Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), Stanford University, Palo Alto, California, USA, March 25-27, 2019.
Martin, A.; Hinkelmann, K.; Gerber, A.; Lenat, D.; van Harmelen, F.; and Clark, P.,
editors.
Volume 2350 CEUR-WS.org, 2019.
Paper
link
bibtex
@book{martin_proceedings_2019,
title = {Proceedings of the {AAAI} 2019 {Spring} {Symposium} on {Combining} {Machine} {Learning} with {Knowledge} {Engineering} ({AAAI}-{MAKE} 2019), {Stanford} {University}, {Palo} {Alto}, {California}, {USA}, {March} 25-27, 2019},
volume = {2350},
copyright = {All rights reserved},
url = {https://ceur-ws.org/Vol-2350},
publisher = {CEUR-WS.org},
editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and van Harmelen, Frank and Clark, Peter},
year = {2019},
}
The Conversational AI Life-Cycle.
Martin, A.
. April 2019.
Paper
paper
doi
link
bibtex
@article{martin_conversational_2019,
title = {The {Conversational} {AI} {Life}-{Cycle}},
copyright = {All rights reserved},
url = {https://zenodo.org/record/7991800},
doi = {10.5281/ZENODO.7991800},
urldate = {2023-05-31},
author = {Martin, Andreas},
month = apr,
year = {2019},
url_paper={https://api.zotero.org/users/1325684/publications/items/7MP5ES86/file/view}
}
Towards An Assistive and Pattern Learning-driven Process Modeling Approach.
Laurenzi, E.; Hinkelmann, K.; Jüngling, S.; Montecchiari, D.; Pande, C.; and Martin, A.
In
Martin, A.; Hinkelmann, K.; Gerber, A.; Lenat, D.; Harmelen, F. v.; and Clark, P., editor(s),
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), volume 2350, pages 6, Palo Alto, California, USA, 2019. CEUR-WS.org
ISSN: 16130073
Paper
paper
link
bibtex
abstract
@inproceedings{Laurenzi2019,
address = {Palo Alto, California, USA},
title = {Towards {An} {Assistive} and {Pattern} {Learning}-driven {Process} {Modeling} {Approach}},
volume = {2350},
copyright = {All rights reserved},
url = {http://ceur-ws.org/Vol-2350},
abstract = {The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.},
booktitle = {Proceedings of the {AAAI} 2019 {Spring} {Symposium} on {Combining} {Machine} {Learning} with {Knowledge} {Engineering} ({AAAI}-{MAKE} 2019)},
publisher = {CEUR-WS.org},
author = {Laurenzi, Emanuele and Hinkelmann, Knut and Jüngling, Stephan and Montecchiari, Devid and Pande, Charuta and Martin, Andreas},
editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter},
year = {2019},
note = {ISSN: 16130073},
pages = {6},
url_paper={https://api.zotero.org/users/1325684/publications/items/YUMU4DZC/file/view}
}
The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
Reports of the AAAI 2019 Spring Symposium Series.
Baldini, I.; Barrett, C.; Chella, A.; Cinelli, C.; Gamez, D.; Gilpin, L.; Hinkelmann, K.; Holmes, D.; Kido, T.; Kocaoglu, M.; Lawless, W.; Lomuscio, A.; Macbeth, J.; Martin, A.; Mittu, R.; Patterson, E.; Sofge, D.; Tadepalli, P.; Takadama, K.; and Wilson, S.
AI Magazine, 40(3): 59–66. September 2019.
Paper
doi
link
bibtex
abstract
@article{Baldini2019,
title = {Reports of the {AAAI} 2019 {Spring} {Symposium} {Series}},
volume = {40},
copyright = {All rights reserved},
issn = {2371-9621},
url = {https://aaai.org/ojs/index.php/aimagazine/article/view/5181},
doi = {10.1609/aimag.v40i3.5181},
abstract = {The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.},
number = {3},
journal = {AI Magazine},
author = {Baldini, Ioana and Barrett, Clark and Chella, Antonio and Cinelli, Carlos and Gamez, David and Gilpin, Leilani and Hinkelmann, Knut and Holmes, Dylan and Kido, Takashi and Kocaoglu, Murat and Lawless, William and Lomuscio, Alessio and Macbeth, Jamie and Martin, Andreas and Mittu, Ranjeev and Patterson, Evan and Sofge, Donald and Tadepalli, Prasad and Takadama, Keiki and Wilson, Shomir},
month = sep,
year = {2019},
pages = {59--66},
}
The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.
Preface: Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019).
Martin, A.; Hinkelmann, K.; Gerber, A.; Lenat, D.; van Harmelen, F.; and Clark, P.
In
Martin, A.; Hinkelmann, K.; Gerber, A.; Lenat, D.; Harmelen, F. v.; and Clark, P., editor(s),
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), pages 1, Palo Alto, California, USA, 2019. CEUR-WS.org
Paper
paper
link
bibtex
@inproceedings{Martin2019,
address = {Palo Alto, California, USA},
title = {Preface: {Combining} {Machine} {Learning} with {Knowledge} {Engineering} ({AAAI}-{MAKE} 2019)},
copyright = {All rights reserved},
url = {http://ceur-ws.org/Vol-2350},
booktitle = {Proceedings of the {AAAI} 2019 {Spring} {Symposium} on {Combining} {Machine} {Learning} with {Knowledge} {Engineering} ({AAAI}-{MAKE} 2019)},
publisher = {CEUR-WS.org},
author = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and van Harmelen, Frank and Clark, Peter},
editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter},
year = {2019},
pages = {1},
url_paper={https://api.zotero.org/users/1325684/publications/items/IIBUSQI6/file/view}
}
Learning and Engineering Similarity Functions for Business Recommenders.
Witschel, H. H. F.; and Martin, A.
In
Martin, A.; Hinkelmann, K.; Gerber, A.; Lenat, D.; Harmelen, F. v.; and Clark, P., editor(s),
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), volume 2350, pages 6, Palo Alto, California, USA, 2019. CEUR-WS.org
ISSN: 16130073
Paper
paper
link
bibtex
abstract
1 download
@inproceedings{Witschel2019,
address = {Palo Alto, California, USA},
title = {Learning and {Engineering} {Similarity} {Functions} for {Business} {Recommenders}},
volume = {2350},
copyright = {All rights reserved},
url = {http://ceur-ws.org/Vol-2350},
abstract = {We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedbacks. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.},
booktitle = {Proceedings of the {AAAI} 2019 {Spring} {Symposium} on {Combining} {Machine} {Learning} with {Knowledge} {Engineering} ({AAAI}-{MAKE} 2019)},
publisher = {CEUR-WS.org},
author = {Witschel, H.F. Hans Friedrich and Martin, Andreas},
editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter},
year = {2019},
note = {ISSN: 16130073},
pages = {6},
url_paper={https://api.zotero.org/users/1325684/publications/items/6ZB4Z99F/file/view}
}
We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedbacks. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.