ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Exploiting the Hierarchical Structure of Rule-Based Specifications for Decision Planning

Rule-based specifications have been very successful as a declarative approach in many domains, due to the handy yet solid foundations offered by rule-based machineries like term and graph rewriting. Realistic problems, however, call for suitable techniques to guarantee scalability. For instance, many domains exhibit a hierarchical structure that can be exploited conveniently. This is particularly evident for composition associations of models. We propose an explicit representation of such structured models and a methodology that exploits it for the description and analysis of model- and rule-based systems. The approach is presented in the framework of rewriting logic and its efficient implementation in the rewrite engine Maude and is illustrated with a case study.

A Study on the Automatic Ship Control Based on Adaptive Neural Networks

Recently, dynamic models of marine ships are often required to design advanced control systems. In practice, the dynamics of marine ships are highly nonlinear and are affected by highly nonlinear, uncertain external disturbances. This results in parametric and structural uncertainties in the dynamic model, and requires the need for advanced robust control techniques. There are two fundamental control approaches to consider the uncertainty in the dynamic model: robust control and adaptive control. The robust control approach consists of designing a controller with a fixed structure that yields an acceptable performance over the full range of process variations. On the other hand, the adaptive control approach is to design a controller that can adapt itself to the process uncertainties in such a way that adequate control performance is guaranteed. In adaptive control, one of the common assumptions is that the dynamic model is linearly parameterizable with a fixed dynamic structure. Based on this assumption, unknown or slowly varying parameters are found adaptively. However, structural uncertainty is not considered in the existing control techniques. To cope with the nonlinear and uncertain natures of the controlled ships, an adaptive neural network (NN) control technique is developed in this thesis. The developed neural network controller (NNC) is based on the adaptive neural network by adaptive interaction (ANNAI). To enhance the adaptability of the NNC, an algorithm for automatic selection of its parameters at every control cycle is introduced. The proposed ANNAI controller is then modified and applied to some ship control problems. Firstly, an ANNAI-based heading control system for ship is proposed. The performance of the ANNAI-based heading control system in course-keeping and turning control is simulated on a mathematical ship model using computer. For comparison, a NN heading control system using conventional backpropagation (BP) training methods is also designed and simulated in similar situations. The improvements of ANNAI-based heading control system compared to the conventional BP one are discussed. Secondly, an adaptive ANNAI-based track control system for ship is developed by upgrading the proposed ANNAI controller and combining with Line-of-Sight (LOS) guidance algorithm. The off-track distance from ship position to the intended track is included in learning process of the ANNAI controller. This modification results in an adaptive NN track control system which can adapt with the unpredictable change of external disturbances. The