Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to synthesize plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness and formalize the notion of plan robustness with respect to an incomplete domain model. We then show an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem, and present experimental results with Probabilistic-FF planner.

In many applications of shortest-path algorithms, it is impractical to find a provably optimal solution; one can only hope to achieve an appropriate balance between search time and solution cost that respects the user's preferences. Preferences come in many forms; we consider utility functions that linearly trade-off search time and solution cost. Many natural utility functions can be expressed in this form. For example, when solution cost represents the makespan of a plan, equally weighting search time and plan makespan minimizes the time from the arrival of a goal until it is achieved. Current state-of-the-art approaches to optimizing utility functions rely on anytime algorithms, and the use of extensive training data to compute a termination policy. We propose a more direct approach, called Bugsy, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. We describe a new method based on off-line parameter tuning and a novel benchmark domain for planning under time pressure based on platform-style video games. We then present what we believe to be the first empirical study of applying anytime monitoring to heuristic search, and we compare it with our proposals. Our results suggest that the parameter tuning technique can give the best performance if a representative set of training instances is available. If not, then Bugsy is the algorithm of choice, as it performs well and does not require any off-line training. This work extends the tradition of research on metareasoning for search by illustrating the benefits of embedding lightweight reasoning about time into the search algorithm itself.

In this paper, we investigate a novel application of online planning and scheduling: controlling an automated infeeder for a packaging line of food and consumer packaged goods. In this system, products arrive continuously at high-speed from the end of the production line and need to be arranged into a specific configuration for downstream primary and secondary packaging machines. In collaboration with a domain expert from the packaging industry, we developed an innovative design for a reconfigurable parallel infeed system using a matrix of interchangeable smart belts. We also adapted our online model-based Plantrol planner to this domain. Our planner can control various configurations of the new in-feed system through simulation both in nominal planning and when runtime failures occur. We are also building a small physical prototype to validate the new design and our sofeware framework.

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge.

We show how edge partitioning, a technique originally developed for external-memory search, can be used to reduce the number of slow synchronization operations needed in parallel graph search. We show that edge partitioning improves on a previous technique called parallel structured duplicate detection by allowing a higher degree of concurrency, even for search problems with little or no inherent locality. For domain-independent graph search, we also show that edge partitioning significantly improves search speed by improving the efficiency of precondition checking. We demonstrate the effectiveness of this approach to parallel graph search for domain-independent STRIPS planning.

In model-based production, a planner uses a system description to create plans that achieve production goals. The same description can be used by model-based diagnosis to infer the condition of components from sensor data. When production is realized by a sequence of plans, prior work has demonstrated that diagnosis can be used to adapt the plans to compensate for component degradation. However, the sources of diagnostic information are severely limited. Diagnosis must either make inferences from observations during production over which it has no control (passive diagnosis), or production must be halted to introduce diagnostic-specific plans (explicit diagnosis). We observe that the declarative nature of the model-based approach allows the planner to achieve production goals in multiple ways. This flexibility is exploited by a novel paradigm, i.e.,pervasive (active) diagnosis, which constructsinformative production plansthat simultaneously achieve production goals while uncovering additional diagnostic information about the condition of components. We present an efficient heuristic search for these informative production plans and show through experiments on a model of an industrial digital printing press that the theoretical increase in long-run productivity can be realized on practical real-time systems. We obtain higher long-run productivity than a decoupled combination of planning and diagnosis.

SAPA is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of SAPA using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of SAPA.