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Helge Spieker

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6 papers

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JAIR Journal 2024 Journal Article

Query-driven Qualitative Constraint Acquisition

Mohamed-Bachir Belaid
Nassim Belmecheri
Arnaud Gotlieb
Nadjib Lazaar
Helge Spieker

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.

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AAAI Conference 2022 Conference Paper

GEQCA: Generic Qualitative Constraint Acquisition

Mohamed-Bachir Belaid
Nassim Belmecheri
Arnaud Gotlieb
Nadjib Lazaar
Helge Spieker

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. On the one hand, the precise modelling of these constraints, which are formulated in various relation algebras, entails a number of possible logical combinations and requires expertise in constraint-based modelling. On the other hand, active constraint acquisition (CA) has been used successfully to support non-experienced users in learning conjunctive constraint networks through the generation of a sequence of queries. In this paper, we propose GEQCA, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative constraints via the concept of qualitative queries. GEQCA combines qualitative queries with time-bounded path consistency (PC) and background knowledge propagation to acquire the qualitative constraints of any scheduling or packing problem. We prove soundness, completeness and termination of GEQCA by exploiting the jointly exhaustive and pairwise disjoint property of qualitative calculus and we give an experimental evaluation that shows (i) the efficiency of our approach in learning temporal constraints and, (ii) the use of GEQCA on real scheduling instances.

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AAAI Conference 2021 Short Paper

Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)

Pierre Bernabé
Helge Spieker
Bruno Legeard
Arnaud Gotlieb

Maritime surveillance is essential to avoid illegal activities and for environmental protection. However, the unlabeled, noisy, irregular time-series data and the large area to be covered make it challenging to detect illegal activities. Existing solutions focus only on trajectory reconstruction and probabilistic models that do ignore the context, such as the neighboring vessels. We propose a novel representation learning method that considers both temporal and spatial contexts learned in a self-supervised manner, using a selection of pretext tasks that do not require to be labeled manually. The underlying model encodes the representation of maritime vessel data compactly and effectively. This generic encoder can then be used as input for more complex tasks lacking labeled data.

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AAAI Conference 2019 Conference Paper

Rotational Diversity in Multi-Cycle Assignment Problems

Helge Spieker
Arnaud Gotlieb
Morten Mossige

In multi-cycle assignment problems with rotational diversity, a set of tasks has to be repeatedly assigned to a set of agents. Over multiple cycles, the goal is to achieve a high diversity of assignments from tasks to agents. At the same time, the assignments’ profit has to be maximized in each cycle. Due to changing availability of tasks and agents, planning ahead is infeasible and each cycle is an independent assignment problem but influenced by previous choices. We approach the multi-cycle assignment problem as a two-part problem: Profit maximization and rotation are combined into one objective value, and then solved as a General Assignment Problem. Rotational diversity is maintained with a single execution of the costly assignment model. Our simple, yet effective method is applicable to different domains and applications. Experiments show the applicability on a multi-cycle variant of the multiple knapsack problem and a real-world case study on the test case selection and assignment problem, an example from the software engineering domain, where test cases have to be distributed over compatible test machines.

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AAAI Conference 2019 Short Paper

Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search

Helge Spieker

This paper proposes a framework for solving constraint problems with reinforcement learning (RL) and sequence-tosequence recurrent neural networks. We approach constraint solving as a declarative machine learning problem, where for a variable-length input sequence a variable-length output sequence has to be predicted. Using randomly generated instances and the number of constraint violations as a reward function, a problem-specific RL agent is trained to solve the problem. The predicted solution candidate of the RL agent is verified and repaired by CBLS to ensure solutions, that satisfy the constraint model. We introduce the framework and its components and discuss early results and future applications.

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AAAI Conference 2018 Short Paper

Different Cycle, Different Assignment: Diversity in Assignment Problems With Multiple Cycles

Helge Spieker
Arnaud Gotlieb
Morten Mossige

We present approaches to handle diverse assignments in multi-cycle assignment problems. The goal is to assign a task to different agents in each cycle, such that all possible combinations are made over time. Our method combines the original proﬁt value, that is to be optimized by the assignment problem with an additional assignment preference. By merging both, we steer the optimization towards diverse assignments without large trade-offs in the original proﬁts.

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