Specialized intelligent agents actions planning methods based on ontological approach

2017 ◽  
Vol 2017 (45) ◽  
pp. 96-103
Author(s):  
V.V. Lytvyn ◽  
◽  
R.V. Vovnjanka ◽  
D.G. Dosyn ◽  
◽  
...  
Keyword(s):  
Intelligent Agents ◽  
Optimization Problem ◽  
Principal Component ◽  
Action Planning ◽  
Search Space ◽  
Initial State ◽  
Ontological Approach ◽  
Planning Methods ◽  
The Individual ◽  
Intelligent Action

The solution of the applied task of constructing intelligent agents (IA) of action planning is proposed. The mathematical support of functioning of intellectual agents of action planning on the basis of ontologies is developed, which made it possible to formalize the behavior of such agents in the state space. The use of ontologies allows narrowing the search space for path from the initial state to the target state, rejecting irrelevant alternatives. A method of narrowing the search area for optimal IA activity is proposed. To assess the reaction of the environment on the behaviour of the IA a method based on reinforcement learning is developed. The two-criterion optimization problem of dynamic programming is formulated, which is solved by one of the iterative methods – by principal component analysis or by the multiple criterion method, depending on the possibility to numerically estimate the target functions of this optimization problem. The architecture of the system of planning the actions of specialized intelligence agents is proposed. It consists of an ontology that contains ontology of tasks, the solution of which is aimed at the functioning of a specialized IA, and a domain ontology, which sets out alternatives to solving individual subtasks. On the example of the problem of corrosion protection of the water supply or gas pipeline pipe the efficiency of the proposed approach is investigated. The software for the functioning of intelligent action planning agents based on constructed models, methods and algorithms has been developed, which make it possible to implement the individual components and functional modules of intellectual action planning agents on the basis of ontologies.

scholarly journals Method of functioning of intelligent agents, designed to solve action planning problems based on ontological approach

2017 ◽  
Vol 3 (2 (87)) ◽  
pp. 11-17 ◽  
Author(s):  
Vasyl Lytvyn ◽  
Victoria Vysotska ◽  
Petro Pukach ◽  
Miroslava Vovk ◽  
Dmytro Ugryn
Keyword(s):  
Intelligent Agents ◽  
Action Planning ◽  
Ontological Approach ◽  
Planning Problems

Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization

2021 ◽  
pp. 1-16
Author(s):  
Qianjin Wei ◽  
Chengxian Wang ◽  
Yimin Wen
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Search Space ◽  
Initial State ◽  
Solution Quality ◽  
Social Spider ◽  
Opposition Based Learning ◽  
Social Spider Optimization ◽  
Intelligent Optimization Algorithm ◽  
Redundancy Detection

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.

scholarly journals Bioactive Phenolic Metabolites from Adriatic Brown Algae Dictyota dichotoma and Padina pavonica (Dictyotaceae)

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1187
Author(s):  
Ivana Generalić Mekinić ◽  
Vida Šimat ◽  
Viktorija Botić ◽  
Anita Crnjac ◽  
Marina Smoljo ◽  
...  
Keyword(s):  
Total Phenolics ◽  
Antioxidant Activities ◽  
Principal Component ◽  
Ultrasound Assisted Extraction ◽  
Antioxidant Power ◽  
Dictyota Dichotoma ◽  
Assisted Extraction ◽  
Sample Data ◽  
Ferric Reducing Antioxidant Power ◽  
The Individual

In this study, the influences of temperature (20, 40 and 60 °C) and extraction solvents (water, ethanol) on the ultrasound-assisted extraction of phenolics from the Adriatic macroalgae Dictyota dichotoma and Padina pavonica were studied. The extracts were analysed for major phenolic sub-groups (total phenolics, flavonoids and tannins) using spectrometric methods, while the individual phenolics were detected by HPLC. The antioxidant activities were evaluated using three methods: Ferric Reducing/Antioxidant Power (FRAP), scavenging of the stabile 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and Oxygen Radical Antioxidant Capacity (ORAC). The aim of the study was also to find the connection between the chemical composition of the extracts and their biological activity. Therefore, principal component analysis (PCA), which permits simple representation of different sample data and better visualisation of their correlations, was used. Higher extraction yields of the total phenolics, flavonoids and tannins were obtained using an alcoholic solvent, while a general conclusion about the applied temperature was not established. These extracts also showed good antioxidant activity, especially D. dichotoma extracts, with high reducing capacity (690–792 mM TE) and ORAC values (38.7–40.8 mM TE in 400-fold diluted extracts). The PCA pointed out the significant influence of flavonoids and tannins on the investigated properties. The results of this investigation could be interesting for future studies dealing with the application of these two algae in foods, cosmetics and pharmaceuticals.

