1,311 research outputs found
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models
Structure learning is a core problem in AI central to the fields of
neuro-symbolic AI and statistical relational learning. It consists in
automatically learning a logical theory from data. The basis for structure
learning is mining repeating patterns in the data, known as structural motifs.
Finding these patterns reduces the exponential search space and therefore
guides the learning of formulas. Despite the importance of motif learning, it
is still not well understood. We present the first principled approach for
mining structural motifs in lifted graphical models, languages that blend
first-order logic with probabilistic models, which uses a stochastic process to
measure the similarity of entities in the data. Our first contribution is an
algorithm, which depends on two intuitive hyperparameters: one controlling the
uncertainty in the entity similarity measure, and one controlling the softness
of the resulting rules. Our second contribution is a preprocessing step where
we perform hierarchical clustering on the data to reduce the search space to
the most relevant data. Our third contribution is to introduce an O(n ln n) (in
the size of the entities in the data) algorithm for clustering
structurally-related data. We evaluate our approach using standard benchmarks
and show that we outperform state-of-the-art structure learning approaches by
up to 6% in terms of accuracy and up to 80% in terms of runtime.Comment: Submitted to AAAI23. 9 pages. Appendix include
Novel Rule Base Development from IED-Resident Big Data for Protective Relay Analysis Expert System
Many Expert Systems for intelligent electronic device (IED) performance analyses such as those for protective relays have been developed to ascertain operations, maximize availability, and subsequently minimize misoperation risks. However, manual handling of overwhelming volume of relay resident big data and heavy dependence on the protection experts’ contrasting knowledge and inundating relay manuals have hindered the maintenance of the Expert Systems. Thus, the objective of this chapter is to study the design of an Expert System called Protective Relay Analysis System (PRAY), which is imbedded with a rule base construction module. This module is to provide the facility of intelligently maintaining the knowledge base of PRAY through the prior discovery of relay operations (association) rules from a novel integrated data mining approach of Rough-Set-Genetic-Algorithm-based rule discovery and Rule Quality Measure. The developed PRAY runs its relay analysis by, first, validating whether a protective relay under test operates correctly as expected by way of comparison between hypothesized and actual relay behavior. In the case of relay maloperations or misoperations, it diagnoses presented symptoms by identifying their causes. This study illustrates how, with the prior hybrid-data-mining-based knowledge base maintenance of an Expert System, regular and rigorous analyses of protective relay performances carried out by power utility entities can be conveniently achieved
Template-based Ontology Evolution
Katedra kybernetik
A Concise Fuzzy Rule Base to Reason Student Performance Based on Rough-Fuzzy Approach
A fuzzy inference system employing fuzzy if then rules able to model the qualitative aspects of human expertise and reasoning processes without employing precise quantitative analyses. This is due to the fact that the problem in acquiring knowledge from human experts is that much of the information is uncertain, inconsistent, vague and incomplete (Khoo and Zhai, 2001; Tsaganou et al., 2002; San Pedro and Burstein, 2003; Yang et al., 2005). The drawbacks of FIS are that a lot of trial and error effort need to be taken into account in order to define the best fitted membership functions (Taylan and Karagözoglu, 2009) and no standard methods exist for transforming human knowledge or experience into the rule base (Jang, 1993)
- …