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24. PuK-Workshop: Planung/Scheduling und Konfigurieren/Entwerfen: Verbal Plan Explanations for Hybrid Planning
[chapter]
2010
Multikonferenz Wirtschaftsinformatik 2010
Preserved Fulltext
By means of natural language dialogs in this work, we focus on the explanation of plans that are generated by a refinement-based planning system. ...
In other words, these systems do not need to understand the underlying semantics of the plans they execute and how these plans have been generated. ...
Community's 7 th Framework Programme (FP7/2007(FP7/ -2013 under grant agreement n° 216837 and from the Transregional Collaborative Research Centre SFB/TRR 62 -Companion-Technology for Cognitive Technical Systems ...
doi:10.17875/gup2010-1478
fatcat:57i43yzfm5aevnwletpyglruxa
Task-structures, knowledge acquisition and learning
1989
Machine Learning
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of generic tasks that can be used as building blocks for the construction of knowledge systems. ...
What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. ...
Explanation-Based Learning The task-oriented view in general and the GT approach in particular have significant potential to aid learning. ...
doi:10.1007/bf00130718
fatcat:l32z4dvx7vdc5dprqcfncbr7wm
Task-Structures, Knowledge Acquisition and Learning
[chapter]
1989
Knowledge Acquisition: Selected Research and Commentary
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of generic tasks that can be used as building blocks for the construction of knowledge systems. ...
What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. ...
Explanation-Based Learning The task-oriented view in general and the GT approach in particular have significant potential to aid learning. ...
doi:10.1007/978-1-4613-1531-5_8
fatcat:vhikdp6rpneghaflsfhqguzq4a
A Study on Multimodal and Interactive Explanations for Visual Question Answering
[article]
2020
arXiv
pre-print
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User explanation ratings are strongly correlated with human prediction accuracy and suggest the efficacy of these explanations in human-machine AI collaboration tasks. ...
We evaluate multimodal explanations in the setting of a Visual Question Answering (VQA) task, by asking users to predict the response accuracy of a VQA agent with and without explanations. ...
In the VQA task, the system provides a question and an image, and the task is to answer the question using the image correctly. ...
arXiv:2003.00431v1
fatcat:ycjgoi65mbdwpgfkthozrhjt6e
Meta-cases: Explaining case-based reasoning
[chapter]
1996
Lecture Notes in Computer Science
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We describe a task-methodknowledge (TMK) model of problem-solving and describe the representation of meta-cases in the TMK language. ...
A meta-case contains a trace of the processing in a problem-solving episode, and provides an explanation of the problem-solving decisions and a (partial) justi cation for the solution. ...
It has been funded in part by a grant from the Advanced Research Projects Agency (research contract #F33615-93-1-1338) and partly by internal seed grants from Georgia Tech's Educational Technology Institute ...
doi:10.1007/bfb0020608
fatcat:h6krphlwobcfhdwiqx77cmknn4
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
[article]
2020
arXiv
pre-print
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In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement ...
In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under ...
In this paper, we present our system that we devised to tackle Task C, Explanation (Generation), of the SemEval 2020 Task 4 -Commonsense Validation and Explanation (ComVE). ...
arXiv:2006.16403v1
fatcat:3brpcqjavzhzdpmfltffhaina4
Knowledge-intensive Language Understanding for Explainable AI
[article]
2021
arXiv
pre-print
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AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. ...
Besides the concerns for fairness, privacy, transparency, and explainability are key to developing trusts in AI systems. As stated in describing trustworthy AI "Trust comes through understanding. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. ...
arXiv:2108.01174v1
fatcat:ohhsrz53vbddjntfb6erknvc7u
Enhancing Human Understanding through Intelligent Explanations
[chapter]
2008
Communications in Computer and Information Science
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In this paper we argue for the need of intelligent explanations, identify the requirements of such explanations, propose a method to achieve generation of intelligent explanations, and report on a prototype ...
