Automatic Problem Solving Research Articles

Development and use of the program of automatic problem solving when conducting practical classes in physics at the university

The creation of a virtual educational environment, consisting of an information space that ensures the availability of unlimited educational material through communication means, a virtual or real communication channel between a student and a teacher, increases the role of self-education, the dominance of learning over teaching. Paraphrasing [1], we can say that physics is owned not by the one who knows the formulas and definitions, but by the one who, with their help, can solve physical problems (PP). Similarly to the words of E. Fermi [2]: "A person knows physics if he/she can solve problems." The ability to solve PP contributes to the concretization of students' knowledge; without it, there is a separation of theoretical, lecture, educational material from the main task of any learning process – the practical application of accumulated knowledge and skills. Solving of PP contributes to the development of mental activity, the formation of creativity, intelligence, observation, independence and accuracy, is one of the forms of repetition, control and assessment of knowledge. At the same time, it is the solution of problems that is the most difficult element of physical education, causing methodological, didactic, psychological, and mathematical difficulties. It is known, that along with the traditional methods of solving PP: arithmetic, algebraic, geometric, graphic, experimental, since the beginning of the 2000s, information technology, computer technology, and programs - answer books have been actively used. The traditionally difficult issue of solving problems in physics requires both the improvement of classical methods and the development of new software tools for solving problems. The paper discusses the possibilities of using computers to solve various types of physical problems, the use of a site, created by the author, with considered examples of solving more than 2500 problems in physics, a developed program for automatic solution of problems in physics (APS - automatic physics solver) during practical exercises.

Abstraction for non-ground answer set programs

Abstraction is an important technique utilized by humans in model building and problem solving, in order to figure out key elements and relevant details of a world of interest. This naturally has led to investigations of using abstraction in AI and Computer Science to simplify problems, especially in the design of intelligent agents and automated problem solving. By omitting details, scenarios are reduced to ones that are easier to deal with and to understand, where further details are added back only when they matter. Despite the fact that abstraction is a powerful technique, it has not been considered much in the context of nonmonotonic knowledge representation and reasoning, and specifically not in Answer Set Programming (ASP), apart from some related simplification methods. In this work, we introduce a notion for abstracting from the domain of an ASP program such that the domain size shrinks while the set of answer sets (i.e., models) of the program is over-approximated. To achieve the latter, the program is transformed into an abstract program over the abstract domain while preserving the structure of the rules. We show in elaboration how this can be also achieved for single or multiple sub-domains (sorts) of a domain, and in case of structured domains like grid environments in which structure should be preserved. Furthermore, we introduce an abstraction-&-refinement methodology that makes it possible to start with an initial abstraction and to achieve automatically an abstraction with an associated abstract answer set that matches an answer set of the original program, provided that the program is satisfiable. Experiments based on prototypical implementations reveal the potential of the approach for problem analysis, by its ability to focus on the parts of the program that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details. This makes domain abstraction an interesting topic of research whose further use in important areas like Explainable AI remains to be explored.

Preface

It is our great pleasure to welcome you to the 2021 5th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2021) on February 26-28, 2021, which is planned to held in Zhuhai, China. However, due to the spread of COVID-19, many countries have strict restrictions to travel aboard. Meanwhile, the safety of the participants is always the priority. We made a hard decision to hold the conference online.IWAACE 2021 is to bring together innovative academics and industrial experts in the field of Advanced Algorithms and Control Engineering to a common forum. The primary goal of the conference is to promote research and developmental activities in Advanced Algorithms and Control Engineering and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Algorithms and Control Engineering and related areas.This scientific event brings together more than 120 national and international researchers. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.In the second part, we invited four professors as our keynote speakers. Prof. Xinguo Yu, the dean and Professor of CCNU Wollongong Joint Institute and Professor of National Engineering Research Center for E-Learning Adjunct professor of University of Wollongong, Australia, performed a speech: Automatic Problem Solving for Basic Education. And then we had Assoc. Prof. Xiang Zhou from School of Data Science, City University of Hong Kong, China. He presented a talk: Machine Learning and Control Theory. He introduced the recent ongoing joint work with Alain Bensoussan and Phillip Yam about a new numerical method for computing the gradient of the value function. Dr. Deepak Kumar Jain, School of Automation, Chongqing University of Posts and Telecomunications, Chongqing, China as our third keynote speakers. Our finale keynote speakers, Dr. Badrul Hisham bin Ahmad, Universiti Teknikal Malaysia Melaka / Faculty of Electronics and Computer Engineering, Malaysia was invited to present his talk Design and Development of VHF FRONT-END for lightning interferometer system. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Communication Technology and Applications, Computer Engineering, Network Engineering and Application Technology, Intelligent System, Electronics and Electrical Engineering and more related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to acknowledge all of those who supported IWAACE 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers.The Committee of IWAACE 2021List of Committee member is available in this pdf.

