Information Technology and Law,
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新外研社七上知识点sb.=somebody 某人 sth.= something 某事;某物 sp.=someplace 某地do代表动词原形 doing代表动词ing形式 to do为动词不定式did 代表动词过去式Starter要掌握的重点内容:名词、数词、冠词、一般将来时、一般过去时、并列连词and/but/or、基本句型1.welcome to + sp.(某地)欢迎来到某地2. a new stage of ... ...的新舞台/阶段learn new subjects 学习新科目3.learn to do sth. 学会做某事learn from sb. 向某人学习learn about sth. 了解某事/某物4.make friends with sb. 和某人交朋友5.be ready for sth. =prepare for sth. 为某事做好准备be ready to do sth. =prepare to do sth. 准备好做某事get prepared = get ready 做好准备6.forget to do sth. 忘记去做某事forget doing sth. 忘记做过某事7.Moral Education and Law 道德与法治education n. 教育→educational adj. 有教育意义的law n. 法律→ lawyer n. 律师rmation Technology 信息技术information 不可数science technology 科学技术9.sports trousers 运动裤 sports jacket 运动夹克sweater 毛衣 coat 外套;大衣 shirt 男士衬衫 blouse 女士衬衫skirt 短裙 dress 连衣裙 school uniform 校服shorts 短裤 pants 裤子 jeans 牛仔裤10.名词短语:a/an + 形容词 +可数名词单数(a grey jacket)形容词+可数名词复数(black shorts)11.一般将来时(simple future tense)句式:肯定:① sb. will do sth. 某人将会做某事如:I will go to junior high school tomorrow.② sb. am/is/are going to do sth. 某人打算去做某事如:He is going to wear a shirt.否定:① will not=won't如:I won't go to junior high school tomorrow.② am/is/are not going to =am not/isn’t/aren’t going to如:He isn’t going to wear a shirt.疑问:① will sb. do sth如:Will you go to junior high school tomorrow?What will you do tomorrow?② am/is/are sb. going to do sth.如:Is he going to wear a shirt?What is he going to wear?表示将来的判定词:tomorrow 明天、the day after tomorrow 后天、tonight/this evening 在今晚、in the future 在未来next year/week/... 明年/下周/下个...in two days/...(时间段) 两天后/...(时间段)后 ...12.有某物在某个地方的表达方式:There is + a/an+可数单数名词 (+介词短语).There is some +不可数名词 (+介词短语).There are some +可数复数名词 (+介词短语).There is a playground in my school. 有一个操场在我的学校里。
《经济学家》推荐的英文书籍及简介《经济学家》(The Economist)是一份国际性的经济与商业周刊,涵盖了全球范围内的政治、商业、科技、金融等多个领域的报道和分析。
以下是一些根据《经济学家》推荐的英文书籍及简介,注意这只是一个示例,而且由于时间有限,我无法提供最新的推荐。
你可以查阅最新一期或他们的书评专栏获取更多信息。
1.《Sapiens:A Brief History of Humankind》-作者:Yuval NoahHarari简介:这本书以生动的语言概括了人类历史的发展,从早期的人类社会到现代文明。
作者提出了一些深刻的见解,涉及文化、宗教、科技等方面。
2.《The Great Convergence:Information Technology and the NewGlobalization》-作者:Richard Baldwin简介:本书探讨了信息技术是如何改变全球经济和社会格局的。
作者分析了数字化时代的新趋势,以及它对全球化和国际贸易的影响。
3.《The Third Pillar:How Markets and the State Leave theCommunity Behind》-作者:Raghuram Rajan简介:作者讨论了市场和国家之间的权衡,以及社区在这个平衡中的角色。
他提出了一种重新思考社会政策的方式,强调了社区在现代社会中的重要性。
4.《The Code of Capital:How the Law Creates Wealth andInequality》-作者:Katharina Pistor简介:本书解析了资本主义中法律的角色,以及法律如何塑造了财富的分配和不平等。
作者通过法律视角分析了资本主义体系的一些关键问题。
5.《The Narrow Corridor:States,Societies,and the Fate of Liberty》-作者:Daron Acemoglu,James A.Robinson简介:这本书由《为什么国家失败》的作者撰写,研究了国家、社会和自由之间的关系。
INTELLECTUAL PROPERTY LAWIntellectual property (IP) law involves the protection of inventions, artistic, musical, and scientific expressions, and other intangible types of property. Areas encompassed by this field include copyright, trademark, patent, product or service branding, advertising, unfair competition, trade secrecy, cyberlaw, entertainment law, media law, and sports law. IP attorneys may work in general practice or boutique law firms, for government agencies and not-for-profit or public-interest organizations, or as in-house counsel for businesses or other organizations, such as museums.IP attorneys litigate disputes involving the use, acquisition, management, and valuation of their clients’ intellectual property and provide transa ctional services to their clients by drafting applications for registration or granting of intellectual property rights, non-disclosure agreements, and contracts or licenses for the transfer or use of those rights. IP lawyers should have a solid understanding of business and administrative law.Additionally, many IP lawyers, particularly in the entertainment and sports fields, may deal with contract negotiations, labor issues, and other matters specific to their client’s needs. Although there are many jobs in intellectual property law, some areas of practice may be more difficult in which to obtain a start. It is therefore important to develop a solid network of strategic contacts, and it may sometimes be necessary to start out by gaining experience in “allied fields” such as business law or litigation with a plan to move into anLEARN ABOUT INTELLECTUAL