Ambient systems that explain their actions promote the user's understanding as they give the user more insight in the effects of their behavior on the environment. ...
So if the explanations are satisfying in the simple case, we are confident that the system is able to generate intelligent explanations in more complex versions of the task and other tasks as well. ...
doi:10.1007/978-3-540-85379-4_38
fatcat:zm37gtlt3rhpzjvdrsbyspy5ve
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
2020
Proceedings of the 25th International Conference on Intelligent User Interfaces
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2020 pre-print arXiv:2001.08298v1 fatcat:qdhogrsesbgv3pxrzmlaooqayy
We conducted two online experiments and one in-person think-aloud study to evaluate two currently common techniques for evaluating XAI systems: (1) using proxy, artificial tasks such as how well humans ...
Explainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions. ...
Proxy Task 3.1.1 Task Description. We designed the task around nutrition because it is generally accessible and plausibly useful in explainable AI applications for a general audience. ...
doi:10.1145/3377325.3377498
dblp:conf/iui/BucincaLGG20
fatcat:wqv4kvncy5dfngfo2peyfsxgs4
Strategic explanations for a diagnostic consultation system
1984
International Journal of Man-Machine Studies
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In all cases, the task in designing these systems is to represent knowledge and reasoning in a well-structured formalism that can be used to solve problems (perhaps in compiled form as in Swartout's system ...
in Intelligent Tutoring Systems. ...
doi:10.1016/s0020-7373(84)80003-6
fatcat:p7lbwtddavf2benlwubv2p3s6i
Generating Fact Checking Explanations
[article]
2020
arXiv
pre-print
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The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model. ...
This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. ...
A manual evaluation shows that the coverage and the overall quality of the explanation system is also improved in the multi-task set-up. ...
arXiv:2004.05773v1
fatcat:ubg5qw3gyvbkzblip4vqkj7sym
Explainable Automated Fact-Checking: A Survey
[article]
2020
arXiv
pre-print
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Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area. ...
In this survey, we focus on the explanation functionality -- that is fact-checking systems providing reasons for their predictions. ...
Furthermore, various task formulations exist for generating explanations for automated fact-checking systems, e.g., explanations as text summaries. ...
arXiv:2011.03870v1
fatcat:moykzxzsvracfjo6nvg7b4nxp4
Explanation Ontology: A Model of Explanations for User-Centered AI
[article]
2020
arXiv
pre-print
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Explanations have often added to an AI system in a non-principled, post-hoc manner. ...
We design an explanation ontology to model both the role of explanations, accounting for the system and user attributes in the process, and the range of different literature-derived explanation types. ...
Each of these AI tasks can generate system recommendations that requires explanations from the clinicians. ...
arXiv:2010.01479v1
fatcat:asks7ztjqzdwja52jywjpwqq64
Explanation-driven case-based reasoning
[chapter]
1994
Lecture Notes in Computer Science
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At the top level, for example, the corresponding explanation task could be to explain a patient's symptoms in terms of the physiological states that cause them. ...
This is followed by an outline of how case knowledge and general knowledge may be integrated in a unified representation system. ...
Pinar Ozturk is studying explanation-and case-driven context focusing in her Ph.D research. They both provided valuable comments to earlier drafts of the paper. ...
doi:10.1007/3-540-58330-0_93
fatcat:2zr6ngwrljfrto5jkotrkzgrl4
Explanation facilities and interactive systems
1993
Proceedings of the 1st international conference on Intelligent user interfaces - IUI '93
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Our main research aim is to improve the provision of explanation facilities in information systems generally, and to identify what is meant by "explanation". ...
We are also concerned with both present and future uses of explanation in information systems and the role of explanation in a broad range of interactive applications. ...
A central notion in FEATS is that the systcm will generate and present to the pilot an ideal task scenario for carrying out particular tasks in the cockpit, and in addition the system will be able to provide ...
doi:10.1145/169891.169951
dblp:conf/iui/JohnsonJ93
fatcat:eps4zi3d4jft5ea35yvyjtcm64
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