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\n\n \n \n \n \n \n \n Towards a Companion System Incorporating Human Planning Behavior – A Qualitative Analysis of Human Strategies.\n \n \n \n \n\n\n \n Benedikt Leichtmann; Pascal Bercher; Daniel Höller; Gregor Behnke; Susanne Biundo; Verena Nitsch; and Martin Baumann.\n\n\n \n\n\n\n In
Proceedings of the 3rd Transdisciplinary Conference on Support Technologies (TCST 2018), pages 89–98, 2018. \n
This paper won the TCST 2018 Best Paper Award\n\n
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@InProceedings{Leichtmann2018HumanPlanningBehavior,\n author = {Benedikt Leichtmann and Pascal Bercher and Daniel H{\\"o}ller and Gregor Behnke and Susanne Biundo and Verena Nitsch and Martin Baumann},\n title = {Towards a Companion System Incorporating Human Planning Behavior -- A Qualitative Analysis of Human Strategies},\n year = {2018},\n pages = {89--98},\n booktitle = {Proceedings of the 3rd Transdisciplinary Conference on Support Technologies (TCST 2018)},\n note = {<b><i>This paper won the TCST 2018 Best Paper Award</i></b>},\n abstract = {User-friendly Companion Systems require Artificial Intelligence planning to take into account human planning behavior. We conducted a qualitative exploratory study of human planning in a knowledge rich, real-world scenario. Participants were tasked with setting up a home theater. The effect of strategy knowledge on problem solving was investigated by comparing the performance of two groups: one group (n = 23) with strategy instructions for problem solving and a control group without such instructions (n = 16). We inductively identify behavioral patterns for human strategy use through Markov matrices. Based on the results, we derive implications for the design of planning-based assistance systems.},\n url_Paper = {https://bercher.net/publications/2018/Leichtmann2018HumanPlanningBehavior.pdf},\n url_Slides = {https://bercher.net/publications/2018/Leichtmann2018HumanPlanningBehaviorSlides.pdf}\n}\n\n\n
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\n User-friendly Companion Systems require Artificial Intelligence planning to take into account human planning behavior. We conducted a qualitative exploratory study of human planning in a knowledge rich, real-world scenario. Participants were tasked with setting up a home theater. The effect of strategy knowledge on problem solving was investigated by comparing the performance of two groups: one group (n = 23) with strategy instructions for problem solving and a control group without such instructions (n = 16). We inductively identify behavioral patterns for human strategy use through Markov matrices. Based on the results, we derive implications for the design of planning-based assistance systems.\n
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\n\n \n \n \n \n \n \n Plan and Goal Recognition as HTN Planning.\n \n \n \n \n\n\n \n Daniel Höller; Gregor Behnke; Pascal Bercher; and Susanne Biundo.\n\n\n \n\n\n\n In
Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018), pages 466–473, 2018. IEEE\n
This paper won the ICTAI 2018 CV Ramamoorthy Best Paper Award\n\n
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@InProceedings{Hoeller2018PlanRecognition,\n author = {Daniel H{\\"o}ller and Gregor Behnke and Pascal Bercher and Susanne Biundo},\n title = {Plan and Goal Recognition as {HTN} Planning},\n year = {2018},\n publisher = {IEEE},\n pages = {466--473},\n note = {<b><i>This paper won the ICTAI 2018 CV Ramamoorthy Best Paper Award</i></b>},\n booktitle = {Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018)},\n abstract = {Plan- and Goal Recognition (PGR) is the task of inferring the goals and plans of an agent based on its actions. Traditional approaches in PGR are based on a plan library including pairs of plans and corresponding goals. In recent years, the field successfully exploited the performance of planning systems for PGR. The main benefits are the presence of efficient solvers and well-established, compact formalisms for behavior representation. However, the expressivity of the STRIPS planning models used so far is limited, and models in PGR are often structured in a hierarchical way. We present the approach Plan and Goal Recognition as HTN Planning that combines the expressive but still compact grammar-like HTN representation with the advantage of using unmodified, off-the-shelf planning systems for PGR. Our evaluation shows that -- using our approach -- current planning systems are able to handle large models with thousands of possible goals, that the approach results in high recognition rates, and that it works even when the environment is partially observable, i.e., if the observer might miss observations.},\n doi = {10.1109/ICTAI.2018.00078},\n url_Paper = {https://bercher.net/publications/2018/Hoeller2018bPlanRecognition.pdf}\n}\n\n
