Earticle

바둑은 적어도 2,500년 이상의 역사를 지녔고 그동안 인간 고유의 게임 영역으로 여겨왔으나, 2016년 컴퓨터 바둑인 알파고에 의해 제압된 마지막 보드게임이 되었다. 바둑에서의 사활문제는 컴퓨터 바둑을 구축 시 반 드시 해결해야 되는 기본 문제 영역이 된다. 연역적 추론은 이미 알고 있는 판단을 근거로 새로운 사실을 추 론하는 논리학의 용어이다. 본 논문에서는 연역적 추론을 위해 제약 충족 방법을 활용하여 사활문제와 직결 되는 3궁, 4궁, 5궁, 6궁의 원형 안형을 표현하는 4-튜플의 형식을 찾고자 했다. 이후 생성된 4-튜플의 형식 을 갖고 점 패턴 매칭을 활용하여 각 궁도의 원형 안형의 갯수를 파악하고자 했다. 실험 결과에 따른 4-튜플 형식의 갯수는 3궁 1개, 4궁 3개, 5궁 4개, 6궁 8개가 있음을 알 수 있었다. 또한 각 궁도의 원형 안형의 갯 수는 3궁 2개, 4궁 5개, 5궁 12개, 6궁 35개가 존재함을 찾아냈다. 마지막으로 컴퓨터 바둑에서의 사활문제 해결을 위해 원형 안형들을 4-튜플 형식의 변형인 5-튜플 형식으로도 제시하였다.

Go has a history of at least 2,500 years and has been regarded as a unique game field for humans, but in 2016 it became the last board game to be overpowered by alphaGo, a computer Go. The life-and-death problem in Go becomes a fundamental problem domain that must be solved when implementing a computer Go. Deductive reasoning is a term in logic that infers new facts based on known judgements. In this paper, we tried to find the 4-tuple forms representing the prototypical eye shapes of 3-eyes, 4-eyes, 5-eyes, and 6-eyes, which are directly related to the life-and-death problems, by using the constraint satisfaction method for deductive reasoning. Afterwards, we tried to figure out the number of prototypical eye shapes of each eyes by using point pattern matching with the generated 4-tuple form. According to the experimental results, we can see that the 4-tuple forms have 1 for 3-eyes, 3 for 4-eyes, 4 for 5-eyes, and 8 for 6-eyes. We also found that the numbers of prototypical eye shapes for each eyes are 2 for 3-eyes, 5 for 4-eyes, 12 for 5-eyes, and 35 for 6-eyes. Lastly, for solving the life-and-death problem in computer Go, we also presented prototypical eye shapes as the 5-tuple forms which are variants of 4-tuple forms.

As the rapid growth of mobile device, a recommender system is required to provide adequate recommendation list even if the customers provide their needs explicitly. In this research, the problem that customers provide their needs is modeled as constraint satisfaction problem. However the existing constraint satisfaction is too rigid, so we employ approximate constraint satisfaction by adopting indifference interval. The proposed recommendation methodology is composed of two phases; the first phase is related to CF-based filtering to generate the candidate recommendation set. The second phase is related to approximate constraint filtering to find the final adequate items fitting individual customers’ concerns. We expect that the proposed methodology contribute to display the adequate items to customer’s concerns for better recommendation in mobile environment.

최근 세계적으로 4차 산업혁명의 도래와 더불어 인공지능 기술이 우리 생활에 밀접하게 접근하고 있다. 학교 교육현장에서는 소프 트웨어 교육에 대한 연구가 활발히 진행되고 있지만, 인공지능 교육과 관련하여 개발된 프로그램은 많지 않다. 이에 본 연구는 2015 개정 교육과정을 바탕으로 초등학교 학교 현장에서 적용할 수 있는 인공지능 교육 프로그램 개발을 진행하였다. 제약만족문제 (CSP)를 중심 주제로 정하였고, 총괄목표는 ‘제약만족문제 해결을 경험하고, 제약만족문제를 해결하기 위한 인공지능 알고리즘을 이해하여 실생활에 도움이 되는 문제해결력을 신장한다.’로 설정하였다. 개발된 프로그램은 ADDIE 모형을 기반으로 하여 진행하였 다. 전문가 10명을 대상으로 두 번의 전문가 타당도를 진행해 타당성을 확보하였고, 제약만족문제 기반의 인공지능교육 언플러그드 프로그램을 개발하였다. 개발된 인공지능 언플러그드 프로그램이 앞으로 학교현장에서 인공지능교육을 실현하기 위한 자료로 활용 되길 기대한다.