performance of the ANNAI-based track control system is then demonstrated by computer simulations under the influence of external disturbances. Thirdly, another application of the ANNAI controller is presented. The ANNAI controller is modified to control ship heading and speed in low-speed maneuvering of ship. Being combined with a proposed berthing guidance algorithm, the ANNAI controller becomes an automatic berthing control system. The computer simulations using model of a container ship are carried out and shows good performance. Lastly, a hybrid neural adaptive controller which is independent of the exact mathematical model of ship is designed for dynamic positioning (DP) control. The ANNAI controllers are used in parallel with a conventional proportional-derivative (PD) controller to adaptively compensate for the environmental effects and minimize positioning as well as tracking error. The control law is simulated on a multi-purpose supply ship. The results are found to be encouraging and show the potential advantages of the neural-control scheme.1. Introduction = 1 1.1 Background and Motivations = 1 1.1.1 The History of Automatic Ship Control = 1 1.1.2 The Intelligent Control Systems = 2 1.2 Objectives and Summaries = 6 1.3 Original Distributions and Major Achievements = 7 1.4 Thesis Organization = 8 2. Adaptive Neural Network by Adaptive Interaction = 9 2.1 Introduction = 9 2.2 Adaptive Neural Network by Adaptive Interaction = 11 2.2.1 Direct Neural Network Control Applications = 11 2.2.2 Description of the ANNAI Controller = 13 2.3 Training Method of the ANNAI Controller = 17 2.3.1 Intensive BP Training = 17 2.3.2 Moderate BP Training = 17 2.3.3 Training Method of the ANNAI Controller = 18 3. ANNAI-based Heading Control System = 21 3.1 Introduction = 21 3.2 Heading Control System = 22 3.3 Simulation Results = 26 3.3.1 Fixed Values of n and = 28 3.3.2 With adaptation of n and r = 33 3.4 Conclusion = 39 4. ANNAI-based Track Control System = 41 4.1 Introduction = 41 4.2 Track Control System = 42 4.3 Simulation Results = 48 4.3.1 Modules for Guidance using MATLAB = 48 4.3.2 M-Maps Toolbox for MATLAB = 49 4.3.3 Ship Model = 50 4.3.4 External Disturbances and Noise = 50 4.3.5 Simulation Results = 51 4.4 Conclusion = 55 5. ANNAI-based Berthing Control System = 57 5.1 Introduction = 57 5.2 Berthing Control System = 58 5.2.1 Control of Ship Heading = 59 5.2.2 Control of Ship Speed = 61 5.2.3 Berthing Guidance Algorithm = 63 5.3 Simulation Results = 66 5.3.1 Simulation Setup = 66 5.3.2 Simulation Results and Discussions = 67 5.4 Conclusion = 79 6. ANNAI-based Dynamic Positioning System = 80 6.1 Introduction = 80 6.2 Dynamic Positioning System = 81 6.2.1 Station-keeping Control = 82 6.2.2 Low-speed Maneuvering Control = 86 6.3 Simulation Results = 88 6.3.1 Station-keeping = 89 6.3.2 Low-speed Maneuvering = 92 6.4 Conclusion = 98 7. Conclusions and Recommendations = 100 7.1 Conclusion = 100 7.1.1 ANNAI Controller = 100 7.1.2 Heading Control System = 101 7.1.3 Track Control System = 101 7.1.4 Berthing Control System = 102 7.1.5 Dynamic Positioning System = 102 7.2 Recommendations for Future Research = 103 References = 104 Appendixes A = 112 Appendixes B = 11

A Study on Development of Expert System for Collision Avoidance and Navigation Based on AIS