PROPERTY LAWMeet with CIPLIT StaffAt your earliest convenience, m eet with the staff of DePaul’s Center forIntellectual Property Law and Information Technology (CIPLIT®). Sign up forthe CIPLIT email mailing list (listserv), and plan to go to as many of CIPLIT’s and other events as possible . Your involvement in CIPLIT’s programs andactivities could be the key to your intellectual property law career.GAIN INTELLECTUAL PROPERTY LEGAL EXPERIENCE GET INVOLVED LEARN ABOUT INTELLECTUAL PROPERTY LAW -Start Your Career! - Take the Technology and Intellectual Property Clinic (TIP Clinic®) -Intern, Extern, or Volunteer -Work in Intellectual Property or an Allied Field -Observe Court Cases, Administrative Hearings, and Mediations-Network and Seek Mentors -Get Involved in Relevant Organizations -Speak with Professors and Career Advisors -Write about Intellectual Property and Get Published - Participate in Moot Courts -Study and Moot Court Abroad -Attend Conferences and Seminars, especially CIPLIT ’s -Meet with CIPLIT Staff -Enroll in IP Law Courses, Seminars, Clinics, and Externships -Read Publications -Seek additional Certifications -Seek Scholarships∙Enroll in Intellectual Property CoursesIntellectual property courses offered at DePaul cover a broad range ofintellectual property and information technology (IP/IT) topics. You shouldplan to get a solid grounding in the core fields of copyrights, trademarks,and patents, and to take additional courses in areas of specialization (suchas cyber law) and in practical training (such as the various IP moot courtsand legal drafting courses). You should also enroll in one of many specializedIP seminars and in the Technology and Intellectual Property Clinic (TIPClinic®). You may gain class credit with an externship through DePaul’sField Placement Program or by taking an independent study with a CIPLITProfessor. Professional skill-building courses in business planning, disputeresolution, mediation, trial advocacy, and litigation strategy can also behelpful.∙Read and Work on Publications focusing on Intellectual Property Law DePaul’s Journal of Art, Technology & Intellectual Property Law and theJournal of Sports Law and Contemporary Problems provide goodopportunities to develop your knowledge of the field as well as your writingskills. Other publications, such as the BNA Patent, Trademark andCopyright Journal (available for free through the law library) and Landslide(published by the American Bar Association Section of Intellectual Property)provide timely and interesting information on IP developments. Also followrelevant IP blogs such as The Patry Copyright Blog; find additional blogs inthe ABA Blawg Directory. Follow CIPLIT online or on facebook.∙Seek additional CertificationsConsider seeking one of the four Certificates offered by DePaul in IntellectualProperty Law to supplement your law degree: the Patent Law Certificate; theIntellectual Property Certificate; the Information Technology Certificate; orthe Arts and Museum Law Certificate.∙Seek Scholarships, Grants, and Income Based RepaymentAll students regardless of practice area(s) of interest can try to lower the costof a legal education by applying for scholarships and grants through DePauland through regional and national organizations. The American IntellectualProperty Education/Sydney B. Williams Law Scholarship is an example of ascholarship available to diverse IP students. Watch the CIPLIT listserv foropportunities. Scholarships, fellowships, and grants are sponsored by barassociations, law firms, community foundations, employers, ethnic andreligious associations, and w Career Services (LCS) also hasprogramming on the College Cost Reduction & Access Act, including PublicService Loan Forgiveness.GET INVOLVED∙Network and Seek MentorsConnect with alumni through CIPLIT’s Career Advisory Board, DePaul’sAlumni Sharing Knowledge (ASK), the law school and university alumniassociations (including your undergraduate school), and social networkingsites such as LinkedIn. Intellectual Property Law Association of Chicago(IPLAC) has frequent events focused on preparing for job applications andinterviews. The Chicago Intellectual Property Alliance (CIPA) has amentorship program that matches second-year law students with practicingIP lawyers. Check the Symplicity and law school event calendars foropportunities to meet IP law professionals. Additional networkingopportunities are available through local, regional, and national barassociations; seek mentors by becoming involved.