The Persuasive Tutor: a BDI Teaching Agent with Role and Reference Grammar Language Interface – Sustainable design of a conversational agent for language learning

This paper investigates how an intelligent teaching agent with Role and Reference Grammar [RRG] (cf. Van Valin 2005) as linguistic engine can support language learning. Based on a user-centred empirical design study the architecture of a highly persuasive tool for language learning as an extension of PLOTLearner (http://europlot.blogspot.dk/2012/07/try-plotlearner-2.html) is developed. Based on grounded theory it is shown that feedback and support is of greatest importance even in self-directed computer assisted language learning. Is also shown how this overall approach to language learning can be situated into traditional conversation based learning theories (cf. Laurillard 2009). It is shown that a computationally adequate model of the RRG-linking algorithm, extended into a computational processing model, can account for communication between a learner and the software by employing conceptual graphs to represent mental states in the software agent and the important role of speech acts is emphasized in this context

Middle-Agents Organized in Fault Tolerant and Fixed Scalable Structure

Agents in a multi-agent system usually use middle-agents to locate service providers. Since one central middle-agent represents a single point of failure and communication bottleneck in the system, therefore a structure of middle-agents is used to overcome these issues. We designed and implemented a structure of middle-agents called dynamic hierarchical teams that has user-defined level of fault-tolerance and is moreover fixed scalable. We prove that the structure that has teams of size lambda has vertex and edge connectivity equal to lambda, i.e., the structure stays connected despite lambda-1 failures of middle-agents or lambda-1 communication channels. We focus on social knowledge management describing several methods that can be used for social knowledge propagation and search in this structure. We also test the fault-tolerance of this structure in practical experiments

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant

Multi-Agent System Based Distributed Voltage Control in Distribution Systems

Distribution System is a standout among the most complex entities of the electric power grid. Moreover, voltage quality sustainability till customer premises, with the introduction of Distributed Generation (DG), is one of the most frenzied control areas. Previously, SCADA in cohesion with Wide Area Measurement Systems (WAMS) was a dependable control strategy, yet as the ever growing and complex distribution system is advancing towards the Smart Grids, control strategies are becoming more and more distributed in spite of the centralized one. A detailed literature review of the voltage control methods ranging from the centralized one to the fully distributed agent based control is conducted. In the light of the previous researches, a distributed voltage control based on Multi-Agent System is proposed, as the agents based control strategies, are becoming well known day by day, due to its autonomous control and decision making capacity. To make the proposed algorithm fully distributed, token transversal through the network and agents communication to remove voltage violation over least correspondence and measurements of the system, are utilized. Following instant voltage control at the load nodes, a penalty function is employed to keep the voltage value curve throughout the network as close as possible to the nominal, with minimum network losses and minimum voltage damage. The authentication of the devised control algorithm is acknowledged by utilizing a Greenfield distribution Network, which is based on the realistic loading data. Agents and the controlling logic are codded in Matlab ® programming software. A sensitivity analysis is performed based on DG penetration to have the complete overview of the proposed methodology. The principle objective of the technique is to keep the voltage value within the standard limit of ±10% of the nominal, at all load nodes while instantly utilizing voltage control entities like DGs, Static VAR Compensator (SVCs) and On-Load Tap Changer (OLTC). In addition, the optimization of network losses and voltage level close to nominal is to be accomplished by the penalty function implementation

Serious Game Berbasis Taksonomi Bloom: Sebuah Pendekatan Alternatif Penilaian Pembelajaran Matematika Berbantuan Teknologi Informasi