오늘날 무역량의 급속한 증가로 세계 주요 항로에서의 해상 교통량은 폭주하고 있다. 더욱이 선박은 대형화와 함께 고속화 되고 있으며 또한 전용화가 이루어 지고 있다. 이런 환경으로 해상에서의 선박 충돌 사고 계속 발생하고 있어 이런 충돌로 인하여 인명 및 재산에 큰 손해를 발생할 뿐만 아니라 심각한 해상 오염을 발생하기도 한다. 한편, 높은 수준의 경제 성장에 따라 사람들은 승선 근무를 기피하게 되어 항해자의 직무 능력은 과거에 비하여 떨어져 있는 편이다. 그런데도 불구하고 항해 중의 의사 결정은 전적으로 책임 항해사의 경험과 지식에 의존하고 있다. 항해사 혹은 선장이 취한 의사 결정은 자신의 선박과 주위의 선박의 운명을 결정하게 된다. 그러나 경험이 많은 선장 및 항해사의 수는 선박 척수보다는 훨씬 적다. 신규의 항해사들은 짧은 시간에 그런 값진 경험들을 습득할 수가 없다. 이런 경험을 적절하게 이용하여 해상에서의 충돌을 효과적으로 줄이기 위하여 이 논문에서는 충돌 회피 및 항해 전문가 시스템(expert system for collision avoidance and navigation, ESCAN)을 제안한다. 신규 항해사들의 낮은 능력을 보완하기 위한 하나의 방법으로 ESCAN 은 충돌 회피에 관한 합리적인 권고를 항해사들에게 제시하여 현재의 교통 상황을 더 이해하게 하고 충돌의 위험이 발생할 때 충돌 회피에 관한 합리적 의사 결정을 하게 한다. 레이더/ARPA 와 같은 장비는 충돌 회피에 관한 단순한 기능을 제공하여 이들 장비에서 나타난 정보는 짧은 시간에 충돌 회피의 의사 결정을 하는데 효과적이지 못하여 충돌 회피에 관한 정보 및 지시 등이 더 필요하게 한다. 한편 AIS 기술 활용하여 이 논문에서 개발한 ESCAN 은 본선 주위에 있는 상대 선박에 관한 보다 유용한 항해 정보를 받을 수 있어 현재의 상황을 처리하는데 보다 나은 권고나 제안을 할 수 있다. 이 논문에서 얻은 결론은 다음과 같다. 먼저 해상충돌방지규칙(COLREGS)와 충돌회피과정, 그와 관련된 내용을 검토하였으며 그 내용은 다음과 같다. (1) 해상에서의 충돌을 예방하고 선박의 안전 항해를 확보하기 위해서 COLREGS 를 정확하게 이해하고 엄격하게 따라야 한다. (2) 안전 속력은 효과적인 충돌 회피 동작을 결정하고 취하는데 충분한 시간을 확보하는 1 차적인 요소이다. 항해 중 그 상황에 맞는 속력을 적절하게 유지하여야 한다. (3) 항해 중 안전한 통과 거리를 확보하여야 하는데 대양 항해에서는 통상 2 마일로 간주한다. (4) 양 선박이 조우할 때 과정은 충돌 회피 동작의 효과가 없는 단계, 충돌의 위험성이 있는 단계, 극한 상황에 있는 단계, 충돌 위험(거의 충돌하는) 단계 등으로 나눌 수 있다. (5) 통상 항해사들은 충돌의 위험이 제일 큰 선박을 결정하는데 충돌위험도 값을 사용한다. ESCAN 에서는 공식 (2-2)를 이용하여 충돌위험도를 평가한다. (6) 본선이 여러 선박과 조우할 때 ESCAN 은 본선과 상대 선박과의 조우 상황을 분석하여 각 선박의 가능한 움직임을 예측한다. 또 어떤 선박을 제일 먼저 피할 것인지 정하고 각각의 선박에 대하여 안전한 충돌 회피 동작 및 시간을 결정한다. 한편 항해사는 현재 상황에 대한 안전 통과 거리를 고려하여 ESCAN 에서 제공한 안전 충돌 회피 영역(방위, 속력)이 적절한지를 확인한다. 이런 방법을 이용하여 현재의 다수의 선박의 조우 상황에 대하여 적절한 의사 결정을 할 수 있다. 두 번째로 ESCAN 을 설계하고 개발하였는데 그 결과는 다음과 같다. (10) ESCAN 은 전문가 시스템의 이론과 기술을 이용하여 설계하고 개발하였으며 AIS, 레이더/ARPA 정보를 기반으로 하였다. (11) ESCAN 은 항해 장비의 데이터를 보존하는 데이터베이스(Facts/Data Base), ESCAN 의 프로덕션 룰을 저장하는 지식베이스(Knowledge Base), 데이터에 알맞은 규칙을 결정하는 추론기구(Inference Engine), 사용자와 ESCAN 과의 통신을 위한 사용자-시스템 인터페이스(User-System Interface) 등으로 4 가지로 구성되어 있다. (12) ESCAN에서는 AIS 기술을 사용한다. AIS는 본선이 본선 주위에 있는 상대 선박에 관한 상세한 항해 정보를 제공한다. 그러므로 이를 이용하면 의사 결정을 보다 합리적으로 할 수 있다 (13) ESCAN 에 사용된 항해 지식은 COLREGS 및 항해 전문가의 지식을 기반으로 한 것이다. (14) ESCAN 의 지식 베이스는 모듈 구조로 되어 있으며 그 내용은 기본 항해 규칙 모듈, 조종 평가 모듈, 조우 단계 구별 모듈, 조우 상태 판단 모듈, 추가 충돌 회피 지식 모듈, 항해 경험 및 다수의 선박의 회피 모듈 등의 6 개의 모듈이다. (15) 프로덕션 룰을 ESCAN 에서 충돌 회피에 관한 지식을 표현하는데 사용하였다. 그 이유는 프로덕션 룰의 구조가 이런 지식을 완전하게 표현할 수 있고 또 CLIPS 언어로 잘 지원될 수 있기 때문이다. (16) ESCAN 에 사용된 충돌 회피에 관한 새로운 추론 과정은 그림 3-8 과 같다. (17) ESCAN 은 전향추론과 후향추론을 혼합한 형태를 사용하였다. (18) CLIPS 는 레터 패턴 매칭 알고리즘을 사용하므로 ESCAN 의 반응 속도는 상당히 향상되었다. 마지막으로 ESCAN 을 실험하여 다음과 같은 결과를 얻었다. (1) ESCAN 의 추론 부분은 CLIPS 로 프로그램 되어 있지만 나머지 부분은 비쥬얼 C++로 되어 있다. (2) ESCAN 은 본선과 상대 선박이 조우하는 여러 가지 상황에 대하여 실시간으로 분석하고 판단하는 기능을 가지고 있으며 항해사들에게 적절한 충돌 회피 계획, 충고, 혹은 권고 등을 제공한다. (3) 또 ESCAN 은 사용자가 충돌 회피 동작을 시뮬레이션 할 수 있는 기능을 가지고 있다. (4) 이 연구에서 제시한 몇 가지 예를 따르면 ESCAN 은 COLREGS 규칙을 따르고 있으며 아울러 항해 전문가의 조언을 따르고 있다. (5) 장차 규칙을 추가하고자 할 때 추가 업그레이드가 가능하다. 이것은 전 시스템을 고치는 것이 아니라 지식 베이스에 사용된 규칙만을 다시 쓰면 되기 때문이다. (6) 다수 선박의 조우 상황에서는 모든 조건을 만족하는 현재의 상황을 처리할 수 있는 표준 케이스를 제공하고 있다. ESCAN 의 개발은 항해사가 합리적인 판단을 하는데 도움을 주어 안전항해를 하게 할 뿐만 아니라 항해 장비를 통합하여 무인 항해가 가능한 통합자동항법시스템의 개발까지 연계될 수 있다. 앞으로 다른 항법시스템과 통하여 사용자에게 편리한 시스템을 구성하는 연구가 남아 있으며, 또 실선에서의 실험을 통하여 ESCAN 을 보완하는 연구가 남아 있다.Nowadays, highly increasing global trade has caused heavy traffic in the main sea routes. Moreover, ships are getting larger and larger in size, faster in speed and highly specialized. Under these circumstances, serious collision accidents between ships happened at sea over and over again, and led to not only huge loss of life and property but also serious damage to marine environment. Meanwhile, due to the high level of economic growth, more and more people tend to choose their jobs in land rather than them aboard ships. Therefore, their