∙Get Involved with Relevant Organizations On and Off CampusGain hands-on experience, contribute to the intellectual property law field,network, and enhance your resume by joining and becoming active instudent organizations such as DePaul’s Intellectual Property Law Society;consider taking on a leadership role. Become involved in regional andnational organizations such as IPLAC, CIPA, INTA (International TrademarkAssociation), AIPLA (American Intellectual Property Law Association), IPO(Intellectual Property Owners Association), FCBA (Federal Circuit BarAssociation (Patent Litigation Committee), the International Association ofEntertainment Lawyers, the Copyright Society of the U.S.A (Midwest), andthe IP, antitrust, business, corporate, communications, litigation, andcyberlaw sections of the American Bar Association (ABA), Illinois State BarAssociation (ISBA), and Chicago Bar Association (CBA). Look for studentmembership rates.∙Speak with Professors and Career AdvisorsProfessors can be good sources of information, job opportunities, networkingcontacts, and research assistant positions. Establishing a relationship withyour Law Career Services Career advisor is critically important. Meet withyour advisor early and often for advice and support to facilitate your futurein law.• Write about Intellectual Property and Get PublishedSubmit work to writing competitions sponsored by organizations such as theAmerican Bar Association, intellectual property law related websites, andblogs; some offer monetary prizes. Watch the CIPLIT listserv foropportunities. W rite for a law school journal such as DePaul’s Journal ofArt, Technology & Intellectual Property Law or the Journal of Sports Law andContemporary Problems, another academic journal, or a bar associationpublication. Publication credits give your resume more cachet, show your“expertise,” and make you more attractive to potential employers.∙Participate in Moot CourtIf you’re interested in litigation, become involved in IP Moot Courtcompetitions or the National Cultural Heritage Law Moot Court Competitionto hone your brief-writing and oral argument skills.∙Study and Moot Court AbroadDePaul’s Study Abroad Program in Beijing, China includes an IntellectualProperty Moot Court Competition sponsored by the Supreme People’s Courtof China and Beijing Foreign Studies University. DePaul’s Study AbroadProgram in Costa Rica currently includes a one-credit course in IntellectualProperty and Human Rights.∙Attend Seminars and ConferencesCheck the Symplicity and law school event calendars for offerings on and offcampus. DePaul’s CIPLIT hosts many conferences and symposiums, most ofwhich are open to students. Search online for intellectual property lawconferences and committee/section meetings held by organizationsincluding the ABA and CBA. IP conferences around Chicago include IPLAC’sJudicial Conference and programs from Lawyers for the Creative Arts.Nationally, look for opportunities including the ABA’s Forum on theEntertainment & Sports Industries Annual Meeting.GAIN INTELLECTUAL PROPERTY LEGAL EXPERIENCE∙Start your Career!Develop and refine your resume to brand yourself as a promising IP law jobcandidate. Create strong cover letters, and search effectively for jobs andinternships with the help of LCS advisors and CIPLIT staff. Check outSymplicity’s Resources tab or contact LCS at (312) 362-8385 for moreinformation.∙Intern, Extern, or VolunteerCheck Symplicity (for internship opportunities, paid or unpaid), the FieldPlacement Program (for externship opportunities, working for class credit),and the Pro Bono & Community Service Initiative (for volunteeropportunities). The Chicago Bar Foundation and American Bar Associationhave many links on public service opportunities. For summer IP internshiplistings, check the American Society of Composers, Authors and Publishers,Broadcast Music Inc., Sound Exchange, CIPA, the U.S. Patent & TrademarkOffice, and the U.S. Copyright Office. Consider an externship with a federaljudge. Network to find job opportunities.