With the advent of the fourth industrial revolution worldwide recently, artificial intelligence technology is approaching our lives closely. While research on software education is being actively carried out at school education sites, there are not many programs developed in relation to artificial intelligence education. Based on the 2015 revised curriculum, this study developed an artificial intelligence education program that can be applied at elementary school sites. Constraint satisfaction problem (CSP) has been set as the main theme, and the overall goal is to 'experience solving Constraint satisfaction problem and understand artificial intelligence algorithms to solve constraint satisfaction problems, thereby enhancing problem-solving skills that are helpful in real life. The developed program was based on the ADDIE model. Two expert feasibility studies were conducted on 10 experts to secure validity and an artificial intelligence education unplugged program based on constraint satisfaction issues was developed. It is hoped that the developed artificial intelligence unplugged program will be used as data to realize artificial intelligence education at school sites in the future.

본 연구는 전시장에 방문하는 참관객의 특성에 따라 제약요인의 차이를 파악하고, 전 시장 방문 제약요인과 방문 만족의 관계를 살펴보고자 한 연구이다. 연구를 진행하기 위하여 KINTEX 전시장 주변 주민 및 전시장을 방문한 전시참관객을 대상으로 설문조 사를 실시하여 최종 405부 유효 표본을 수집하였다. 분석결과에 따라 여가의 제약 연구에서 세 가지 제약의 분류와 다르게 크게 개인적 제약과 전시장 및 전시회의 운영적인 제약으로 분류한 결과가 나타났다. 이는 전시장 방문 제약은 여가 제약과 다르게 대인적 제약에 영향을 덜 받는 결론을 내릴 수 있다. 연구결과를 통하여 참관객의 특성에 따라 느껴지는 전시장 방문 제약요인이 다른 결 과를 도출하였다. 이런 결과에 따라 전시 주최자는 자기의 표적참관객을 유치하기 위 하여 다른 특성을 가진 참관객에게 차별화된 서비스를 마련하여야 한다. 방문 제약요인이 방문만족에 주는 영향을 검증한 결과, 접근성 및 혼잡성은 전시장 시설 만족, 전시장 서비스 만족, 전시장 행사 만족에 부정적 영향을 미치는 것으로 나 타났으며, 정보 및 프로그램의 부족은 전시장 서비스 만족에 부정적 영향을 미치는 것 으로 나타났다. 본 연구의 시사점을 살펴보면, 전시장 방문 제약요인에 대한 고찰을 통하여 전시장 운영자 측면에서 전시장 건립단계에서부터 접근성 및 환경을 고려한 위치 선택이 매우 중요하고, 전시장을 운영하는 동안에도 전시장 내부의 혼잡 문제와 시설 문제 등을 잘 해결해야 한다. 콘텐츠의 종류와 콘텐츠의 내용은 동시에 큰 관심을 가지고 고려하여 야 하며, 관람객의 관점에서 전시회는 비즈니스 공간만이 아닌 엔터테인먼트를 위한 시간과 공간의 역할을 할 수 있어야 한다. 또한 전시장 운영자는 전시 주최자와 협력 하여 전시장과 전시회를 적극 홍보에 힘써야 한다. 이는 전시회의 지속적인 유치에도 도움이 되며, 전시장의 브랜딩 효과도 동시에 누릴 수 있을 것이다.

This study investigated the relationship between visitation constraints of exhibition center and visitation satisfaction according to the characteristics of visitors. We surveyed residents who lived nearby KINTEX and visitors of KINTEX. Based on the analysis, our study is different from the constraints that proposed in leisure studies as we only put forward two big constrains factors which are personal constraints and operational constraints. Visitation constraints are different depending on the characteristics of visitors. As a result, accessibility and congestion factor was found to have a negative effect on satisfaction of exhibition center facilities, satisfaction of exhibition center service, satisfaction of exhibition center events. The lack of information and programs factor appeared to negatively affect the satisfaction of the exhibition center service. Organizers should provide differentiated services to visitors in order to attract their target visitors. In addition, they should strive to eliminate visitation constraints.

