Joerg Hoffmann


2020

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MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents
Arne Köhn | Julia Wichlacz | Christine Schäfer | Álvaro Torralba | Joerg Hoffmann | Alexander Koller
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.

2016

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From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation
Maximilian Schwenger | Álvaro Torralba | Joerg Hoffmann | David M. Howcroft | Vera Demberg
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an example, we show that we can effectively detect partial solutions (edges) which cannot ultimately be part of a complete sentence because of their syntactic category. Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem. We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation). Our experiments show that this can filter out large fractions of infeasible edges in, and thus benefit the performance of, complex realization processes.