competence as navigation officers becomes worse now than in the past. Even so decision-making during navigation entirely depends on the experience and knowledge of responsible officers or shipmasters aboard. During navigation, decision-making made by them can determine the fate of own ship and the ships in the vicinity of her. However, the number of experienced navigation officers or shipmasters is far less than that of the world fleet. New seafarers can not absorb and comprehend such precious experience in a short time. In order to adequately utilize the experience and effectively reduce collisions at sea, an expert system for collision avoidance and navigation (hereinafter called ？gESCAN？h) is proposed in this paper. As a method to come up with the low competence of new seafarers, the ESCAN can provide them with reasonable recommendations of collision avoidance or can help them to know better about current traffic situation and make more reasonable decisions of collision avoidance when dangerous situations happen. Some equipment like radar/ARPA can provide a very simple function for collision avoidance. However, the information obtained from such equipment can not effectively help new seafarers to make reasonable decision-making of collision avoidance in a short time, and they need more helpful information and instructions of collision avoidance. On the other hand, with use of AIS technology, the ESCAN developed in this paper can receive more useful navigational information of other ships in the vicinity of own ship and can provide more sophisticated recommendations or suggestions for dealing with current situation. The following are conclusions from this study. Firstly, COLREGS, the process of collision avoidance and some other related aspects are discussed here. Some results are given as follows: (1) In order to prevent and avoid collisions at sea, and to secure safe navigation of ships, COLREGS needs to be correctly comprehended and strictly carried out. (2) Safe speed is a primary factor ensuring if own ship has enough time to determine and take proper and effective avoidance actions. During navigation, it should be appropriately determined so as to adapt to prevailing circumstances and conditions. (3) Safe passing distance should be maintained during navigation. Normally in open sea two(2) nautical miles are considered to be sufficient. (4) Encountering process of two ships can be divided into 4 phases such as phase of effect-free action, phase of involving risk of collision, phase of involving close-quarters situation and phase of involving danger of collision. (5) Usually, navigators use value of collision risk to know the risk of collision and to select the primary target to avoid. In ESCAN, formula (2-2) is used to appraise the value of collision risk. (6) If own ship is involved in a multi-target encountering situation, ESCAN will analyze the encountering situations between own ship and other ships, predict possible movement of other ships, determine which target is the primary one to avoid, and determine avoiding action and the time to take. Meanwhile, navigators should also consider the safe passing distance of current situation and the safe zone of collision avoidance provided by ESCAN. By using this approach, appropriate decision-making for dealing with current multi-target encountering situation of can be acquired. Secondly, detailed design of ESCAN is introduced and some results can be drawn as follows: (1) The ESCAN is designed and developed by using the theory and technology of expert system and