LatRec: Recognizing Goals in Latent Space (Demo).
Amado, L.; Aires, J. P.; Pereira, R. F.; Magnaguagno, M. C.; Granada, R.; Licks, G. P.; and Meneguzzi, F.
In
Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS), 2019. AAAI Press
link
bibtex
abstract
@inproceedings{Amado2019,
author = {Leonardo Amado and Jo\~{a}o Paulo Aires and Ramon F. Pereira and Maur\'{i}cio C. Magnaguagno and Roger Granada and Gabriel Paludo Licks and Felipe Meneguzzi},
title = {{LatRec: Recognizing Goals in Latent Space (Demo)}},
booktitle = {Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS)},
year = {2019},
publisher = {AAAI Press},
abstract = {Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms.
These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. LatRec applies modern goal recognition algorithms directly to real-world data (images) by building planning domain knowledge using an unsupervised learning algorithm that generates domain theories from raw images. We demonstrate this approach in an online simulation of simple games, such as the n-puzzle game.}
}
Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. LatRec applies modern goal recognition algorithms directly to real-world data (images) by building planning domain knowledge using an unsupervised learning algorithm that generates domain theories from raw images. We demonstrate this approach in an online simulation of simple games, such as the n-puzzle game.
ConCon: A Contract Conflict Identifier.
Aires, J. P.; Granada, R.; and Meneguzzi, F.
In
Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems, 2019.
link
bibtex
abstract
@InProceedings{Aires2019a,
author = {Jo{\~{a}}o Paulo Aires and Roger Granada and Felipe Meneguzzi},
title = {ConCon: A Contract Conflict Identifier},
booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems},
year = {2019},
abstract = {Contracts are the main medium through which people and legal entities formalise their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still encoded in contracts written in natural language, necessitating substantial human revision effort to eliminate possible conflicting statements, especially for long and complex contracts. We demonstrate the ConCon (Contract Conflicts) tool, to automatically read natural language contracts and indicate potential conflicts among their clauses. Using our tool, legal professionals and the general public can benefit from a ranking of potential conflicts between the clauses in a contract, saving time and effort from legal experts in contract proof-reading.}
}
Contracts are the main medium through which people and legal entities formalise their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still encoded in contracts written in natural language, necessitating substantial human revision effort to eliminate possible conflicting statements, especially for long and complex contracts. We demonstrate the ConCon (Contract Conflicts) tool, to automatically read natural language contracts and indicate potential conflicts among their clauses. Using our tool, legal professionals and the general public can benefit from a ranking of potential conflicts between the clauses in a contract, saving time and effort from legal experts in contract proof-reading.
Classification of Contractual Conflicts via Learning of Semantic Representations.
Aires, J. P.; Granada, R.; Monteiro, J.; Barros, R. C.; and Meneguzzi, F.
In
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019, pages 1764–1766, 2019.
![Classification of Contractual Conflicts via Learning of Semantic Representations [link]](//bibbase.org/img/filetypes/link.svg "Paper")
Paper
link
bibtex
@inproceedings{DBLP:conf/atal/AiresGMBM19,
author = {Jo{\~{a}}o Paulo Aires and
Roger Granada and
Juarez Monteiro and
Rodrigo Coelho Barros and
Felipe Meneguzzi},
title = {Classification of Contractual Conflicts via Learning of Semantic Representations},
booktitle = {Proceedings of the 18th International Conference on Autonomous Agents
and MultiAgent Systems, {AAMAS} '19, Montreal, QC, Canada, May 13-17,
2019},
pages = {1764--1766},
year = {2019},
crossref = {DBLP:conf/atal/2019},
url = {http://dl.acm.org/citation.cfm?id=3331911},
timestamp = {Wed, 29 May 2019 16:36:58 +0200},
biburl = {https://dblp.org/rec/bib/conf/atal/AiresGMBM19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
ConCon: A Contract Conflict Identifier.
Aires, J. P.; Granada, R.; and Meneguzzi, F.
In
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019, pages 2327–2329, 2019.
Paper
link
bibtex
@inproceedings{DBLP:conf/atal/AiresGM19,
author = {Jo{\~{a}}o Paulo Aires and
Roger Granada and
Felipe Meneguzzi},
title = {ConCon: {A} Contract Conflict Identifier},
booktitle = {Proceedings of the 18th International Conference on Autonomous Agents
and MultiAgent Systems, {AAMAS} '19, Montreal, QC, Canada, May 13-17,
2019},
pages = {2327--2329},
year = {2019},
crossref = {DBLP:conf/atal/2019},
url = {http://dl.acm.org/citation.cfm?id=3332101},
timestamp = {Wed, 29 May 2019 16:36:58 +0200},
biburl = {https://dblp.org/rec/bib/conf/atal/AiresGM19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Automating News Summarization with Sentence Vectors Offset.
Steinert, M.; Granada, R.; Aires, J. P.; and Meneguzzi, F.
In
8th Brazilian Conference on Intelligent Systems, BRACIS 2019, Salvador, Brazil, October 15-18, 2019, pages 102–107, 2019.
Paper
doi
link
bibtex
@inproceedings{DBLP:conf/bracis/SteinertGAM19,
author = {Mauricio Steinert and
Roger Granada and
Jo{\~{a}}o Paulo Aires and
Felipe Meneguzzi},
title = {Automating News Summarization with Sentence Vectors Offset},
booktitle = {8th Brazilian Conference on Intelligent Systems, {BRACIS} 2019, Salvador,
Brazil, October 15-18, 2019},
pages = {102--107},
year = {2019},
crossref = {DBLP:conf/bracis/2019},
url = {https://doi.org/10.1109/BRACIS.2019.00027},
doi = {10.1109/BRACIS.2019.00027},
timestamp = {Thu, 09 Jan 2020 12:36:19 +0100},
biburl = {https://dblp.org/rec/bib/conf/bracis/SteinertGAM19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Classifying Norm Conflicts using Learned Semantic Representations.
Aires, J. P.; Granada, R.; Monteiro, J.; Barros, R. C.; and Meneguzzi, F.
CoRR, abs/1906.02121. 2019.
Paper
link
bibtex
@article{DBLP:journals/corr/abs-1906-02121,
author = {Jo{\~{a}}o Paulo Aires and
Roger Granada and
Juarez Monteiro and
Rodrigo C. Barros and
Felipe Meneguzzi},
title = {Classifying Norm Conflicts using Learned Semantic Representations},
journal = {CoRR},
volume = {abs/1906.02121},
year = {2019},
url = {http://arxiv.org/abs/1906.02121},
archivePrefix = {arXiv},
eprint = {1906.02121},
timestamp = {Thu, 13 Jun 2019 01:00:00 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1906-02121},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
![Classification of Contractual Conflicts via Learning of Semantic Representations [link]](http://bibbase.org/img/filetypes/link.svg "Paper")
Paper
link
bibtex