions of our collaborators on the MITMP project: Lina Khatib, Conor McGann, Karen Myers, and Michael Wolverton.

6. References [1] J. Bresina, A. Jónsson, P. Morris, K. Rajan. “MixedInitiative Planning in MAPGEN: Capabilities and Shortcomings.” Workshop on Mixed-Initiative Planning and Scheduling, ICAPS05, 2005. [2] J. Bresina, A. Jónsson, P. Morris, and K. Rajan, “Activity Planning for the Mars Exploration Rovers”, Fourteenth International Conference on Automated Planning and Scheduling, Monterey, 2005, pp. 40-49. [3] T. Cormen, C. Lieserson, R. and Rivest, Introduction to Algorithms, MIT Press/McGraw-Hill, 1990. [4] R. Dechter, I. Meiri, and J. Pearl, “Temporal constraint networks”, Artificial Intelligence, 49, May 1991, pp. 61– 95. [5] A. K. Jónsson, P. H. Morris, N. Muscettola, and K. Rajan, “Next generation Remote Agent planner”, Proceedings of the Fifth International Symposium o n Artificial Intelligence, Robotics and Automation in Space (iSAIRAS99), 1999. [6] L. Khatib, P. Morris, R. Morris, and B. Venable, “Tractable pareto optimization of temporal preferences.” Eighteenth International Joint Conference on AI, Acapulco, Mexico, 2003. [7] P. Maldague, A. Ko, D. Page, and T. Starbird, “APGEN: A multi-mission semi-automated planning tool.” First International NASA Workshop on Planning and Scheduling, Oxnard, CA, 1998. [8] P. Morris, R. Morris, L. Khatib, S. Ramakrishnan, and A. Bachmann, “Strategies for Global Optimization of Temporal Preferences”, Tenth International Conference o n Principles and Practices of Constraint Programming (CP2004), Toronto, Canada, 2004. [9] N. Muscettola, P. Nayak, B. Pell, and B. Williams, “Remote Agent: To Boldly Go Where No AI System Has Gone Before”, Artificial Intelligence, 103(1/2), 1998.