Preface

The 2020 4th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2020) was held on February 21-23, 2020. Since 2017, this annual international conference has been held for three times, and this time, the fourth conference was held via online platform due to the COVID-19 crisis, which is different from the previous traditional way. Despite the distance, online IWAACE 2020 enables experts and scholars in the field of Advanced Algorithms and Control Engineering to continue to communicate and discuss the state-of-the-art research with each other. It provides a flexible way for scholars and practitioners to enhance academic exchange and cooperation.We were honored to have Dr. Yuanzhu Chen, Head of Department of Computer Science, Memorial University of Newfoundland, Canada, to chair IWAACE 2020. Our Technical Program Committee constitutes more than 40 experts in the field of Advanced Algorithms and Control Engineering from home and abroad. During the conference, we were pleased to invite three distinguished experts to present their insightful speeches. Prof. Xinguo Yu from University of Wollongong, Australia, shared his study on Automatic Problem Solving for Basic Education. Assoc. Prof. Xiang Zhou from City University of Hong Kong, China, held a speech on the topic of Machine Learning and Control Theory. Dr. Badrul Hisham bin Ahmad from Universiti Teknikal Malaysia Melaka, Malaysia, talked about Design and Development of VHF FRONT-END for lightning interferometer system.List of More details of the virtual conference format, Committee members are available in this pdf.

Leveraging inconsistency management in the multi‐view collaborative modelling of cyber‐physical production systems

Cyber-physical production systems (CPPS) are interconnected systems producing customised products. Such a system appeals for flexibility and closer cross-disciplinary collaboration, which might introduce inconsistencies among models and lead to system failures. Detecting inconsistencies needs expert knowledge and is usually performed in an ad-hoc way. Knowledge base, a technology which stores knowledge formally for automatic problem-solving, can efficiently assist engineers in detecting inconsistencies across engineering models and between the models and their physical realisations. The approach is evaluated with a bench-scale CPPS.

Industrial Applications of Answer Set Programming

Automated problem solving in combination with declarative specifications of search-problems have shown to substantially improve the implementation and maintenance costs as well as the man-machine interaction of deployed industrial applications. The knowledge representation and reasoning (KRR) framework of answer set programming (ASP) offers a rich representation language and high performance solvers. Therefore, ASP has become very attractive for the representation and solving of search-problems both for academia and industry. This article focuses on the latest industrial applications of ASP. We do not only present successful applications of ASP but also describe the development process and the design of ASP programs in an industrial context. Finally, we discuss current approaches to tackle the most significant application challenges such as grounding and runtime improvements by heuristics.

Simple arithmetic: not so simple for highly math anxious individuals.

Fluency with simple arithmetic, typically achieved in early elementary school, is thought to be one of the building blocks of mathematical competence. Behavioral studies with adults indicate that math anxiety (feelings of tension or apprehension about math) is associated with poor performance on cognitively demanding math problems. However, it remains unclear whether there are fundamental differences in how high and low math anxious individuals approach overlearned simple arithmetic problems that are less reliant on cognitive control. The current study used functional magnetic resonance imaging to examine the neural correlates of simple arithmetic performance across high and low math anxious individuals. We implemented a partial least squares analysis, a data-driven, multivariate analysis method to measure distributed patterns of whole-brain activity associated with performance. Despite overall high simple arithmetic performance across high and low math anxious individuals, performance was differentially dependent on the fronto-parietal attentional network as a function of math anxiety. Specifically, low—compared to high—math anxious individuals perform better when they activate this network less—a potential indication of more automatic problem-solving. These findings suggest that low and high math anxious individuals approach even the most fundamental math problems differently.

FERTILIZERS - INFORMATION TECHNOLOGY OPTIMUM PRODUCTION CONTROL

This article describes the basic principles of information technology, management of production of mineral fertilizers, which will combine the following hardware, software and organizational and methodological tools: mathematical models of the installation process research, process optimization, security system and automatic control system. On the basis of the proposed information technology optimal control decided the new scientific and applied problems, namely increased efficiency of processes of granular fertilizers with the requirements of energy efficiency, ensure the specified quality of the finished product and increased uptime of equipment. A new approach - the ideology of automated problem solving, considering information technology as part of manufacturing of fertilizers, as a set of class methods, which in contrast to the ACS, flexible use of various kinds of components and software, depending on many substantive problems to be solved .

Structural modeling of the production quality as a multidimensional object of measurement and control

The structural-analytical models of product quality as a multidimensional process of evaluation, measurement and control are developed. The product quality is represented as a multi-factor, multi-criteria and multi-parameter estimation object. This structural formalization of quality demonstrates the multidimensional qualities: comprehensiveness due to a set of environmental factors; multicriteriality due collectively evaluated quality criteria; multiparameter information models that describe the relationship between the factors and evaluated criteria. The developed models allow us to establish the relationship between the structural elements of the formation of the product quality.The advantages of neural network modeling to quantify and data quality assurance are proved. Using the level of formality of quality control processes based on advanced intelligent technologies allows creating a computational and experimental base of automated problem solving of information quality and reducing the cost of development, testing and manufacture of product.

