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Daniel Höller, Gregor Behnke, Pascal Bercher and Susanne Biundo.
The PANDA Framework for Hierarchical Planning.
KI - Künstliche Intelligenz. 2021.
(Online)
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David Speck and Michael Katz.
Symbolic Search for Oversubscription Planning.
In
Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021).
2021.
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The objective of optimal oversubscription planning is to find a plan that
yields an end state with a maximum utility while keeping plan cost under a
certain bound. In practice, the situation occurs whenever a large number of
possible, often competing goals of varying value exist, or the resources are
not sufficient to achieve all goals. In this paper, we investigate the use
of symbolic search for optimal oversubscription planning. Specifically, we
show how to apply symbolic forward search to oversubscription planning tasks
and prove that our approach is sound, complete and optimal. An empirical
analysis shows that our symbolic approach favorably competes with explicit
state-space heuristic search, the current state of the art for
oversubscription planning.
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Gregor Behnke and David Speck.
Symbolic Search for Optimal Total-Order HTN Planning.
In
Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021).
2021.
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David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller and Marius Lindauer.
Learning Heuristic Selection with Dynamic Algorithm Configuration.
In
Proceedings of the Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL 2020), p. 61–69.
2020.
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A key challenge in satisficing planning is to use multiple heuristics within one
heuristic search. An aggregation of multiple heuristic estimates, for example by
taking the maximum, has the disadvantage that bad estimates of a single heuristic
can negatively affect the whole search. Since the performance
of a heuristic varies from instance to instance, approaches
such as algorithm selection can be successfully applied. In
addition, alternating between multiple heuristics during the
search makes it possible to use all heuristics equally and improve performance.
However, all these approaches ignore the internal search dynamics of a planning
system, which can help to select the most helpful heuristics for the current expansion
step. We show that dynamic algorithm configuration can be used for dynamic heuristic
selection which takes into account the internal search dynamics of a planning system.
Furthermore, we prove that this approach generalizes over existing approaches and that
it can exponentially improve the performance of the heuristic search. To learn dynamic
heuristic selection, we propose an approach based on reinforcement learning and show
empirically that domain-wise learned policies, which take the internal search dynamics
of a planning system into account, can exceed existing approaches in terms of coverage.
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Dominik Drexler, David Speck and Robert Mattmüller.
Subset-Saturated Transition Cost Partitioning for Optimal Classical Planning.
In
Proceedings of the 12th Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP 2020), p. 23–31.
2020.
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Cost partitioning admissibly combines the information from
multiple heuristics for state-space search. We use a greedy
method called saturated cost partitioning that considers the
heuristics in sequence and assigns the minimal fraction of the
remaining costs that it needs to preserve the heuristic estimates.
In this work, we address the problem of using more
expressive transition cost functions with saturated cost partitioning
to obtain stronger heuristics. Our contribution is
subset-saturated transition cost partitioning that combines the
concepts of using transition cost functions and prioritizing
states that look more important during the search. Our empirical
evaluation shows that this approach still causes too much
computational overhead but leads to more informed heuristics.
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Thorsten Engesser, Robert Mattmüller, Bernhard Nebel and Felicitas Ritter.
Token-based Execution Semantics for Multi-Agent Epistemic Planning.
In
Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR-2020), pp. 351-360.
2020.
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Thorsten Engesser, Robert Mattmüller, Bernhard Nebel and Felicitas Ritter.
Token-based Execution Semantics for Multi-Agent Epistemic Planning.
In
Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR-2020), pp. 351-360.
2020.
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(Online; PDF)
Epistemic planning has been employed as a means to achieve implicit coordination in cooperative multi-agent systems where world knowledge is distributed between the agents, and agents plan and act individually. However, recent work has shown that even if all agents act with respect to plans that they consider optimal from their own subjective perspective, infinite executions can occur. In this paper, we analyze the idea of using a single token that can be passed around between the agents and which is used as a prerequisite for acting. We show that introducing such a token to any planning task will prevent the existence of infinite executions. We furthermore analyze the conditions under which solutions to a planning task are preserved under our tokenization.
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Matthias Kraus, Marvin Schiller, Gregor Behnke, Pascal Bercher, Michael Dorna, Michael Dambier, Birte Glimm, Susanne Biundo and Wolfgang Minker.
``Was that successful?'' On Integrating Proactive Meta-Dialogue in a DIY-Assistant using Multimodal Cues.
In
Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI 2020).
2020.
(PDF)
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Lukas Berger, Bernhard Nebel and Marco Ragni.
A Heuristic Agent in Multi-Agent Path Finding Under Destination Uncertainty.
In
KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI,, pp. 259-266.
2020.
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Humans are capable of recognizing intentions by solely observing another agent’s actions. Hence, in a cooperative planning task, i.e., where all agents aim for all other agents to reach their respective goals, to some extend communication or a central planning instance are not necessary. In epistemic planning a recent research line investigates multi-agent planning problems (MAPF) with goal uncertainty. In this paper, we propose and analyze a round-based variation of this problem, where each agent moves or waits in each round. We show that simple heuristics from cognition can outperform in some cases an adapted formal approach on computation time and solve some new instances in some cases. Implications are discussed.
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Daniel Höller, Pascal Bercher, Gregor Behnke and Susanne Biundo.
HTN Plan Repair via Model Transformation.
In
Proceedings of the 42+1st Annual German Conference on Artificial Intelligence (KI 2020).
2020.
(PDF)
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Daniel Höller, Pascal Bercher and 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 and Bernhard Nebel.
Evaluation of the Moral Permissibility of Action
Plans.
Artificial Intelligence 287. 2020.
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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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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), pp. 212-216.
2020.
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(Online; 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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David Speck, Florian Geißer and 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), pp. 263-271.
2020.
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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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Roman Bartak, Simona Ondrckova, Adrien Maillard, Gregor Behnke and Pascal Bercher.
A Novel Parsing-based Approach for Verification of Hierarchical Plans.
In
Proceedings of the 32nd International Conference on Tools with Artificial Intelligence (ICTAI 2020).
2020.
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Florian Geißer, David Speck and 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), pp. 38-47.
2020.
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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.
