Patrick Eyerich

ICAPS Conference 2013 Conference Paper

Domain Predictive Control Under Uncertain Numerical State Information

• Johannes Löhr
• Patrick Eyerich
• Stefan Winkler 0004
• Bernhard Nebel

In planning, hybrid system states consisting of logical and numerical variables are usually assumed to be completely known. In particular, for numerical state variables full knowledge of their exact values is assumed. However, in real world applications states are results of noisy measurements and imperfect actuators. Therefore, a planned sequence of state transitions might fail to lead a hybrid system to the desired goal. We show how to propagate and reason about uncertain state information directly in the planning process, enabling hybrid systems to find plans that satisfy numerical goals with predefined confidence.

ICAPS Conference 2013 Conference Paper

Stronger Abstraction Heuristics Through Perimeter Search

• Patrick Eyerich
• Malte Helmert

Perimeter search is a bidirectional search algorithm consisting of two phases. In the first phase, a limited regression search computes the perimeter, a region which must necessarily be passed in every solution. In the second phase, a heuristic forward search finds an optimal plan from the initial state to the perimeter. The drawback of perimeter search is the need to compute heuristic estimates towards every state on the perimeter in the forward phase. We show that this limitation can be effectively overcome when using pattern database (PDB) heuristics in the forward phase. The combination of perimeter search and PDB heuristics has been considered previously by Felner and Ofek for solving combinatorial puzzles. They claimed that, based on theoretical considerations and experimental evidence, the use of perimeter search in this context offers "limited or no benefits". Our theoretical and experimental results show that this assessment should be revisited.

ICAPS Conference 2012 Conference Paper

A Planning Based Framework for Controlling Hybrid Systems

• Johannes Löhr
• Patrick Eyerich
• Thomas Keller 0001
• Bernhard Nebel

The control of dynamic systems, which aims to minimize the deviation of state variables from reference values in a continuous state space, is a central domain of cybernetics and control theory. The objective of action planning is to find feasible state trajectories in a discrete state space from an initial state to a state satisfying the goal conditions, which in principle addresses the same issue on a more abstract level. We combine these approaches to switch between dynamic system characteristics on the fly, and to generate control input sequences that affect both discrete and continuous state variables. Our approach (called Domain Predictive Control) is applicable to hybrid systems with linear dynamics and discretizable inputs.

ECAI Conference 2012 Conference Paper

Preferring Properly: Increasing Coverage while Maintaining Quality in Anytime Temporal Planning

• Patrick Eyerich

Temporal Fast Downward (TFD) is a successful temporal planning system that is capable of dealing with numerical values. Rather than decoupling action selection from scheduling, it searches directly in the space of time-stamped states, an approach that has shown to produce plans of high quality at the price of coverage. To increase coverage, TFD incorporates deferred evaluation and preferred operators, two search techniques that usually decrease the number of heuristic calculations by a large amount. However, the current definition of preferred operators offers only limited guidance in problems where heuristic estimates are weak or where subgoals require the execution of mutex operators. In this paper, we present novel methods for refinement of this definition and show how to combine the diverse strengths of different sets of preferred operators using a restarting procedure incorporated into a multi-queue best-first search. These techniques improve TFD's coverage drastically and preserve the average solution quality, leading to a system that solves more problems than each of the competitors of the temporal satisficing track of IPC 2011 and clearly outperforms all of them in terms of IPC score.

ICAPS Conference 2012 Conference Paper

PROST: Probabilistic Planning Based on UCT

• Thomas Keller 0001
• Patrick Eyerich

We present PROST, a probabilistic planning system that is based on the UCT algorithm by Kocsis and Szepesvari (2006), which has been applied successfully to many areas of planning and acting under uncertainty. The objective of this paper is to show the application of UCT to domain- independent probabilistic planning, an area it had not been applied to before. We furthermore present several enhance- ments to the algorithm, including a method that is able to drastically reduce the branching factor by identifying super- fluous actions. We show how search depth limitation leads to a more thoroughly investigated search space in parts that are influential on the quality of a policy, and present a sound and polynomially computable detection of reward locks, states that correspond to, e. g. , dead ends or goals. We describe a general Q-value initialization for unvisited nodes in the search tree that circumvents the initial random walks inher- ent to UCT, and leads to a faster convergence on average. We demonstrate the significant influence of the enhancements by providing a comparison on the IPPC benchmark domains.

ICAPS Conference 2011 Conference Paper

A Polynomial All Outcome Determinization for Probabilistic Planning

• Thomas Keller 0001
• Patrick Eyerich

Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. In this paper, we present a method to map probabilistic operators to an equivalent set of probabilistic operators in a novel normal form, requiring polynomial time and space. From this, we directly derive a determinization which can be used for, e. g. , replanning strategies incorporating a classical planning system. Unlike previously described all outcome determinizations, the number of deterministic operators is not exponentially but polynomially bounded in the number of parallel probabilistic effects, enabling the use of more sophisticated determinization-based techniques in the future.

ICAPS Conference 2010 Conference Paper

Coming Up With Good Excuses: What to do When no Plan Can be Found

• Moritz Göbelbecker
• Thomas Keller 0001
• Patrick Eyerich
• Michael Brenner 0001
• Bernhard Nebel

When using a planner-based agent architecture, many things can go wrong. First and foremost, an agent might fail to execute one of the planned actions for some reasons. Even more annoying, however, is a situation where the agent is incompetent, i. e. , unable to come up with a plan. This might be due to the fact that there are principal reasons that prohibit a successful plan or simply because the task's description is incomplete or incorrect. In either case, an explanation for such a failure would be very helpful. We will address this problem and provide a formalization of coming up with excuses for not being able to find a plan. Based on that, we will present an algorithm that is able to find excuses and demonstrate that such excuses can be found in practical settings in reasonable time.

