Publications

Minecraft is a videogame that offers many interesting challenges for AI systems. In this paper, we focus in construction scenarios where an agent must build a complex structure made of individual blocks. As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchical task network. We model a house-construction scenario in classical and HTN planning and compare the advantages and disadvantages of both kinds of models.

• whtn19     (0.14MB)
• https://www.semanticscholar.org/paper/Construction-Planning-Models-in-Minecraft-Wichlacz-Torralba/d2ffb1c4b815f1b245f248d436baf9a3c28cc148

@inproceedings{Wichlacz2019,
title = {Construction-Planning Models in Minecraft},
author = {Julia Wichlacz and {\'A}lvaro Torralba and J{\"o}rg Hoffmann},
url = {https://www.semanticscholar.org/paper/Construction-Planning-Models-in-Minecraft-Wichlacz-Torralba/d2ffb1c4b815f1b245f248d436baf9a3c28cc148},
year = {2019},
date = {2019},
booktitle = {Proceedings of the 2nd Workshop on Hierarchical Planning at ICAPS 2019},
pages = {1-5},
abstract = {Minecraft is a videogame that offers many interesting challenges for AI systems. In this paper, we focus in construction scenarios where an agent must build a complex structure made of individual blocks. As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchical task network. We model a house-construction scenario in classical and HTN planning and compare the advantages and disadvantages of both kinds of models.},
pubstate = {published},
type = {inproceedings}
}

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When generating technical instructions, it is often necessary to describe an object that does not exist yet. For example, an NLG system which explains how to build a house needs to generate sentences like “build a wall of height five to your left” and “now build a wall on the other side.” Generating (indefinite) referring expressions to objects that do not exist yet is fundamentally different from generating the usual definite referring expressions, because the new object must be distinguished from an infinite set of possible alternatives. We formalize this problem and present an algorithm for generating such expressions, in the context of generating building instructions within the Minecraft video game.

• https://www.aclweb.org/anthology/W19-8601
• https://doi.org/10.18653/v1/W19-8601

@inproceedings{Köhn2019,
title = {Talking about what is not there: Generating indefinite referring expressions in Minecraft},
author = {Arne K{\"o}hn and Alexander Koller},
url = {https://www.aclweb.org/anthology/W19-8601},
doi = {https://doi.org/10.18653/v1/W19-8601},
year = {2019},
date = {2019},
booktitle = {Proceedings of the 12th International Conference on Natural Language Generation},
pages = {1-10},
publisher = {Association for Computational Linguistics},
address = {Tokyo, Japan},
abstract = {When generating technical instructions, it is often necessary to describe an object that does not exist yet. For example, an NLG system which explains how to build a house needs to generate sentences like “build a wall of height five to your left” and “now build a wall on the other side.” Generating (indefinite) referring expressions to objects that do not exist yet is fundamentally different from generating the usual definite referring expressions, because the new object must be distinguished from an infinite set of possible alternatives. We formalize this problem and present an algorithm for generating such expressions, in the context of generating building instructions within the Minecraft video game.},
pubstate = {published},
type = {inproceedings}
}

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We present a model which predicts how individual users of a dialog system understand and produce utterances based on user groups. In contrast to previous work, these user groups are not specified beforehand, but learned in training. We evaluate on two referring expression (RE) generation tasks; our experiments show that our model can identify user groups and learn how to most effectively talk to them, and can dynamically assign unseen users to the correct groups as they interact with the system.

• W18-5018     (1.04MB)
• https://arxiv.org/abs/1806.05947

@inproceedings{Engonopoulos2018discovering,
title = {Discovering user groups for natural language generation},
author = {Nikos Engonopoulos and Christoph Teichmann and Alexander Koller},
url = {https://arxiv.org/abs/1806.05947},
year = {2018},
date = {2018},
booktitle = {Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue},
abstract = {We present a model which predicts how individual users of a dialog system understand and produce utterances based on user groups. In contrast to previous work, these user groups are not specified beforehand, but learned in training. We evaluate on two referring expression (RE) generation tasks; our experiments show that our model can identify user groups and learn how to most effectively talk to them, and can dynamically assign unseen users to the correct groups as they interact with the system.},
pubstate = {published},
type = {inproceedings}
}

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