Publikationen

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Aktuelle Veröffentlichungen (letzte 12 Monate):

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Bernhard Nebel.
On the Computational Complexity of Multi-Agent Pathfinding on Directed Graphs.
In Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020). 2020.
To appear.
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The determination of the computational complexity of multi-agent pathfinding on directed graphs has been an open problem for many years. For undirected graphs, solvability can be decided in polynomial time, as has been shown already in the eighties. Further, recently it has been shown that a special case on directed graphs can be decided in polynomial time. In this paper, we show that the problem is NP-hard in the general case. In addition, some upper bounds are proven.
Gregor Behnke, Daniel Höller, Alexander Schmid, Pascal Bercher und Susanne Biundo.
On Succinct Groundings of HTN Planning Problems.
In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). 2020.
To appear.
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Both search-based and translation-based planning systemsusually operate on grounded representations of the problem. Planning models, however, are commonly defined using lifted description languages. Thus, planning systems usually generate a grounded representation of the lifted model as a pre-processing step. For HTN planning models, only one method to ground lifted models has been published so far. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method.
David Speck, Robert Mattmüller und Bernhard Nebel.
Symbolic Top-k Planning.
In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). 2020.
To appear.
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The objective of top-k planning is to determine a set of k different plans with lowest cost for a given planning task. In practice, such a set of best plans can be preferred to a single best plan generated by ordinary optimal planners, as it allows the user to choose between different alternatives and thus take into account preferences that may be difficult to model. In this paper we show that, in general, the decision problem version of top-k planning is PSPACE-complete, as is the decision problem version of ordinary classical planning. This does not hold for polynomially bounded plans for which the decision problem turns out to be PP-hard, while the ordinary case is NP-hard. We present a novel approach to top-k planning, called SYM-K, which is based on symbolic search, and prove that SYM-K is sound and complete. Our empirical analysis shows that SYM-K exceeds the current state of the art for both small and large k.
Thorsten Engesser und Tim Miller.
Implicit Coordination Using FOND Planning.
In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20). 2020.
To appear.
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Epistemic planning can be used to achieve implicit coordination in cooperative multi-agent settings where knowledge and capabilities are distributed between the agents. In these scenarios, agents plan and act on their own without having to agree on a common plan or protocol beforehand. However, epistemic planning is undecidable in general. In this paper, we show how implicit coordination can be achieved in a simpler, propositional setting by using nondeterminism as a means to allow the agents to take the other agents' perspectives. We identify a decidable fragment of epistemic planning that allows for arbitrary initial state uncertainty and non-determinism, but where actions can never increase the uncertainty of the agents. We show that in this fragment, planning for implicit coordination can be reduced to a version of fully observable nondeterministic (FOND) planning and that it thus has the same computational complexity as FOND planning. We provide a small case study, modeling the problem of multi-agent path finding with destination uncertainty in FOND, to show that our approach can be successfully applied in practice.
Felix Lindner, Barbara Kuhnert, Laura Wächter und Katrin Möllney.
Perception of Creative Responses to Moral Dilemmas by a Conversational Robot.
In Proc. ICSR 2019. 2019.
(PDF)
Felix Lindner und Katrin Möllney.
Extracting Reasons for Moral Judgments under Various Ethical Principles.
In Proceedings of KI 2019. 2019.
(PDF)
Daniel Reifsteck, Thorsten Engesser, Robert Mattmüller und Bernhard Nebel.
Epistemic Multi-agent Planning Using Monte-Carlo Tree Search.
In KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, S. 277-289. 2019.
Sumitra Corraya, Florian Geißer, David Speck und Robert Mattmüller.
An Empirical Study of the Usefulness of State-Dependent Action Costs in Planning.
In Proceedings of the 42th German Conference on Artificial Intelligence (KI 2019), S. 123-130. 2019.
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The vast majority of work in planning to date has focused on state-independent action costs. However, if a planning task features state-dependent costs, using a cost model with state-independent costs means either introducing a modeling error, or potentially sacrificing compactness of the model. In this paper, we investigate the conflicting priorities of modeling accuracy and compactness empirically, with a particular focus on the extent of the negative impact of reduced modeling accuracy on (a) the quality of the resulting plans, and (b) the search guidance provided by heuristics that are fed with inaccurate cost models. Our empirical results show that the plan suboptimality introduced by ignoring state-dependent costs can range, depending on the domain, from inexistent to several orders of magnitude. Furthermore, our results show that the impact on heuristic guidance additionally depends strongly on the heuristic that is used, the specifics of how exactly the costs are represented, and whether one is interested in heuristic accuracy, node expansions, or overall runtime savings.
Tim Schulte.
Trial-based Heuristic Tree-search for Distributed Multi-Agent Planning.
In Proceedings of the Twelfth Annual Symposium on Combinatorial Search (SoCS 2019) (SoCS 2019). 2019.
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We present a novel search scheme for privacy-preserving multi-agent planning, inspired by UCT search. We compare the presented approach to classical multi-agent forward search and evaluate it based on benchmarks from the CoDMAP competition.
Bernhard Nebel, Thomas Bolander, Thorsten Engesser, Robert Mattmüller und .
Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract).
In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2019), S. 6372-6374. 2019.
Benedict Wright.
Workflow Generation with Planning.
FreiDok plus 2019.
Dissertation Thesis.
(PDF)
Florian Geißer, David Speck und Thomas Keller.
An Analysis of the Probabilistic Track of the IPC 2018.
In Proceedings of the ICAPS-2019 Workshop on the International Planning Competition (WIPC 2019), S. 27-35. 2019.
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The International Planning Competition 2018 consisted of several tracks on classical, temporal and probabilistic planning. In this paper, we focus on the discrete MDP track of the probabilistic portion of the competition.