scholarly journals Qualitative Analysis of Traditional Italian Dishes: FTIR Approach

2018 ◽  
Vol 10 (11) ◽  
pp. 4112 ◽  
Author(s):  
Alessandra Durazzo ◽  
Johannes Kiefer ◽  
Massimo Lucarini ◽  
Emanuela Camilli ◽  
Stefania Marconi ◽  
...  
Keyword(s):  
Qualitative Analysis ◽  
Ftir Spectroscopy ◽  
Functional Groups ◽  
Food Systems ◽  
Principal Component ◽  
Integrated Approach ◽  
Spectroscopic Characterization ◽  
Cooking Methods ◽  
Food Research ◽  
The Individual

Italian cuisine and its traditional recipes experience an ever-increasing popularity around the world. The “Integrated Approach” is the key to modern food research and the innovative challenge for analyzing and modeling agro-food systems in their totality. The present study aims at applying and evaluating Fourier Transformed Infrared (FTIR) spectroscopy for the analysis of complex food matrices and food preparations. Nine traditional Italian recipes, including First courses, One-dish meals, Side courses, and Desserts, were selected and experimentally prepared. Prior to their analysis via FTIR spectroscopy, the samples were homogenized and lyophilized. The IR spectroscopic characterization and the assignment of the main bands was carried out. Numerous peaks, which correspond to functional groups and modes of vibration of the individual components, were highlighted. The spectra are affected by both the preparation procedures, the cooking methods, and the cooking time. The qualitative analysis of the major functional groups can serve as a basis for a discrimination of the products and the investigation of fraud. For this purpose, the FTIR spectra were evaluated using Principal Component Analysis (PCA). Our results show how the utilization of vibrational spectroscopy combined with a well-established chemometric data analysis method represents a potentially powerful tool in research linked to the food sector and beyond. This study is a first step towards the development of new indicators of food quality.

scholarly journals Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3343 ◽  
Author(s):  
Yi-Fei Pei ◽  
Qing-Zhi Zhang ◽  
Zhi-Tian Zuo ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Information ◽  
Accurate Identification ◽  
Mid Infrared ◽  
Model Classification ◽  
Paris Polyphylla ◽  
Mir Spectra ◽  
The Individual

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.

scholarly journals Feature Selection using Genetic Programming

2019 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
George Mweshi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Information Gain ◽  
Principal Component ◽  
Search Space ◽  
Classification Performance ◽  
The Other ◽  
Searching Strategy ◽  
Mining Algorithms ◽  
Feature Selection Techniques

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

scholarly journals Feed efficiency of lactating Holstein cows is less reproducible when changing dietary starch and fibre concentrations than within diet over subsequent lactation stages

2021 ◽  
Author(s):  
Amélie Fischer ◽  
Philippe Gasnier ◽  
Philippe Faverdin
Keyword(s):  
Principal Components ◽  
Feed Efficiency ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Holstein Cows ◽  
Diet Change ◽  
Total Variability ◽  
The One ◽  
The Individual ◽  
Starch Diet