Application of The Agent-oriented Analysis to A Software System in Problem Cause Identification using Fuzzy Decision Tree

A problem cause identification software system is usually very complex and may contain multiroles dealing processes such as R&D, customer and manufacturing. The causes of a problem may have cognitive uncertainty properties, which need to be analyzed for the purpose of algorithm-based automatic problem-solving. In this study, an application of the scenario-driven agent-oriented analysis to a software system in problem cause identification is proposed. A case study is conducted to demonstrate the problem-solving algorithm for problem cause identification of a notebook computer product. The major contributions of this research include: (1) The proposal of an autonomous problem-solving system base on a scenario-driven agent-oriented approach. (2) The proposal of a complex system of problem cause identification based on the fuzzy data.

Towards the Design of Heuristics by Means of Self-Assembly

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

In a scientific domain, learning comprises studying a finite set of principles of the domain and applying them to solve a wide variety of problems. Therefore an intelligent tutoring system in a scientific domain is required to possess an adequate methodology to deal with this principle. We suggest a tutoring architecture for geometry where the domain principles are automatically converted to inference operators for use by the domain-independent, inferential portion of the tutoring system. An important part of this architecture is an intelligent drawing interface that facilitates automatic conversion of the figures in geometry into an internal form that is suitable for problemsolving tutoring. During student problem solving, the system monitors the student’s steps, tracks a step that has multiple inferences, and gives hints and explanations. We discuss the advantages of our approach in enhancing the performance and interactivity of the tutoring system.

A visual programming system for automated problem solving

Although new AI planning algorithms and techniques are being developed and improved rapidly, there is a lack of efficient and easy to use systems able to incorporate and utilize them. Furthermore, while visual representation facilitates design, maintenance and comprehension of planning domains and problems, very few systems incorporate it. This paper presents VLEPPO, an integrated system aiming at visually modeling planning domains and problems through a convenient graphical interface, while maintaining compatibility with the Planning Domain Definition Language (PDDL), with import and export features. Solutions to planning problems can be obtained by invoking different planners employing the web services technology. The demonstration of the system is performed through a case study involving web service composition viewed as a planning problem.

PASER: a curricula synthesis system based on automated problem solving

This paper presents PASER, a system for automatically synthesising curricula using AI Planning and Machine Learning techniques based on an ontology of educational resources metadata. Given the initial state of the problem (learner's profile, preferences, needs and abilities), the available actions (study an educational resource, take an exam, join an e-learning course, etc.) and the goals (obtain a certificate, learn a subject, acquire a skill, etc.), the planning module of PASER constructs a complete educational curriculum that achieves the goals. The Machine Learning module of PASER matches textually described learning requests, objectives and prerequisites to concepts of the ontology.

Principles and applications of continual computation

Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, effort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We examine continual computation, reasoning policies that capture a broader conception of problem by considering the proactive allocation of computational resources to potential future challenges. We explore policies for allocating idle time for several settings and present applications that highlight opportunities for harnessing continual computation in real-world tasks.

Diagrammatic Reasoning: An Artificial IntelligencePerspective

A common motivation for developing computational frameworks for diagrammatic reasoning is the hope that they might serve as re-configurable tools for studying human problem solving performance. Despite the ongoing debate as to the precise mechanisms by which diagrams, or any other external representation, are used in human problem solving, there is little doubt that diagrammatic representations considerably help humans solve certain classes of problems. In fact, there are a host of applications of diagrams and diagrammatic representations in computing, from data presentation to visual programming languages. In contrast to both the use of diagrams in human problem solving and the ubiquitous use of diagrams in the computing industry, the topic of this review is the use of diagrammatic representations in automated problem solving. We therefore investigate the common, and often implicit, assumption that if diagrams are so useful for human problem solving and are so apparent in human endeavour, then there must be analogous computational devices of similar utility.

Design systems and paradigms

This paper looks first at three well-known texts that describe design systems, focusing on their treatment of design strategy. Inadequacies in the treatments are uncovered. The rational problem-solving paradigm of design is examined in the light of some recent work in automatic problem-solving. The paradigm is developed so as to deal with the inadequacies in the treatment of strategy. Consideration of these developments indicates a convergence with the reflection in action paradigm.

Automatic versus reflective social problem solving in relation to children's sociometric status.

Children's automatic and reflective social problem-solving skills were compared by requiring them to generate solutions to hypothetical social problems immediately after hearing them or after being required to wait 20 s before answering. The implications of the findings for aggressive and rejected children's social problem-solving deficiencies are discussed

Automatic versus reflective social problem solving in relation to children's sociometric status.

Automatic versus reflective social problem solving in relation to children's sociometric status.