We discuss the changes to the input language RDDL, which give rise to new challenges for planning systems, and analyze each of the eight competition domains separately and highlight unique properties. We demonstrate flaws of the used evaluation criterion, the IPC score, and discuss the need for optimal upper bounds. An evaluation of the three top-performers, including their post-competition versions, and a brief analysis of their performance highlights the strengths and weaknesses of the individual approaches.
Bernhard Nebel.
Some Thoughts on Forward Induction in Multi-Agent-Path Finding Under Destination Uncertainty.
In Description Logic, Theory Combination, and All That - Essays Dedicated to Franz Baader on the Occasion of His 60th Birthday. Springer-Verlag, Berlin, Heidelberg, New York 2019.
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While the notion of implicit coordination helps to design frameworks in which agents can cooperatively act with only minimal communication, it so far lacks exploiting observations made while executing a plan. In this note, we have a look at what can be done in order to overcome this shortcoming, at least in a specialized setting.
Thorsten Engesser und Tim Miller.
Planning for Implicit Coordination using FOND.
In Proceedings of the Workshop on Knowledge Engineering for Planning and Scheduling (KEPS19). 2019.
Superseded by the AAAI-20 paper by the same authors.
(Abstract einblenden) (Abstract ausblenden)

Epistemic Planning can be used to achieve implicit coordination in cooperative multi-agent settings where knowledge and capabilities are distributed between the agents. In these scenarios, agents plan and act on their own without having to agree on a common plan or protocol beforehand. However, epistemic planning is undecidable in general. In this paper, we identify a decidable fragment of epistemic planning that allows for arbitrary initial state uncertainty and nondeterminism, but where actions can never increase the uncertainty of the agents. We show that in this fragment, planning with and without implicit coordination can be reduced to fully observable nondeterministic (FOND) planning and that it shares the same omputational complexity. We also provide a small case study, modeling the problem of multi-agent path finding with destination uncertainty in FOND, to show that our compilation approach can be successfully applied in practice.
David Speck, Florian Geißer, Robert Mattmüller und Álvaro Torralba.
Symbolic Planning with Axioms.
In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019), S. 464-572. 2019.
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Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rarely support axioms, especially in cost-optimal planning. Symbolic search is a popular and competitive optimal planning technique based on the manipulation of sets of states. In this work, we extend symbolic search algorithms to support axioms natively. We analyze different ways of encoding derived variables and axiom rules to evaluate them in a symbolic representation. We prove that all encodings are sound and complete, and empirically show that the presented approach outperforms the previous state of the art in cost-optimal classical planning with axioms.
Thomas Bolander, Thorsten Engesser, Andreas Herzig, Robert Mattmüller und Bernhard Nebel.
The Dynamic Logic of Policies and Contingent Planning.
In Logics in Artificial Intelligence - 16th European Conference (JELIA-2019), S. 659-674. 2019.
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In classical deterministic planning, solutions to planning tasks are simply sequences of actions, but that is not sufficient for contingent plans in non-deterministic environments. Contingent plans are often expressed through policies that map states to actions. An alternative is to specify contingent plans as programs, e.g. in the syntax of Propositional Dynamic Logic (PDL). PDL is a logic for reasoning about programs with sequential composition, test and non-deterministic choice. However, as we show in the paper, none of the existing PDL modalities directly captures the notion of a solution to a planning task under non-determinism. We add a new modality to star-free PDL correctly capturing this notion. We prove the appropriateness of the new modality by showing how to translate back and forth between policies and PDL programs under the new modality. More precisely, we show how a policy solution to a planning task gives rise to a program solution expressed via the new modality, and vice versa. We also provide an axiomatisation of our PDL extension through reduction axioms into standard star-free PDL.
Hanna Stellmach und Felix Lindner.
Perception of an Uncertain Ethical Reasoning Robot.
Journal of Interactive Media 18(1). 2019.
Daniel Kuhner, Lukas D.J. Fiederer, Johannes Aldinger, Felix Burget, Martin Völker, Robin T. Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball und Wolfram Burgard.
A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain–computer interfacing.
Robotics and Autonomous Systems 116, S. 98-113. 2019.
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As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of robotic tasks and environments. Traditional control modalities such as touch, speech or gesture are not necessarily suited for all users. While some users can make the effort to familiarize themselves with a robotic system, users with motor disabilities may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: a brain–computer interface (BCI) that uses non-invasive neuronal signal recording and co-adaptive deep learning, high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real-world scenarios, considering fetch-and-carry tasks, close human–robot interactions and in presence of unexpected changes. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level task planning based on referring expressions and an autonomous robotic system, interesting new perspectives open up for non-invasive BCI-based human–robot interactions.
Bernhard Nebel, Thomas Bolander, Thorsten Engesser und Robert Mattmüller.
Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity.
Journal of Artificial Intelligence Research 64, S. 497-527. 2019.
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In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.