This paper presents TPSYS, a Temporal Planning SYStem, which arises as an attempt to combine the ideas of Graphplan andTGP to solve temporal planning problems more efficiently. TPSYS is based on a three-stage process. The first stage, a preprocessing stage, facilitates the management of constraints on duration of actions. The second stage expands a temporal graph and obtains the set of temporal levels at which propositions and actions appear. The third stage, the plan extraction, obtains the plan of minimal duration by finding a proper flow of actions.

Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and use them for guiding search, in the hope to speed up the planning process. We go beyond the previous approaches by defining ordering constraints not only over the (top level) goals, but also over the sub-goals that will arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We show how such landmarks can be found, how their inherent ordering constraints can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant performance improvements in both heuristic forward search and Graphplan-style planning.

Several real world applications require planners that deal with  non-deterministic domains and with temporally extended goals. Recent  research is addressing this planning problem. However, the ability  of dealing in practice with large state spaces is still an open  problem. In this paper we describe a planning algorithm for extended goals  that makes use of BDD-based symbolic model checking  techniques. We implement the algorithm in the MBP planner,  evaluate its applicability experimentally, and compare it with  existing tools and algorithms. The results show that, in spite of the difficulty of the problem, MBP deals in practice with domains of large size and with  goals of a certain complexity.

We describe an incremental and adaptive approach to integrating hierarchical task network planning and constraint-based scheduling. The approach is grounded in the concept of approximating the ‘resource intensity’ of planning options. A given planning problem is decomposed into a sequence of (not necessarily independent) subtasks, which are planned and then scheduled in turn. During planning, operators are rated according to a heuristic estimate of their expected resource requirements. Options are selected that best match a computed ‘target intensity’ for planning. Feedback from the scheduler is used to adapt the target intensity after completion of each subplan, thus guiding the planner toward solutions that are tuned to resource availability. Experimental results from an air operations domain validate the effectiveness of the approach relative to typical waterfall models of planner/scheduler integration.

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems.

This volume contains the papers accepted for presentation at ECP 2001, the Sixth European Conference on Planning, held in Toledo, Spain, on September 12-14, 2001. ECP continued the traditional high standards of AIPS and ECP as an archival forum for new research in the field of automated planning and scheduling. ECP conferences were first organized in 1991.

The demonstration (ASC) will fly onboard the Air Force’s TechSat-21 constellation (an unclassified mission scheduled for launch in 2004). ASC will use onboard science analysis, replanning, robust execution, model-based estimation and control, and formation flying to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology. We offer a demonstration of the planning, scheduling, and execution framework for this application.

Recently, universal planning has become feasible through the use of efficient symbolic methods for plan generation and representation based on reduced ordered binary decision diagrams (OBDDs). In this paper, we address adversarial universal planning for multi-agent domains in which a set of uncontrollable agents may be adversarial to us (as in e.g. robotics soccer). We present two new OBDD-based universal planning algorithms for such adversarial nondeterministic finite domains, namely optimistic adversarial planning and strong cyclic adversarial planning. We prove and show empirically that these algorithms extend the existing family of OBDD- based universal planning algorithms to the challenging domains with adversarial environments. We further relate ad- verserial planning to positive stochastic games by analyzing the properties of adversarial plans when these are considered policies for positive stochastic games. Our algorithms have been implemented within the Multi-agent OBDD-based Planner, UMOP, using the Non-deterministic Agent Domain Language, NADL.

The operational traffic control problem comes up in a number of different contexts. It involves the coordinated movement of a set of vehicles and has by and large the flavor of a scheduling problem. In trying to apply scheduling techniques to the problem, one notes that this is a job-shop scheduling problem with blocking, a type of scheduling problem that is quite unusual. In particular, we will highlight a condition necessary to guarantee that job-shop schedules can be executed in the presences of the blocking constraint. Based on the insight that the traffic problem is a scheduling problem, we can derive the computational complexity of the operational traffic control problem and can design some algorithms to deal with this problem. In particular, we will specify a very simple method that works well in fast-time simulation contexts.

This paper describes the Dynamic Execution Order Scheduling (DEOS) system that has been developed to handle highly dynamic and interactive scheduling domains. Unlike typical scheduling problems which have a static task list, DEOS is able to handle dynamic task lists in which tasks are added, deleted and modified “on the fly" DEOS is also able to handle tasks with uncertain and/or probabilistic outcomes. DEOS extends the current scheduling paradigm to allow tasking in dynamic and uncertain environments by viewing the planning and scheduling tasks as being integrated and evolving entities. DEOS has been successfully applied to the domains of Air Campaign Planning (ACP) and Intelligence, Surveillance and Reconnaissance (ISR) management. The paper provides an overview of the dynamic task model and the “penalty box" scheduling algorithm which was developed to provide robust solutions to over constrained scheduling problems. The basic algorithm is described together with extensions to handle flexible time constraints.