Artificial intelligence(AI)and robotics have gone through three generations of development,from Turing test,logic theory machine,to expert system and self-driving car.In the third-generation today,AI and robotics have...Artificial intelligence(AI)and robotics have gone through three generations of development,from Turing test,logic theory machine,to expert system and self-driving car.In the third-generation today,AI and robotics have collaboratively been used in many areas in our society,including industry,business,manufacture,research,and education.There are many challenging problems in developing AI and robotics applications.We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies.In this inaugural issue,we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms.We present examples of such development environments created by both industry and academia.We also selected eight papers in the related areas to celebrate the foundation of this journal.展开更多
This study presents the authors＇ recent research and application of a new visual programming language and its development environment： VIPLE （Visual IoT/Robotics Programming Language Environment） at Arizona State ...This study presents the authors＇ recent research and application of a new visual programming language and its development environment： VIPLE （Visual IoT/Robotics Programming Language Environment） at Arizona State University （ASU）. ASU VIPLE supports a variety of loT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100： Introduction to Engineering and in CSE 446： Software Integration and Engineering, as well as in several other universities worldwide.展开更多
Teaching students the concepts behind computational thinking is a difficult task,often gated by the inherent difficulty of programming languages.In the classroom,teaching assistants may be required to interact with st...Teaching students the concepts behind computational thinking is a difficult task,often gated by the inherent difficulty of programming languages.In the classroom,teaching assistants may be required to interact with students to help them learn the material.Time spent in grading and offering feedback on assignments removes from this time to help students directly.As such,we offer a framework for developing an explainable artificial intelligence that performs automated analysis of student code while offering feedback and partial credit.The creation of this system is dependent on three core components.Those components are a knowledge base,a set of conditions to be analyzed,and a formal set of inference rules.In this paper,we develop such a system for our own language by employing π-calculus and Hoare logic.Our detailed system can also perform self-learning of rules.Given solution files,the system is able to extract the important aspects of the program and develop feedback that explicitly details the errors students make when they veer away from these aspects.The level of detail and expected precision can be easily modified through parameter tuning and variety in sample solutions.展开更多
The purpose of this research is to create a simulated environment for teaching algorithms,big data processing,and machine learning.The environment is similar to Google Maps,with the capacity of finding the fastest pat...The purpose of this research is to create a simulated environment for teaching algorithms,big data processing,and machine learning.The environment is similar to Google Maps,with the capacity of finding the fastest path between two points in dynamic traffic situations.However,the system is significantly simplified for educational purposes.Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic.The environments used in the project are Visual IoT/Robotics Programming Language Environment(VIPLE)and a traffic simulator developed in the Unity game engine.This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE.The traffic data are generated from the recorded real traffic data published on the Arizona Maricopa County website.Based on the generated traffic data,VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.展开更多
College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intellig...College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.展开更多
Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia.Several physical quantum computers are now freely available to use through clo...Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia.Several physical quantum computers are now freely available to use through cloud services,with some implementations supporting upwards of hundreds of qubits.These advances mark the beginning of the noisy intermediate-scale quantum(NISQ)era of quantum computing,paving the way for hybrid quantum-classical(HQC)systems.This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to HQC machine learning.展开更多
文摘Artificial intelligence(AI)and robotics have gone through three generations of development,from Turing test,logic theory machine,to expert system and self-driving car.In the third-generation today,AI and robotics have collaboratively been used in many areas in our society,including industry,business,manufacture,research,and education.There are many challenging problems in developing AI and robotics applications.We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies.In this inaugural issue,we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms.We present examples of such development environments created by both industry and academia.We also selected eight papers in the related areas to celebrate the foundation of this journal.
文摘This study presents the authors＇ recent research and application of a new visual programming language and its development environment： VIPLE （Visual IoT/Robotics Programming Language Environment） at Arizona State University （ASU）. ASU VIPLE supports a variety of loT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100： Introduction to Engineering and in CSE 446： Software Integration and Engineering, as well as in several other universities worldwide.
基金supported by general funding at IoT and Robotics Education Lab and FURI program at Arizona State University.
文摘Teaching students the concepts behind computational thinking is a difficult task,often gated by the inherent difficulty of programming languages.In the classroom,teaching assistants may be required to interact with students to help them learn the material.Time spent in grading and offering feedback on assignments removes from this time to help students directly.As such,we offer a framework for developing an explainable artificial intelligence that performs automated analysis of student code while offering feedback and partial credit.The creation of this system is dependent on three core components.Those components are a knowledge base,a set of conditions to be analyzed,and a formal set of inference rules.In this paper,we develop such a system for our own language by employing π-calculus and Hoare logic.Our detailed system can also perform self-learning of rules.Given solution files,the system is able to extract the important aspects of the program and develop feedback that explicitly details the errors students make when they veer away from these aspects.The level of detail and expected precision can be easily modified through parameter tuning and variety in sample solutions.
文摘The purpose of this research is to create a simulated environment for teaching algorithms,big data processing,and machine learning.The environment is similar to Google Maps,with the capacity of finding the fastest path between two points in dynamic traffic situations.However,the system is significantly simplified for educational purposes.Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic.The environments used in the project are Visual IoT/Robotics Programming Language Environment(VIPLE)and a traffic simulator developed in the Unity game engine.This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE.The traffic data are generated from the recorded real traffic data published on the Arizona Maricopa County website.Based on the generated traffic data,VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.
基金The authors wish to thank their colleagues and students who were involved in this study and provided valuable implementation and technical support.The research is partly supported by general funding at IoT and Robotics Education Lab and FURI program at Arizona State University and is partly supported by China Scholarship Council,Guangdong Science and Technology Department,under Grant Number 2016A010101020,2016A010101021,and 2016A010101022Guangzhou Science and Information Bureau under Grant Number 201802010033.
文摘College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.
基金The research is supported by Arizona State University faculty funding.
文摘Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia.Several physical quantum computers are now freely available to use through cloud services,with some implementations supporting upwards of hundreds of qubits.These advances mark the beginning of the noisy intermediate-scale quantum(NISQ)era of quantum computing,paving the way for hybrid quantum-classical(HQC)systems.This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to HQC machine learning.
