We present an algorithm for summarizing the metric resource requirements of an abstract task based on the resource usages of its potential refinements. We use this summary information within the ASPEN planner/scheduler to coordinate a team of rovers that conflict over shared resources. We find analytically and experimentally that an iterative repair planner can experience an exponential speedup when reasoning with summary information about resource usages and state constraints, but there are some cases where the extra overhead involved can degrade performance.

There is a strong demand for robots to work in environments, such as aircraft manufacturing, where they share tasks with humans and must quickly adapt to each other's needs. To do so, a robot must both infer the intent of humans, and must adapt accordingly. The literature to date has made great progress on these two tasks - recognition and adaptation - but largely as separate research activities. In this paper, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically.  Key to our approach is a task representation that uses choice to represent alternative plans for both the human and robot, allowing a single set of algorithms to simultaneously achieve recognition and adaptation. To achieve such fluidity, a labeled propagation mechanism is used where decisions made by the human and robot during execution are propagated to relevant future open choices, as determined by causal link analysis, narrowing the possible options that the human would reasonably take (hence achieving intent recognition) as well as the possible actions the robot could consistently take (adaptation). This paper introduces Pike, an executive for human-robot teamwork that quickly adapts and infers intent based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either robot or human).  We evaluate Pike's performance and demonstrate it on a household task in a human-robot team testbed.

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.

In classical planning, the polynomial-time computability of propositional delete-free planning (planning with only positive effects and preconditions) led to the highly successful Relaxed Graphplan heuristic. We present a hierarchy of new computational complexity results for different classes of propositional delete-free HTN planning, with two main results: We prove that finding a plan for the delete-relaxation of a propositional HTN problem is NP-complete: hence unless P=NP, there is no directly analogous GraphPlan heuristic for HTN planning. However, a further relaxation of HTN planning (delete-free HTN planning with task insertion) is polynomial-time computable. Thus, there may be a possibility of using this or other relaxations to develop search heuristics for HTN planning.

Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.

List of sponsors for the Twenty-Fourth International Conference on Automated Planning and Scheduling.

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 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.

Flexible support for crisis management can definitely be improved by making use of advanced planning capabilities. However, the complexity of the underlying domain often causes intractable efforts in modeling the domain as well as a huge search space to be explored by the system. A way to overcome these problems is to impose a structure not only according to tasks but also according to relationships between and properties of the objects involved, thereby using so-called decomposition axioms. We outline the prototype of a system that is capable of tackling planning for complex application domains. It is based on a well-founded combination of action and state abstractions. The paper presents the basic techniques and provides a formal semantic foundation of the approach. It introduces the planning system and illustrates its underlying principles by examples taken from the crisis management domain used in our ongoing project.

Many real world planning problems require goals with deadlines anddurative actions that consume resources. In this paper, we present Sapa, a domain-independent heuristic forward chaining planner thatcan handle durative actions, metric resource constraints, and deadlinegoals. The main innovation of Sapa is the set of distance basedheuristics it employs to control its search. We consider bothoptimizing and satisficing search. For the former, we identifyadmissible heuristics for objective functions based on makespan andslack. For satisficing search, our heuristics are aimed at scalabilitywith reasonable plan quality. Our heuristics are derived from the``relaxed temporal planning graph'' structure, which is ageneralization of planning graphs to temporal domains. We also providetechniques for adjusting the heuristic values to account for resourceconstraints. Our experimental results indicate that Sapa returnsgood quality solutions for complex planning problems in reasonabletime.