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Daniel Höller, Pascal Bercher und Gregor Behnke.
Delete- and Ordering-Relaxation Heuristics for HTN Planning.
In
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2020).
2020.
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Felix Lindner, Robert Mattmüller und Bernhard Nebel.
Evaluation of the Moral Permissibility of Action
Plans.
Artificial Intelligence. 2020.
To appear.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
Research in classical planning so far has been mainly concerned with
generating a satisficing or an optimal plan. However, if such
systems are used to make decisions that are relevant to humans, one
should also consider the ethical consequences generated plans can have.
Traditionally, ethical principles are formulated
in an action-based manner, allowing to judge the execution
of one action. We show how such a judgment can be generalized to
plans. Further, we study the computational complexity of making ethical judgment
about plans.
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David Speck, Florian Geißer und Robert Mattmüller.
When Perfect is not Good Enough: On the Search Behaviour of Symbolic Heuristic Search.
In
Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020), S. 263-271.
2020.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
Symbolic search has proven to be a competitive approach to cost-optimal
planning, as it compactly represents sets of states by symbolic data
structures.
While heuristics for symbolic search exist, symbolic bidirectional
blind search empirically outperforms its heuristic counterpart and is
therefore the dominant search strategy.
This prompts the question of why heuristics do not seem to pay off in symbolic
search. As a first step in answering this question, we investigate the search
behaviour of symbolic heuristic search by means of BDDA*.
Previous work identified the partitioning of state sets
according to their heuristic values as the main bottleneck.
We theoretically and empirically evaluate the search behaviour of BDDA*
and reveal another fundamental problem: we prove that the use of a heuristic
does not always improve the search performance of BDDA*. In general, even
the perfect heuristic can exponentially deteriorate search performance.
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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.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
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.
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Florian Geißer, David Speck und Thomas Keller.
Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces.
In
Proceedings of the 13th Annual Symposium on Combinatorial Search (SoCS 2020), S. 38-47.
2020.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
MDPs with factored action spaces, i.e. where actions are described as assignments to a set of action variables, allow reasoning over action variables instead of action states, yet most algorithms only consider a grounded action representation. This includes algorithms that are instantiations of the trial-based heuristic tree search (THTS) framework, such as AO* or UCT.
To be able to reason over factored action spaces, we propose a generalisation of THTS where nodes that branch over all applicable actions are replaced with subtrees that consist of nodes that represent the decision for a single action variable. We show that many THTS algorithms retain their theoretical properties under the generalised framework, and show how to approximate any state-action heuristic to a heuristic for partial action assignments. This allows to guide a UCT variant that is able to create exponentially fewer nodes than the same algorithm that considers ground actions. An empirical evaluation on the benchmark set of the probabilistic track of the latest International Planning Competition validates the benefits of the approach.
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David Speck, Robert Mattmüller und Bernhard Nebel.
Symbolic Top-k Planning.
In
Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), S. 9967-9974.
2020.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
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.
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Daniel Höller, Pascal Bercher, Gregor Behnke und Susanne Biundo.
HTN Planning as Heuristic Progression Search.
Journal of Artificial Intelligence Research, S. 835-880. 2020.
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Gregor Behnke, Pascal Bercher, Matthias Kraus, Marvin Schiller, Kristof Mickeleit, Häge Timo, Michael Dorna, Michael Dambier, Wolfgang Minker, Birte Glimm und Susanne Biundo.
New Developments for Robert - Assisting Novice Users Even Better in DIY Projects.
In
Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020), S. 114-122.
2020.
(PDF)
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Daniel Höller, Gregor Behnke, Pascal Bercher, Susanne Biundo, Humbert Fiorino, Damien Pellier und Ron Alford.
HDDL - A Language to Describe Hierarchical Planning Problems.
In
Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), S. 6-17.
2020.
(PDF)
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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 2020).
2020.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
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.
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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.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
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.
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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)
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Felix Lindner und Katrin Möllney.
Extracting Reasons for Moral Judgments under Various Ethical Principles.
In
Proceedings of KI 2019.
2019.
(PDF)
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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.
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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.
(Abstract einblenden)
(Abstract ausblenden)
(PDF; Online)
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.
