Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

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ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

Symmetry ◽

10.3390/sym11091123 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1123

Author(s):

Iantovics ◽

Kountchev ◽

Crișan

Keyword(s):

Problem Solving ◽

Multiagent Systems ◽

Intelligent Systems ◽

Scientific Literature ◽

Real Life ◽

Evaluation Study ◽

Difficult Problem ◽

Machine Intelligence ◽

Universal Method ◽

Life Problems

In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.

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A Hybrid Secure Storage Scheme to Avoid EDOS Attacks in Cloud Computing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5861.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1236-1242

Keyword(s):

Cloud Computing ◽

Problem Solving ◽

Prior Knowledge ◽

Private Information ◽

Black Box ◽

Cloud Environment ◽

Secure Storage ◽

Storage Scheme ◽

Edos Attacks ◽

Box Method

In advent of cloud environment, cloud operator is not a completely trusted to put on private information, because of lack of consumer to cloud control. To assurance privacy, documents sharer deploy their encipher documents. Encipher documents dispense to among consumers using CP-ABE scheme. But it is not completely safe in opposition to different assaults. The prior knowledge cannot offer any verification ability to cloud operator whether the user can decipher or not. Various invaders may obtain lot of document by initiate EDoS assaults. The consumer of cloud abides cost. To handle above issues, this article suggests a problem solving plan to safe encipher cloud repository from EDoS assaults and maintain supply utilization. It utilizes CP-ABE tactics in a black-box method furthermore accomplish impulsive entryway contract epithetical CP-ABE. We tend to present 2 mechanisms for various styles, observed via achievement and shield research.

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Predicate-Based Model of Problem-Solving for Robotic Actions Planning

Mathematics ◽

10.3390/math9233044 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3044

Author(s):

Oleksandr Tsymbal ◽

Paolo Mercorelli ◽

Oleg Sergiyenko

Keyword(s):

Problem Solving ◽

Set Theory ◽

Manufacturing Systems ◽

Intelligent Agent ◽

Adaptive Strategies ◽

Point Of View ◽

Logical Model ◽

Agent Based ◽

Dynamic Structures ◽

Robotic Applications

The aim of the article is to describe a predicate-based logical model for the problem-solving of robots. The proposed article deals with analyses of trends of problem-solving robotic applications for manufacturing, especially for transportations and manipulations. Intelligent agent-based manufacturing systems with robotic agents are observed. The intelligent cores of them are considered from point of view of ability to propose the plans of problem-solving in the form of strategies. The logical model of adaptive strategies planning for the intelligent robotic system is composed in the form of predicates with a presentation of data processing on a base of set theory. The dynamic structures of workspaces, and a possible change of goals are considered as reasons for functional strategies adaptation.

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Model of Problem Solving for Robotic Actions Planning

10.20944/preprints202101.0028.v1 ◽

2021 ◽

Author(s):

Oleksandr Tsymbal ◽

Paolo Mercorelli

Keyword(s):

Problem Solving ◽

Data Processing ◽

Set Theory ◽

Manufacturing Systems ◽

Intelligent Agent ◽

Dynamic Structure ◽

Adaptive Strategies ◽

Robotic Platform ◽

Agent Based ◽

Robotic Applications

Proposed article deals with analyses on trends of problem-solving robotic applications in manu-facturing, especially in transportations and manipulations. Intelligent agent-based manufacturing systems with robotic agents are observed. Intelligent core of such units must be able to propose the plan of problem-solving in form of strategy. The logical model of adaptive strategies planning for intelligent robotic system is described in form of predicates with presentation of data processing on base of set theory descriptions. Dynamic structure of workspace and possible change of goals are considered as reasons for functional strategies adaptation. Proposed formal descriptions are sup-ported by model of mobile robotic platform, acting in warehouse.

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The Effects of Math-Based Convergence Program Using Nature and Real Life Examples on the Mathematical Personality and Creative Problem Solving Ability of Elementary School Gifted Students

Journal of Curriculum Integration ◽

10.35304/jci.12.4.05 ◽

2018 ◽

Vol 12 (4) ◽

pp. 99-133

Author(s):

Eunkyung Lee ◽

Mihyun Yoo

Keyword(s):

Elementary School ◽

Problem Solving ◽

Real Life ◽

Gifted Students ◽

Creative Problem Solving ◽

Creative Problem

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Intelligent Agent Feasibility Study. Volume 1: Agent-based System Technology

10.21236/ada359612 ◽

1998 ◽

Author(s):

Stephen E. Deutsch

Keyword(s):

Feasibility Study ◽

Intelligent Agent ◽

System Technology ◽

Agent Based

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What affordances do open-ended real-life tasks offer for sharing student agency in collaborative problem-solving?

Educational Studies in Mathematics ◽

10.1007/s10649-021-10074-9 ◽

2021 ◽

Author(s):

Juuso Henrik Nieminen ◽

Man Ching Esther Chan ◽

David Clarke

Keyword(s):

Problem Solving ◽

Video Recording ◽

Real Life ◽

Collaborative Problem Solving ◽

Student Agency ◽

Student Groups ◽

Collaborative Problem ◽

Mathematical Arguments ◽

Life Tasks ◽

Collaborative Construction

AbstractThe important role of student agency in collaborative problem-solving has been acknowledged in previous mathematics education research. However, what remains unknown are the processes of agency in open-ended tasks that draw on real-life contexts and demand argumentation beyond “mathematical”. In this study, we analyse a video recording of two student groups (each consisting of four students) taking part in collaborative problem-solving. We draw on the framework for collaborative construction of mathematical arguments and its interplay with student agency by Mueller et al. (2012). This original framework is supplemented by (i) testing and revising it in the context of open-ended real-life tasks, with (ii) student groups rather than pairs working on the tasks, and by (iii) offering a strengthened methodological pathway for analysing student agency in such a context. Based on our findings, we suggest that the framework suits this new context with some extensions. First, we note that differences in student agency were not only identified in terms of the discourse students drew on, but in how students were able to shift between various discourses, such as between “mathematical” and “non-mathematical” discourses. We identify a novel discourse reflecting student agency, invalidation discourse, which refers to denying other students’ agency by framing their contribution as invalid. Finally, we discuss the need to reframe “mathematical” arguments—and indeed student agency—while the task at hand is open-ended and concerns real-life contexts.

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Assisting humans in privacy management: an agent-based approach

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-020-09488-1 ◽

2020 ◽

Vol 35 (1) ◽

Author(s):

A. Can Kurtan ◽

Pınar Yolum

Keyword(s):

Online Social Networks ◽

Online Social Network ◽

Real Life ◽

Privacy Management ◽

Agent Based ◽

Image Sharing ◽

Privacy Preferences ◽

Privacy Settings ◽

Privacy Setting ◽

Sharing Behavior

AbstractImage sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy preferences, editing these privacy settings is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an online social network. Accordingly, this paper proposes a privacy recommendation model for images using tags and an agent that implements this, namely pelte. Each user agent makes use of the privacy settings that its user have set for previous images to predict automatically the privacy setting for an image that is uploaded to be shared. When in doubt, the agent analyzes the sharing behavior of other users in the user’s network to be able to recommend to its user about what should be considered as private. Contrary to existing approaches that assume all the images are available to a centralized model, pelte is compatible to distributed environments since each agent accesses only the privacy settings of the images that the agent owner has shared or those that have been shared with the user. Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user’s friends have varying privacy preferences.

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