IROS Conference 2010 Conference Paper

Coordinated exploration with marsupial teams of robots using temporal symbolic planning

• Kai M. Wurm
• Christian Dornhege
• Patrick Eyerich
• Cyrill Stachniss
• Bernhard Nebel
• Wolfram Burgard

The problem of autonomously exploring an environment with a team of robots received considerable attention in the past. However, there are relatively few approaches to coordinate teams of robots that are able to deploy and retrieve other robots. Efficiently coordinating the exploration with such marsupial robots requires advanced planning mechanisms that are able to consider symbolic deployment and retrieval actions. In this paper, we propose a novel approach for coordinating the exploration with marsupial robot teams. Our method integrates a temporal symbolic planner that explicitly considers deployment and retrieval actions with a traditional cost-based assignment procedure. Our approach has been implemented and evaluated in several simulated environments and with varying team sizes. The results demonstrate that our proposed method is able to coordinate marsupial teams of robots to efficiently explore unknown environments.

ICAPS Conference 2010 Conference Paper

G-Value Plateaus: A Challenge for Planning

• J. Benton 0001
• Kartik Talamadupula
• Patrick Eyerich
• Robert Mattmüller
• Subbarao Kambhampati

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.

SoCS Conference 2010 Conference Paper

High-Quality Policies for the Canadian Traveler's Problem

• Patrick Eyerich
• Thomas Keller 0001
• Malte Helmert

We consider the stochastic variant of the Canadian Traveler

AAAI Conference 2010 Conference Paper

High-Quality Policies for the Canadian Traveler’s Problem

• Patrick Eyerich
• Thomas Keller
• Malte Helmert

We consider the stochastic variant of the Canadian Traveler’s Problem, a path planning problem where adverse weather can cause some roads to be untraversable. The agent does not initially know which roads can be used. However, it knows a probability distribution for the weather, and it can observe the status of roads incident to its location. The objective is to find a policy with low expected travel cost. We introduce and compare several algorithms for the stochastic CTP. Unlike the optimistic approach most commonly considered in the literature, the new approaches we propose take uncertainty into account explicitly. We show that this property enables them to generate policies of much higher quality than the optimistic one, both theoretically and experimentally.

ICAPS Conference 2009 Conference Paper

Semantic Attachments for Domain-Independent Planning Systems

• Christian Dornhege
• Patrick Eyerich
• Thomas Keller 0001
• Sebastian Trüg
• Michael Brenner 0001
• Bernhard Nebel

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

ICAPS Conference 2009 Conference Paper

Using the Context-enhanced Additive Heuristic for Temporal and Numeric Planning

• Patrick Eyerich
• Robert Mattmüller
• Gabriele Röger

Planning systems for real-world applications need the ability to handle concurrency and numeric fluents. Nevertheless, the predominant approach to cope with concurrency followed by the most successful participants in the latest International Planning Competitions (IPC) is still to find a sequential plan that is rescheduled in a post-processing step. We present Temporal Fast Downward (TFD), a planning system for temporal problems that is capable of finding low-makespan plans by performing a heuristic search in a temporal search space. We show how the context-enhanced additive heuristic can be successfully used for temporal planning and how it can be extended to numeric fluents. TFD often produces plans of high quality and, evaluated according to the rating scheme of the last IPC, outperforms all state-of-the-art temporal planning systems.

KR Conference 2008 Conference Paper

On the Complexity of Planning Operator Subsumption

• Patrick Eyerich
• Michael Brenner
• Bernhard Nebel

Formal action models play a central role in several subfields of AI because they are used to model application domains, e. g., in automated planning. However, there are hitherto no automated methods for relating such domain models to each other, in particular for checking whether one is a specialization or generalization of the other. In this paper, we introduce two kinds of subsumption relations between operators, both of which are suitable for modeling and verifying hierarchies between actions and operators: applicability subsumption considers an action to be more general than another if the latter can be replaced by the first at each point in each sound sequence of actions; abstraction subsumption exploits relations between actions from an ontological point of view. For both kinds of subsumption, we prove complexity results for verifying operator subsumption in three important subclasses: The problems are NP-complete when the expressiveness of the operators is restricted to the well-known basic STRIPS formalism, Σp2-complete when we admit boolean logical operators and undecidable when the full power of the planning language ADL is permitted.

IJCAI Conference 2007 Conference Paper

• Jens Cla
• szlig; en
• Patrick Eyerich
• Gerhard Lakemeyer
• Bernhard Nebel

The action language Golog has been applied successfully to the control of robots, among other things. Perhaps its greatest advantage is that a user can write programs which constrain the search for an executable plan in a flexible manner. However, when general planning is needed, Golog supports this only in principle, but does not measure up with state-of-the-art planners. In this paper we propose an integration of Golog and planning in the sense that planning problems, formulated as part of a Golog program, are solved by a modern planner during the execution of the program. Here we focus on the ADL subset of the plan language PDDL. First we show that the semantics of ADL can be understood as progression in the situation calculus, which underlies Golog, thus providing us with a correct embedding of ADL within Golog. We then show how Golog can be integrated with an existing ADL planner for closed-world initial databases and compare the performance of the resulting system with the original Golog.