This study is for demonstrating a causal relationship between class satisfaction through golf lesson in university and leisure perception and constraint intention of exercise. For this, 285 participants from the colleges and universities in Seoul and Kyonggi areas were sampled and analyzed by SPSS 18.0 and the results were following. First, environmental satisfaction that is one of the sub-factors of lesson satisfaction had a statically effect on self actualization and health maintenance that is one of the sub-factors of leisure recognition. Second, as analyzing the effect of lesson satisfaction on exercise adherence, instructional satisfaction that is one of the sub-factors of lesson satisfaction had positive(+) relationships with possibility that is one of the sub-factors of exercise adherence and positivity that is one of the antecedents of exercise adherence. Third, as analyzing the effect of leisure recognition on exercise adherence, the sub-factors of leisure recognition such as self actualization and health maintenance had a positive(+) relationship with all sub-factor of exercise adherence. These results were clear expectations that the mediator-like role for leisure recognition is important in case of high lesson satisfaction by participation. Also, through this study, it was possible to demonstrate the importance of exercise adherence in the process of leisure recognition.

본 연구는 축제와 관련된 기존의 연구를 검토하고, 축제 참여자의 제약요인과 협상노력간의 관계를 살펴보고, 제약협상 노력에 따른 축제 참여 관람객의 체험인식과 만족의 관계를 검증하였다. 앞서 설 명한 이론적 배경을 바탕으로 설정한 가설에 근거하여 실증적으로 검증하였으며, 연구결과를 요약하 면 다음과 같다. 첫 번째, 축제참여 제약과 축제참여에 대한 협상노력 측면의 경우 축제참여의 제약요인(내재적 제약, 대인적 제약, 구조적 제약)과 협상노력간의 관계에서 대인적 제약과 구조적 제약의 경우 축제참여 제 약협상 노력에 정(+)의 영향을 미치는 것으로 나타났으며, 내재적 제약 요인의 경우 기각되었다. 두 번째, 축제참여와 관람객의 축제체험인식과 만족의 연구결과를 살펴 보면 축제참여가 인지적 체 험, 감정적 체험 그리고 만족에 모두 정(+)의 영향을 미치고 있음을 확인할 수 있었다.

A study of the impact of constraint factors of festival participation and negotiation efforts of festival participant on festival satisfaction. This study reviewed earlier studies about festival and verified an impact of the leisure constraint factors of festival participant and the negotiation efforts on festival satisfaction by considering previous literature. Hypotheses based on literature reviews were analyzed emprically. According to the result of study, in aspect of the relationship between constraint factors of festival participation(intrapersonal constraint, interpersonal constraint, structural constraint) and negotiation efforts of festival participation, both interpersonal constraint and structural constraint have positive influence on constraint negotiation efforts. However, intrapersonal constraint factor influenced negatively. Also, the results revealed that in aspect of the relationship among festival participation, festival experience recognition, and satisfaction, festival participation have positive influence on cognitive and emotional experience and finally satisfaction.

Random constraint satisfaction problem models play a significant role in computer science. They provide instances for benchmarking algorithms, help inform the design of algorithms and heuristics, and provide insight into problem hardness. In this paper, model RA which was revised from classical model A, was studied. When parameter k satisfied k ≥ 3, model RA has the same satisfiability transition with model RB, which was revised from classical model B and has already got considerable theoretical and practical studies. We also compared the performances of fundamental algorithms on model RA with on model RB.

In the context of mass customization, to address the issues about method based on Constraint Satisfaction Problem (CSP) in the field of product configuration, a combined product configuration method based on the CSP and Bayesian Network (BN) is proposed in this paper. On the basis of CSP and BN theory, a product configuration model established based on CSP and BN, and the specific method for solving was given, Including the reasoning on posterior probability by establishing the Bayesian Network model and the reasoning on Constraint Satisfaction Problem. Finally, to assemble the computer as an example, a configuration system of assembly computer product is developed so as to verify the feasibility and effectiveness.

