Towards General Evaluation of Intelligent Systems: Using Semantic Analysis to Improve Environments in the AIQ Test

2018 ◽  
pp. 248-258
Author(s):  
Ondřej Vadinský
Keyword(s):  
Intelligent Systems ◽  
Semantic Analysis ◽  
General Evaluation

scholarly journals Linguistic Ontologies: Designing and Using in the Educational Intellectual Systems

2021 ◽  
Vol 4 (1) ◽  
pp. 97-111
Author(s):  
Olha Tkachenko ◽  
Kostiantyn Tkachenko ◽  
Oleksandr Tkachenko
Keyword(s):  
Computer Networks ◽  
Intelligent Systems ◽  
Semantic Analysis ◽  
Intelligent System ◽  
Subject Area ◽  
Intellectual System ◽  
Proposed Model ◽  
Small Set ◽  
Ontological Model ◽  
Linguistic Ontology

The purpose of the article is to investigate and consider the general trends, problems and prospects of designing and using linguistic ontologies in educational intellectual systems. The research methodology consists in semantic analysis methods of the basic concepts in the considered subject area (linguistic ontologies in the educational intellectual systems). The article discusses approaches to the use of linguistic models in modern educational intelligent systems. The novelty of the research is the analysis of the linguistic ontologies use in the educational intellectual systems. Conclusions. A model of linguistic ontology for the domain (disciplines “Computer Networks” and “Modelling Systems”) is presented. This model is used in the development of an educational intellectual system that supports online learning in these disciplines. The proposed model describes a set of relations of linguistic ontology, specially selected to describe the analyzed domain. To ensure these properties, it was proposed to use a small set of relationships. The proposed linguistic ontological model is implemented in an educational intelligent system that supports such disciplines as “Computer Networks” and “Modelling Systems”.

scholarly journals Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results

2018 ◽  
Vol 9 (1) ◽  
pp. 1-54 ◽  
Author(s):  
Ondřej Vadinský
Keyword(s):  
Intelligent Systems ◽  
Intelligence Quotient ◽  
Evaluation Method ◽  
Lessons Learned ◽  
Action Space ◽  
Test Results ◽  
General Evaluation ◽  
Observation Space ◽  
The Impact ◽  
Reference Machine

Abstract This paper attempts to replicate the results of evaluating several artificial agents using the Algorithmic Intelligence Quotient test originally reported by Legg and Veness. Three experiments were conducted: One using default settings, one in which the action space was varied and one in which the observation space was varied. While the performance of freq, Q0, Qλ, and HLQλ corresponded well with the original results, the resulting values differed, when using MC-AIXI. Varying the observation space seems to have no qualitative impact on the results as reported, while (contrary to the original results) varying the action space seems to have some impact. An analysis of the impact of modifying parameters of MC-AIXI on its performance in the default settings was carried out with the help of data mining techniques used to identifying highly performing configurations. Overall, the Algorithmic Intelligence Quotient test seems to be reliable, however as a general artificial intelligence evaluation method it has several limits. The test is dependent on the chosen reference machine and also sensitive to changes to its settings. It brings out some differences among agents, however, since they are limited in size, the test setting may not yet be sufficiently complex. A demanding parameter sweep is needed to thoroughly evaluate configurable agents that, together with the test format, further highlights computational requirements of an agent. These and other issues are discussed in the paper along with proposals suggesting how to alleviate them. An implementation of some of the proposals is also demonstrated.

scholarly journals Ontological Modelling of the Decision-making Support Intellectual System in Risks Analysis in the Innovation and Investment Sphere

2021 ◽  
Vol 4 (1) ◽  
pp. 66-78
Author(s):  
Kostiantyn Tkachenko ◽  
Andrii Baidak
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Semantic Analysis ◽  
Subject Area ◽  
Investment Projects ◽  
Problem Situations ◽  
Intellectual System ◽  
The Status ◽  
Develop Software ◽  
Decision Making Processes

The purpose of the article is to study, analyze and consider general problems and prospects of using ontologies when modelling decision-making processes in intelligent systems when analyzing the risks of the innovation and investment sphere. The research methodology lies in methods of the basic concepts semantic analysis of a given subject area (innovation and investment projects and management processes for the implementation of these projects). The article discusses the existing approaches to modelling decision-making processes in the analysis of risks in the innovation and investment sphere. The novelty of the research is the intellectualization problems solving of processes in the innovation and investment sphere on the basis of formal ontological models. Conclusions. The practical value of the presented results lies in the development and use of knowledge management system components for identifying and predicting problem situations in innovation and investment projects. The proposed integrated ontology can be used in the management of innovation and investment projects in various subject areas since it contains classes of concepts that have the status of a project activity standard. The developed ontological model makes it possible to develop software architecture for an intelligent decision support system, develop metadata and build a set of interrelated thesauri to support the semantics of end-user requests.

Referential-Semantic Analysis

1980 ◽  
Author(s):  
Torben Thrane
Keyword(s):  
Semantic Analysis

Feel Thine Own Self – Mood Congruency Evaluation of Emotional State Adjectives

2020 ◽  
pp. 1-17
Author(s):  
Szczepan J. Grzybowski ◽  
Miroslaw Wyczesany ◽  
Jan Kaiser
Keyword(s):  
Mood Induction ◽  
Semantic Analysis ◽  
Emotional State ◽  
Time Window ◽  
Event Related Potential ◽  
Negativity Bias ◽  
Positive Potential ◽  
Semantic Access ◽  
Current Mood ◽  
Component Time

Abstract. The goal of the study was to explore event-related potential (ERP) differences during the processing of emotional adjectives that were evaluated as congruent or incongruent with the current mood. We hypothesized that the first effects of congruence evaluation would be evidenced during the earliest stages of semantic analysis. Sixty mood adjectives were presented separately for 1,000 ms each during two sessions of mood induction. After each presentation, participants evaluated to what extent the word described their mood. The results pointed to incongruence marking of adjective’s meaning with current mood during early attention orientation and semantic access stages (the P150 component time window). This was followed by enhanced processing of congruent words at later stages. As a secondary goal the study also explored word valence effects and their relation to congruence evaluation. In this regard, no significant effects were observed on the ERPs; however, a negativity bias (enhanced responses to negative adjectives) was noted on the behavioral data (RTs), which could correspond to the small differences traced on the late positive potential.

Conference on intelligent systems

1967 ◽  
Author(s):  
Rowena Swanson
Keyword(s):  
Intelligent Systems
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