based on information provided by AIS and radar/ARPA system. (2) It is composed of four components. Facts/Data Base in charge of preserving data from navigational equipment, Knowledge Base storing production rules of the ESCAN, Inference Engine deciding which rules are satisfied by facts, User-System Interface for communication between users and ESCAN. (3) In ESCAN, AIS technology is used. AIS can help own ship to receive more detailed navigational information from the ships in the vicinity of her. Therefore, more reasonable decision-making can be determined according to such abundant information. (4) Navigational knowledge used in ESCAN is based on COLREGS and other navigation expertise. (5) Module structure is used to build the knowledge base of ESCAN. And it is divided into six modules such as basic navigational rules module, maneuverability judgment module, division of encountering phase module, encountering situation judgment module, auxiliary knowledge of collision avoidance module, and navigation experience and multi-ship encountering scene avoiding action module. (6) Production rules are used to represent the knowledge of collision avoidance in ESCAN because the structure of them is perfect for representing such knowledge and they are supported by CLIPS well. (7) A new inference process of collision avoidance as shown in Fig.3-8 is used in ESCAN. (8) Mixed inference which combines forward inference and backward inference is used in ESCAN. (9) Because CLIPS adopts Rete Pattern-Matching Algorithm, response speed of ESCAN is greatly increased. Finally, detailed implementation of ESCAN is introduced and some conclusions are given as follows: (1) The part of ESCAN in charge of inference is programmed in CLIPS and the remaining part of it is programmed in Visual C++. (2) The ESCAN has the function of real-time analysis and judgment of various encountering situations between own ship and targets, and is to provide navigators with appropriate plans of collision avoidance and additional advice and recommendation. (3) Auxiliary functions of ESCAN are convenient for users such as simulation function which can simulate avoiding actions provided by ESCAN. (4) According to the results of the examples, the suggestions provided by ESCAN conform to the rules of COLREGS and the advice given by navigation experts well. (5) It is easy to upgrade ESCAN when rules are required to be upgraded in the future. Only rules in Knowledge Base should be rewritten rather than the whole system. (6) Multi-target encountering case matching function of ESCAN can provide a recorded reference case for dealing with current situation if all the conditions of the case are matched. Development of ESCAN not only can help navigators make more reasonable decision-making of collision avoidance so as to ensure safe navigation of ships, but also can promote the development of integrated automatic navigation system which integrates all shipborne systems and implements intelligent unmanned navigation. The future study will deal with integrating ESCAN with other shipborne systems and make it more user-friendly and will carry out the experiment on board which is the important part of ESCAN.Chapter 1 Introduction = 1 1.1 Background and Purpose of the Study = 1 1.2 Introduction of AIS = 5 1.3 Introduction of Expert System and CLIPS = 8 1.4 Related Studies of the Study = 9 1.4.1 Related Studies in China = 9 1.4.2 Related Studies in Other Countries = 10 1.4.3 Principal Research Method in the Related Studies = 11 1.5 Scope and Content of the Study = 13 Chapter 2 Analysis