The Job Shop Scheduling Problem(JSSP) is concerned with schedule of m different machines and n jobs where each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. The purpose of this paper is to develop the efficient heuristic method for solving the minimum makespan problem of the large scale job shop scheduling. The proposed heuristic method is based on a Tabu Search(TS) and on a Constraint Satisfaction Technique(CST). In this paper, ILOG libraries is used to embody the job shop model, and a CST is developed for this model to generate the increased solution. Then, TS is employed to overcome the increased search time of CST on the increased problem size md to refine the next-current solution. Also, this paper presents the new way of finding neighbourhood solution using TS. On applying TS, a new way of finding neighbourhood solution is presented. Computational experiments on well known sets of MT and LA problem instances show that, in several cases, our approach yields better results than the other heuristic procedures discussed In literature.

Locomotive scheduling in railway systems experiences many difficulties because of the complex interrelations among resources, knowledge and various constraints. Artificial intelligence technology has been applied to solve these scheduling problems. These technologies have proved to be efficient in representing knowledge and rules for complex scheduling problems. In this paper, we have applied the CSP (Constraints Satisfaction Problems) programming technique, one of the AI techniques, to solve the problems associated with locomotive scheduling. This method is more effective at solving complex scheduling problems than available mathematical programming techniques. The advanced locomotive scheduling system using the CSP programming technique is realized based on the actual timetable of the Saemaul type train on the Kyong-bu line. In this paper, an overview of the CSP programming technique is described, the modeling of domain and constraints is represented and the experimental results are compared with the real-world existing schedule. It is verified that the scheduling results by CSP programming are superior to existing scheduling performed by human experts. The executing time for locomotive scheduling is remarkably reduced to within several decade seconds, something requiring several days in the case of locomotive scheduling by human experts.

According to the Projection Principle (Chomsky 1981), expletives have no semantic content and thus cannot occur in theta-marked positions. However, there seem to exist overt cases where the expletive it appears in the theta-marked object position. Many attempts (Case-based analysis by Authier (1991), Predication analysis by Rothstein (1995), Spec analysis by Stroik (1991, 1996)) have been made to account for such cases, with the common postulation of generating the expletive in the non-theta marked position and moving to the object position. Though such movement operations could account for general cases, they have not been successful in capturing the contrast that happens with respect to various properties including the optionality of the expletive it. This paper claims that such a contrast, in addition to the distribution possibilities of it in the object position, easily follows from a lexical and constraint-based analysis couched upon tight interactions among various grammatical components such as lexicon, argument structure, syntax and semantics.

A constraint satisfaction problem (CSP) is a general framework that can formalize various application problems in artificial intelligence. In this paper, we will focus on an important subclass of distributed partial CSP called the distributed maximal CSP that can be applied to more practical kinds of problems. Specifically, we propose a method of solving distributed mammal CSPs using a combination of approximate and exact algorithms that yields faster optimal solutions than otherwise possible using conventional methods. Experimental results are presented that demonstrate the effectiveness of the proposed new approach.

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

We have already presented the constraint satisfaction algorithm which could solve the losed loop problem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and efficiently. This algorithm is a hybrid type of using both declarative description (constraint represention) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

This paper presents solving the Sudoku puzzle as a constraint satisfaction problem (CSP). After introducing the rules and characteristics of the puzzle, we formulate the puzzle as a CSP and develop various methods of solving the problem. Blind search, minimum remaining value (MRV) heuristic, and some advanced methods are investigated, and their algorithms are implemented in this undergraduate project. The performance comparisons of these methods are discussed in the paper.

모든 제약만족 최적화 문제의 해를 구하는 일반화된 알고리즘을 구하는 것은 매우 어렵다. 그러나 결정 변수의 특성에 따라 세분화된 문제는 해를 위한 알고리즘을 구하기에 더 쉽다는 가정을 할 수 있다. 이와 같은 가정 하에 문제를 세분화 시키는 문제분류규칙을 제안하고 세분화된 문제의 특성에 맞는 백트래킹 알고리즘을 개발한다. 백트래킹을 이용한 깊이우선탐색에서 해를 빨리 찾기 위한 방법 중 하나는 탐색되는 노드의 순서를 효과적으로 배열하는 것이다. 정적 특성이 우세한 무선 센서 네트워크의 클러스터 헤드 위치문제와 동적 및 정적 특성의 혼합특성을 갖는 RFID 리더 간섭 최소화 문제를 선택하여 최적의 변수 순서화 알고리즘을 개발하고 기존의 방법과 비교하였다. 결과적으로 문제를 세분화시킴으로써 체계적인 탐색을 위한 백트래킹의 구조화를 실현하였다. 또한 개발된 백트래킹 알고리즘의 성능이 우수함을 보였다.

