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Distributed knowledge by explanation networks
2004
37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the
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To conclude, the idea of distributing knowledge by support from an explanation network is fruitful and feasible. ...
The explanation network idea has great knowledge acquisition power. ...
This work has been supported by grants from the Swedish Board for Research in The Humanities and Social Sciences and the Swedish Board for Technical Development. ...
doi:10.1109/hicss.2004.1265332
dblp:conf/hicss/WaernR04
fatcat:jkozcsyrp5dcdjtsj6gy7wgmla
A Hybrid System Composed of Neural Networks and Genetic Algorithms
2012
International Journal of Asian Business and Information Management
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In contrast to the systems mentioned above, neural networks are not generally able to offer explanations, because knowledge are not explicit, knowledge are transferred in the form of distributed weights ...
Given the distributed way of knowledge representation, neural networks have some advantages in this respect. ...
doi:10.4018/jabim.2012100105
fatcat:5obtxkfkbzc3dmxil3iytcuf6y
The use of an explanation algorithm in a clinical event monitor
1999
Proceedings. AMIA Symposium
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Therefore, we decided to implement an advanced method for explanation (Suermondt's method for belief-network explanation). ...
Clinical event monitors (CEMs) seek to improve patient care and reduce its cost by prompting clinicians to take actions that have these effects. ...
Acknowledgements This work was supported by grants T15 LM/DE07059 and 5 R29 LM06233 from the National Library of Medicine. ...
pmid:10566365
pmcid:PMC2232853
fatcat:t726i4s2ofbm3p6a5clwc3bwoy
Handling Revision Inconsistencies: Creating Useful Explanations
2015
2015 48th Hawaii International Conference on System Sciences
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In this article we examine the topic of creating and presenting explanations by discussing different components of inconsistencies and resulting consequences for explanations. ...
initial distribution. ...
One popular method for storing knowledge is by representing it in form of probability distributions and using the revision operation to incorporate new beliefs while keeping changes to the original knowledge ...
doi:10.1109/hicss.2015.447
dblp:conf/hicss/SchmidtGK15
fatcat:un4irznuo5cjfcd7fxunmauawu
Explaining Neural Networks Semantically and Quantitatively
[article]
2018
arXiv
pre-print
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This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. ...
The analysis of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, but it is also of significant practical values in certain applications. ...
Quantitative explanations α i y i for the male attribute. ...
arXiv:1812.07169v1
fatcat:e3d4cgdc6zhxndndvkkh24hhhq
On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research
2020
Information
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Within such a general direction, we identify three specific challenges for future research—knowledge matching, cross-disciplinary explanations and interactive explanations. ...
A complementary approach is represented by symbolic AI, where symbols are elements of a lingua franca between humans and deep learning. ...
As underlined by Rumelhart et al. [18] , the resulting knowledge consists of the connections between these computational units distributed throughout the network. ...
doi:10.3390/info11020122
fatcat:77ni2i6tdrhqxopw25vbybghi4
Page 197 of American Society of Civil Engineers. Collected Journals Vol. 118, Issue CP2
[page]
1992
American Society of Civil Engineers. Collected Journals
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In Neuroform the problem knowledge is distributed in the network weights and these weights create the desired mapping be- tween the system inputs and the outputs. ...
In Neuroform the values of the network weights are determined by the network training and during the retrieval of the system outputs, all the weights are considered simultaneously (or in parallel). ...
Inference and reasoning in a Bayesian knowledge-intensive CBR system
2021
Progress in Artificial Intelligence
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AbstractThis paper presents the inference and reasoning methods in a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. ...
The semantic network inference methods and the CBR method are employed to handle the difficulties of inferencing and reasoning in uncertain domains. ...
Pål Skalle for providing the drilling network model, and preparing the drilling cases, and Prof. Helge Langseth and Dr. Frode Sørmo for their useful suggestions. ...
doi:10.1007/s13748-020-00223-1
fatcat:fj5vslq6ovb4bja76jvsehpjju
Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems
2005
International Journal of Hybrid Intelligent Systems
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Section 2 discusses background knowledge focusing on the advantages and disadvantages of symbolic rules and neural networks. ...
We then stress the point that not all hybrid neuro-symbolic approaches can be accommodated by existing categories. ...
Acknowledgements This work was supported by the Research Committee of the University of Patras, Greece, Program "Karatheodoris", project No 2788. ...
doi:10.3233/his-2004-13-401
fatcat:uzemw427gjg6vj4tjqy6biddim
An intelligent problem solving environment for designing explanation models and for diagnostic reasoning in probabilistic domains
[chapter]
1996
Lecture Notes in Computer Science
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This differs from existing reasoning systems based on Bayesian networks, i.e. in medical domains, which contain a built-in knowledge base that may be used but not created or modified by the learner. ...
Uncertainty is handled by the Bayesian network approach. Thus the modelling task for the learner consists of creating a Bayesian network for the problem at hand. ...
In this case, it is the uncertainty of knowledge, which we chose to handle by the Bayesian network approach. ...
doi:10.1007/3-540-61327-7_133
fatcat:4pxeqn3cbnbl7khl7ue6lsupxa
Correlation DialTone-Building Internet—Based Distributed Event Correlation Services
[chapter]
2000
Lecture Notes in Computer Science
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We describe process/knowledge representation models and implementation of the distributed correlation service using CORBA, XML, Java, and model-based reasoning technologies. ...
We present the component-based architecture of the distributed event correlation service and its component services: correlation subscription, data mediation, event parsing, event correlation, event delivery ...
Explanation Service The Explanation Service (Figure 4 ) is used to analyze conclusions or situations recognized by the Correlation Engine. ...
doi:10.1007/10722515_9
fatcat:som6dimg7ze23ath3tg3igohu4
The split-up system
1995
Proceedings of the fifth international conference on Artificial intelligence and law - ICAIL '95
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Argument structures proposed by Toulmin can be used to represent legal knowledge in a manner that enables rulebased reasoning to be integrated with neural networks. ...
Because explanations are at least as important as conclusions, we iHustrate the use of Toulrnin structures in the generation of explanations for conclusions reached by either mle sets or neural networks ...
The knowledge
that each expert
brings to the task is represented within the knowledge
base by Toulmin structures. ...
doi:10.1145/222092.222235
dblp:conf/icail/ZeleznikowS95
fatcat:oxq7yzjf2rbwnpbdudojxuptoe
Neurosymbolic AI: The 3rd Wave
[article]
2020
arXiv
pre-print
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We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. ...
for network models. ...
In neural-symbolic computation, knowledge learned by a neural network can be represented symbolically. Reasoning takes place either symbolically or within the network in distributed form. ...
arXiv:2012.05876v2
fatcat:m5g4kvcvo5epzmlsmft7vj5hva
Page 934 of Psychological Abstracts Vol. 80, Issue 2
[page]
1993
Psychological Abstracts
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In neural networks, knowledge is encoded in numeric parameters and distributed all over the system. This paper dis- cusses the ability of neural networks to generate explanations. ...
Connectionist semantic networks (i.e., connectionist systems with an explicit conceptual hierarchy) be- long to a class of artificial neural networks that can be extended by an explanation component that ...
Introspective Learning by Distilling Knowledge from Online Self-explanation
[article]
2020
arXiv
pre-print
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We start by investigating the effective components of the knowledge transferred from the teacher network to the student network. ...
Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations. ...
Networks Learned from Peer Networks In our proposed algorithm, we trains a network with knowledge in explanations created on the same network. ...
arXiv:2009.09140v1
fatcat:z2j222u77rb23fetcxaoqhe6n4
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