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\n Plan- and Goal Recognition (PGR) is the task of inferring the goals and plans of an agent based on its actions. Traditional approaches in PGR are based on a plan library including pairs of plans and corresponding goals. In recent years, the field successfully exploited the performance of planning systems for PGR. The main benefits are the presence of efficient solvers and well-established, compact formalisms for behavior representation. However, the expressivity of the STRIPS planning models used so far is limited, and models in PGR are often structured in a hierarchical way. We present the approach Plan and Goal Recognition as HTN Planning that combines the expressive but still compact grammar-like HTN representation with the advantage of using unmodified, off-the-shelf planning systems for PGR. Our evaluation shows that – using our approach – current planning systems are able to handle large models with thousands of possible goals, that the approach results in high recognition rates, and that it works even when the environment is partially observable, i.e., if the observer might miss observations.\n
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\n\n \n \n \n \n \n \n A Generic Method to Guide HTN Progression Search with Classical Heuristics.\n \n \n \n \n\n\n \n Daniel Höller; Pascal Bercher; Gregor Behnke; and Susanne Biundo.\n\n\n \n\n\n\n In
Proceedings of the 28th International Conference on Automated Planning and Scheduling (ICAPS 2018), pages 114–122, 2018. AAAI Press\n
This paper won the ICAPS 2018 Best Student Paper Award\n\n
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@InProceedings{Hoeller2018ProgressionHeuristics,\n author = {Daniel H{\\"{o}}ller and Pascal Bercher and Gregor Behnke and Susanne Biundo},\n title = {A Generic Method to Guide {HTN} Progression Search with Classical Heuristics},\n booktitle = {Proceedings of the 28th International Conference on Automated Planning and Scheduling ({ICAPS 2018})},\n note = {<b><i>This paper won the ICAPS 2018 Best Student Paper Award</i></b>},\n publisher = {{AAAI} Press},\n year = {2018},\n pages = {114--122},\n abstract = {HTN planning combines actions that cause state transition with grammar-like decomposition of compound tasks that additionally restricts the structure of solutions. There are mainly two strategies to solve such planning problems: decomposition-based search in a plan space and progression-based search in a state space. Existing progression-based systems do either not rely on heuristics (e.g. SHOP2) or calculate their heuristics based on extended or modified models (e.g. GoDeL). Current heuristic planners for standard HTN models (e.g. PANDA) use decomposition-based search. Such systems represent search nodes more compactly due to maintaining a partial order between tasks, but they have no current state at hand during search. This makes the design of heuristics difficult. In this paper we present a progression-based heuristic HTN planning system: We (1) provide an improved progression algorithm, prove its correctness, and empirically show its efficiency gain; and (2) present an approach that allows to use arbitrary classical (non-hierarchical) heuristics in HTN planning. Our empirical evaluation shows that the resulting system outperforms the state-of-the-art in HTN planning.},\n doi = {10.1609/icaps.v28i1.13900},\n url_Paper = {https://bercher.net/publications/2018/Hoeller2018ProgressionHeuristics.pdf},\n url_video_of_presentation = {https://www.youtube.com/watch?v=KOZuIkJaC0w}\n} \n\n
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\n HTN planning combines actions that cause state transition with grammar-like decomposition of compound tasks that additionally restricts the structure of solutions. There are mainly two strategies to solve such planning problems: decomposition-based search in a plan space and progression-based search in a state space. Existing progression-based systems do either not rely on heuristics (e.g. SHOP2) or calculate their heuristics based on extended or modified models (e.g. GoDeL). Current heuristic planners for standard HTN models (e.g. PANDA) use decomposition-based search. Such systems represent search nodes more compactly due to maintaining a partial order between tasks, but they have no current state at hand during search. This makes the design of heuristics difficult. In this paper we present a progression-based heuristic HTN planning system: We (1) provide an improved progression algorithm, prove its correctness, and empirically show its efficiency gain; and (2) present an approach that allows to use arbitrary classical (non-hierarchical) heuristics in HTN planning. Our empirical evaluation shows that the resulting system outperforms the state-of-the-art in HTN planning.\n
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