and Research of COLREGS and Collision Avoidance = 14 2.1 COLREGS = 14 2.1.1 Introduction of COLREGS = 14 2.1.2 Content of COLREGS = 15 2.1.3 Look-out = 15 2.1.4 Safe Speed = 16 2.1.5 Risk of Collision = 17 2.1.6 Criterions for Appraising Avoiding Actions = 18 2.2 Process of Collision Avoidance = 19 2.2.1 Flow Chart of Collision Avoidance = 19 2.2.2 Safe Passing Distance = 23 2.2.3 Division of Encountering Process = 23 2.2.4 Division of Encountering Situations of Ships in Sight of One = 27 2.2.5 Avoiding Actions of Ships not in Sight of One Another Because... = 29 2.2.6 Division of Avoiding Actions = 34 2.3 Value of Collision Risk = 35 2.3.1 Approaches for Appraising Collision Risk = 35 2.3.2 Approaches Using Specialities of Sech Function for Appraising... = 42 2.4 Multi-target Collision Avoidance = 54 2.4.1 Judging Encountering Situations with Target-ships = 55 2.4.2 Predicting the Movement Trends of Target-ships = 57 2.4.3 Determining the Primary Target-ship to Avoid = 58 2.4.4 Determining Timing of Taking Avoiding Actions = 60 2.4.5 Considering the Safe Action Zones = 62 2.4.6 Considering the Typical Cases of Multi-ship Collision ... = 63 2.4.7 Approach for Dealing with Multi-target Situation in ESCAN = 66 Chapter 3 Design of ESCAN = 67 3.1 Design of Integrated Structure = 67 3.1.1 External Connection of ESCAN = 67 3.1.2 Structure of ESCAN 6 = 9 3.2 Design of Facts/Data Base = 70 3.3 Design of Knowledge Base = 74 3.3.1 Sources of the Knowledge of Collision Avoidance = 74 3.3.2 Process of Building Knowledge Base = 75 3.3.3 Module Structure of Knowledge Base = 77 3.3.4 Knowledge Representation = 82 3.3.5 Management of Knowledge Base = 107 3.4 Design of Inference Engine = 108 3.4.1 Introduction of Inference Engine = 108 3.4.2 Inference Process of ESCAN = 110 3.4.3 Approaches of Deduction Inference = 114 3.4.4 Pattern-Matching Algorithm = 116 3.4.5 Conflict Resolution = 119 3.5 Design of User-System Interface = 120 Chapter 4 Implementation of ESCAN = 121 4.1 Principles for Developing Expert Systems = 121 4.2 Functional Description of ESCAN = 123 4.3 Computing Formulas Used in ESCAN = 124 4.3.1 Formulas for Calculating Information of Relationship ... = 125 4.3.2 Formulas for Calculating Information of Relationship ... = 127 4.3.3 Formulas for Calculating Position of One Target-ship by ... = 129 4.4 Approach for Judging Whether Ships Have Kept Well Clear off ... = 130 4.5 Approach for Determining Magnitude of Avoiding Action = 131 4.6 Software for Developing ESCAN = 132 4.6.1 Two Types of Software = 132 4.6.2 Embedding CLIPS in Visual C++ = 132 4.7 Building the Modules of Knowledge Base = 133 4.8 Layout of User-System Interface = 134 4.8.1 Main User-System Interface = 134 4.8.2 Other Interfaces = 137 4.9 Practical Functions of ESCAN = 137 4.9.1 Primary Function = 137 4.9.2 Auxiliary Functions = 139 4.10 Using ESCAN to Deal with Single Target-ship Encountering ... = 144 4.10.1 Head-on Situation = 144 4.10.2 Overtaking Situation = 146 4.10.3 Crossing Situation = 147 4.11 Using ESCAN to Deal with Multiple Target-ships Encountering ... = 147 4.11.1 Determining Encountering Situation with Each Target-ship = 148 4.11.2 Selecting the Primary Target-ship to Avoid = 149 4.11.3 Determining Avoiding Action and Timing to Take = 150 4.11.4 Determining Safe Action Zone = 151 4.11.5 Simulating the Determined Avoiding Action = 152 4.11.6 Multi-target Encountering Case Matching = 155 Chapter 5 Conclusion = 159 References = 164 Annex I Content of COLREGS = 171 List of Published Papers during Doctoral Course = 173 Acknowledgements = 17