It is very hard to obtain a general algorithm for solution of all the constraint satisfaction optimization problems. However, if the whole problem is separated into subproblems by characteristics of decision variables, we can assume that an algorithm to obtain solutions of these subproblems is easier. Under the assumption, we propose a problem classifying rule which subdivide the whole problem, and develop backtracking algorithms fit for these subproblems. One of the methods of finding a quick solution is efficiently arrange for any order of the search tree nodes. We choose the cluster head positioning problem in wireless sensor networks in which static characteristics is dominant and interference minimization problem of RFID readers that has hybrid mixture of static and dynamic characteristics. For these problems, we develop optimal variable ordering algorithms, and compare with the conventional methods. As a result of classifying the problem into subproblems, we can realize a backtracking framework for systematic search. We also have shown that developed backtracking algorithms have good performance in their quality.

컨테이너 터미널의 수출 장치장은 수출될 컨테이너들이 지속적으로 반입되어 해당 선박에 선적되기까지 일시적으로 보관되는 장소이다. 장치장의 공간 활용도를 높이면서 선적 시 작업의 능률을 극대화하기 위해서는 여러 가지 제약조건과 장치 규칙에 따라 컨테이너들의 장치 위치를 결정해야 할 뿐 아니라, 소정의 기간을 대상으로 그 동안 반입 예정인 전 컨테이너들에 대한 적절한 공간할당 계획을 미리 수립해 두어야 한다. 본 논문에서는 수출 장치장 계획 문제를 제약조건만족 문제로 보고 이를 효과적으로 해결하기 위한 탐색 기법을 제시하고 있다 대규모의 탐색공간으로부터 효율적으로 해를 찾기 위해 dependency-directed backtracking 기법을 적용하였고, 탐색 중에 제약조건을 만족하는 해를 찾기 어렵다고 판단될 경우에는 일부 제약조건을 완화하여 해를 재 탐색하는 제약조건 완화 기법을 적용하였다. 실제 부산 신선대 컨테이너 터미널의 데이터를 이용한 실험 결과 만족할 만한 수준의 계획을 빠른 시간 내에 수립할 수 있음을 확인하였다

본 연구에서는 선박 건조 공정 중 블록조립공정을 다룬다 블록과 정반(bay)의 정보가 주어져 있을 때, 각 블록의 조립작업을 하게 될 정반과 조립작업을 시작하는 시작시점을 결정해야 한다. 정반에서 중요하게 고려할 두 가지 자원은 바로 면적과 시수이다. 본 연구에서 다루는 문제의 목적식은 정반에 할당피지 못하거나 작업시점을 결정하지 못해 작업을 할 수 없게 된 실패 블록의 수를 죄소화시키는 것과 시수 부하의 부하평준화를 이루는 것이다. 이 문제를 풀기 위해 제약만족기법(CSP)에 기반을 두는 알고리듬을 제안한다. 그리고 이 알고리듬은 실제 데이터를 가지고 실험을 하게 되며 그 결과와 기존의 일정계획 결과와 비교 분석하게 될 것이다.

This paper represents a relaxation matching algorithm based on global structure constraint satisfaction. Relaxation matching algorithm is a conventional approach to the matching problem. However, we confronted some problems such as null-matching and multi-matching problems by just using the relaxation matching technique. In order to solve the problems, in this paper, the matching problem is regarded as constraint satisfaction problem, and a relaxation matching algorithm is proposed based on global structure constraint satisfaction. The proposed algorithm is applied a landslide picture to show the effectiveness. When the algorithm is processed at landslide inspecting and monitoring system, motion parameters such as displacement area and its direction are computed. Once movement is recognized, displacements are estimated graphically with statistical amount in the image plane. Simulation has been done to prove the proposed algorithm by using time-sequence image of landslide inspection and monitoring system.