Pada penelitian ini dikembangkan sebuah pendekatan baru dalam penilaian pembelajaran matematika berbantuan serious game dengan melibatkan komponen pengetahuan geometri bangun datar jajar genjang dan komponen pedagogi yakni taksonomi belajar menurut Bloom. Pendekatan penilaian ini diusulkan sebagai alternatif baru merekam data pembelajar yang representatif mewakili karakteristik individu mereka. Serious game yang diimplementasikan dikembangkan mengikuti kerangka teknis serious game yang konstruktivis. Tantangan di serious game didistribusikan ke dalam tiga level domain kognitif Bloom yang dimplementasikan di SD: kemampuan mengingat (C1), memahami (C2), dan menerapkan (C3). Serious game juga mengatur level kesukaran tantangan secara dinamis berdasarkan pengalaman pemain pada tantangan sebelumnya. Pengaturan level kesukaran secara dinamis ditujukan agar pemain tidak cepat frustrasi atau bosan dalam permainan. Serious game yang diimplementasikan dalam penilaian sudah melalui uji penerimaan dan uji tanggapan dari pengguna. Klasifikasi data permainan dilakukan melalui metode Bayes Net (BN), Naïve Bayes (NB), dan J48. Dalam melakukan klasifikasi, penerapan tiga metode digabungkan dengan dua opsi tes: crossvalidation dan percentage split. Klasifikasi di masing-masing perlakukan dikerjakan dalam sepuluh ulangan melibatkan sub data yang dipilih acak sebagai data pengujian. Hasil pengujian menunjukkan: (1) dari delapan skenario pengujian penerimaan pengguna diperoleh bahwa keseluruhan masukan skenario pengujian memberikan luaran yang sesuai harapan; (2) rata-rata skor respon pengguna yang dikumpulkan menggunakan kuesioner skala Likert dengan lima opsi terletak dalam interval kategori respon positif (59,93); (3) persentase kebenaran klasifikasi tertinggi yang dihasilkan pada klasifikasi data permainan adalah 88,5% yang dihasilkan melalui penerapan metode J48 yang digabungkan dengan opsi tes percentage split = 85%. Kategori agreement pada penerapan metode J48 dengan opsi tes percentage split adalah Baik (78%). Dari tiga bentuk pengujian disimpulkan bahwa selayaknya metode J48 dipilih dalam melakukan klasifikasi data permainan serta penilaian melibatkan serious game berbasis taksonomi Bloom dijadikan alternatif dalam penilaian pembelajaran materi geometri bangun datar untuk siswa SD kelas 5. ==================================================================================== We developed a new approach for mathematics' learning assessment applying a serious game which is called BoTySeGa. This approach was proposed as an alternative way for recording learners' data which are representative to understand the characteristic of learners. The game implemented in assessment involves the three aspects of a serious game: game, knowledge, and pedagogy. We involve the plane geometry of parallelogram for the 5th elementary students and Bloom's taxonomy successively as knowledge and pedagogy aspects of the game. The serious game was developed following the serious game constructivist framework for children’s learning. Inside the game; the challenges are distributed into the first three of Bloom's cognitive domain which are implemented in elementary school: remember, understand, and apply. The game system adjusts dynamically challenge's level of difficulty in consideration with players' experience on the previous challenge. This approach is designed to bring players far away of boredom and frustration. The serious game applied in the proposed assessment has been tested through user acceptance testing and players' respond to the usage of the game in assessment. Gameplay data are classified through three methods i.e.: Bayes Net, Naïve Bayes, and J48. Each method is conducted with two testing options: crossvalidation and percentage split. The classification in each treatment is done in ten times of repetition. Test results show that: (1) user acceptance testing involving 85 learners shows that BoTySeGa has fulfilled the learning assessment requirement, (2) the average score of players' response recorded utilizing five-points Likerttype of questionnaire is 59,93, it falls within "Positive" category, (3) the highest percentage of correctly classification of gameplay data is 88,5% which is calculated through classification applying method J48 with percentage split testing option 85%. Level agreement of classification is 0.78 which is Good. Based on testing results, we suggest the use of J48 method for the classification of gameplay data and support the implementation of mathematics’ learning assessment utilizing Bloom's taxonomy-based serious game as an alternative assessment in learning

Enhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making

The early-tardy distinct due date machine scheduling problem with job splitting

Master'sMASTER OF SCIENC