ABSTRACTBackgroundImproving feed efficiency has become a common target for dairy farmers to meet the requirement of producing more milk with fewer resources. To improve feed efficiency, a prerequisite is to ensure that the cows identified as most or least efficient will remain as such, independently of diet composition. Therefore, the current research analysed the ability of lactating dairy cows to maintain their feed efficiency while changing the energy density of the diet by changing its concentration in starch and fibre. A total of 60 lactating Holstein cows, including 33 primiparous cows, were first fed a high starch diet (diet E+P+), then switched over to a low starch diet (diet E−P−). Near infra-red (NIR) spectroscopy was performed on each individual feed ingredient, diet and individual refusals to check for sorting behaviour. A principal component analysis (PCA) was performed to analyse if the variability in NIR spectra of the refusals was explained by the differences in feed efficiency.ResultsThe error of reproducibility of feed efficiency across diets was 2.95 MJ/d. This error was significantly larger than the errors of repeatability estimated within diet over two subsequent lactation stages, which were 2.01 MJ/d within diet E−P− and 2.40 MJ/d within diet E+P+. The coefficient of correlation of concordance (CCC) was 0.64 between feed efficiency estimated within diet E+P+ and feed efficiency estimated within diet E−P−. This CCC was smaller than the one observed for feed efficiency estimated within diet between two subsequent lactation stages (CCC = 0.72 within diet E+P+ and 0.85 within diet E−P−). The first two principal components of the PCA explained 90% of the total variability of the NIR spectra of the individual refusals. Feed efficiency was poorly correlated to those principal components, which suggests that feed sorting behaviour did not explain differences in feed efficiency.ConclusionsFeed efficiency was significantly less reproducible across diets than repeatable within the same diet over subsequent lactation stages, but cow’s ranking for feed efficiency was not significantly affected by diet change. The differences in sorting behaviour between cows were not associated to feed efficiency differences in this trial neither with the E+P+ diet nor with the E−P− diet. Those results have to be confirmed with cows fed with more extreme diets (for example roughage only) to ensure that the least and most efficient cows will not change.

scholarly journals Individualization of Products and Services with Artificial General Intelligence and General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Online Education ◽  
News Media ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Online News ◽  
General Intelligence ◽  
Entertainment Media ◽  
Individual User ◽  
Artificial General Intelligence ◽  
The Individual

INTRODUCTION: With advances in big data techniques having already led to search results and advertising being customized to the individual user, the concept of an online education designed solely for an individual, or the concept of online news or entertainment media, or any other virtual service being designed uniquely for each individual, no longer seems as far fetched. However, designing services that maximize user outcomes as opposed to services that maximize outcomes for the corporation owning them, requires modeling user processes and the outcomes they target.OBJECTIVES: To explore the use of Human-Centric Functional Modeling (HCFM) to define functional state spaces within which human processes are well-defined paths, and within which products and services solve specific navigation problems, so that by considering all of any given individual’s desired paths through a given state space, it is possible to automate the customization of those products and services for that individual or to groups of individuals.METHODS: An analysis is performed to assess how and whether intelligent agents based on some subset of functionality required for Artificial General Intelligence (AGI) might be used to optimize for the individual user. And an analysis is performed to determine whether and if so how General Collective Intelligence (GCI) might be used to optimize across all users.RESULTS: AGI and GCI create the possibility to individualize products and services, even shared services such as the Internet, or news services so that every individual sees a different version.CONCLUSION: The conceptual example of customizing a news media website for two individual users of opposite political persuasions suggests that while the overhead of customizing such services might potentially result in massively increased storage and processing overhead, within a network of cooperating services in which this customization reliably creates value, this is potentially a significant opportunity.

scholarly journals Differences in acid phosphomonoesterase activity in forest soils of the Smrk Massive in the Moravskoslezské Beskydy Mts. (Czech Republic)

Beskydy ◽  
2012 ◽  
Vol 5 (2) ◽  
pp. 135-152
Author(s):  
A. Bajer ◽  
P. Samec ◽  
M. Žárník
Keyword(s):  
Relative Size ◽  
Chemical Properties ◽  
Principal Component ◽  
European Beech ◽  
Soil Characteristics ◽  
Strong Correlations ◽  
Spruce Stands ◽  
The Individual ◽  
A Site

The purpose of this paper is to determine the individual relations between APEA and specific soils and environmental factors. To disclose these relations, analysis of component vectors and principal component analysis (PCA) were performed. Vectors of soil characteristics with participation of APEA (aAKFE) and vectors of pedochemical variables (aCHEM) were also calculated. Their ratio (ia) indicated the relative size of the APEA impact on the relations between pedochemical characteristics. Based on the statistical analyses, different role of APEA in Norway spruce and in European beech stands was detected. While APEA in spruce stands did not show significant correlations with the other examined soil chemical properties, soils under beech stands displayed strong correlations with some of the pedochemical variables. The idea of this research is to find out whether APEA could be used as an indicator of forest vegetation status and of the anthropogenic load on a site.

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