∙Work in Intellectual Property or an Allied FieldIt helps to have good contacts in the IP law field. Not everyone may find anIP law job as a new law grad. If you can’t locate an IP job initially, s tart byworking in an “allied field” such as business law or litigation with a plan tomake a lateral move into an IP practice with solid transferable skills. Jobopportunities can be found in the intellectual property departments of lawfirms, in small boutique firms, in corporations, in non-profit organizations,and in government agencies. As a law student, contact CIPLIT staff if you’reinterested in working at the USPTO or looking for a research assistantshipduring the summer. Check for postings on the CIPLIT listserv and inSymplicity’s Job Posting tab.∙Observe Court Cases, Administrative Hearings, and MediationsEven if not specific to your practice area of interest, understanding how thelegal system works is important in the practice of law. With limitedexceptions (i.e., juvenile cases), most of the judicial process is open to thepublic. Students who wish to observe a court in session may go to thefederal courthouse, check the court calendar, and watch a proceeding. Berespectful of the judicial process and try to enter the courtroom before courtis called into session; dress professionally.RESOURCESBlogs∙Breaking Legal News and Entertainment Law BlogSports Law Blog∙TechLawForum∙∙The Patry Copyright Blog∙VRP Law – The Business, Intellectual Property, and Employment Law Blog Conferences∙American Bar Association Annual International Legal Symposium on the World of Music, Film, Television and Sports∙Sports Industry Networking and Career Conference (SINC)DePaul University College of Law∙Alumni Sharing Knowledge (ASK)∙Center for Intellectual Property Law and Information Technology (CIPLIT)∙College of Law Event Calendar∙Field Placement Program∙Intellectual Property Law Society∙Journal of Art, Technology, and Intellectual Property Law∙Journal of Sports Law and Contemporary Problems∙American Bar Association Section of Intellectual Property Law∙American Intellectual Property Law Association (AIPLA)∙Association of Intellectual Property Law Firms∙Chicago Bar Association Young Lawyers Section Creative Arts Committee∙Chicago Intellectual Property Alliance (CIPA)∙Copyright Society of the U.S.A (Midwest)∙Illinois State Bar Association Intellectual Property Section Council∙Intellectual Property Law Association of Chicago (IPLAC)∙International Association of Entertainment Lawyers∙International Trademark Association (INTA)∙Lawyers for the Creative Arts∙LinkedIn∙National Sports and Entertainment Law Society∙Public Interest Intellectual Property Advisors (PIIPA)∙United States Copyright Office∙United States Patent & Trademark OfficePublications∙BNA Patent, Trademark and Copyright Journal∙Journal of Intellectual Property Law and Practice@IPLawAlerts, @CopyrightLaw, @EntLawUpdate, @SportsLawGuy,@WinstonAdvLaw, @darrenrovell, @Sportsbizmiss, @paper.li (Technology Law Report)More at ABA Blawg Directory。
澳门大学欧盟法,国际法及比较法授课型研究生申请要求澳门大学简介学校名称澳门大学学校英文名称University of Macau学校位置中国 | 澳门 | 氹仔2020 QS 世界排名387澳门大学概述澳门大学(Universidade de Macau / University ofMacau),简称澳大,是澳门第一所现代大学,为中欧商校联盟、“一带一路”国际科学组织联盟创始成员,是澳门第一所获AACSB认证的大学。
澳门大学前身为1981年3月28日成立的东亚大学。
1991年更名为澳门大学。
2014年8月正式迁入位于横琴岛的新校区。
据2019年8月学校官网显示,学校校园面积约一平方公里;设有10个学院,约130多个学位课程;拥有学生10,414人,其中本科生7141人,研究生3,273人;截至2018年12月,澳门大学全职工作人员总数1538人。
欧盟法,国际法及比较法专业简介为了增强澳门作为中欧之间桥梁的持续作用,以及中欧与世界之间关系的显着增长,有必要建立欧盟法,国际法和比较法硕士学位课程, 在这些领域培养出更多合格的法学家,专业人士和学者。
该计划自2003/2004学年开始,旨在在欧盟法,国际法和比较法领域提供优质的教育和培训,并保持和提高澳门大学法学院的地位。
澳门法律,欧洲大陆法,国际法和比较法在该地区乃至全球的重要研究机构。
欧盟法,国际法及比较法专业相关信息专业名称欧盟法,国际法及比较法专业英文名称Master of Law in Eurpean Union Law, International and Comparative Law隶属学院法学院学制2年语言要求雅思6(小分5.5),托福80,六级430GMAT/GRE 要求不需要2020 Fall 申请时间10月学费(当地货币)102600澳门币欧盟法,国际法及比较法课程内容序号课程中文名称课程英文名称1研究方法论与法律写作研讨会Research Methodology and Legal Writing Seminar2比较法律制度Comparative Legal Systems 3欧盟机构法Institutional Law of the European Union4欧盟经济法Economic Law of the European Union5欧盟的外部法律和政策External Law and Policy of the European Union6选修课程将从欧盟选修课程列表中选择elective courses to be selected fromthe list of Elective Course EU Competition7欧盟环境法EU Environmental Law8欧盟自然资源和能源法EU Natural Resources and Energy Law9欧盟条约改革Treaty Reforms in the EU 10外商投资法Foreign Investment Law 11国际刑法和人道主义法International Criminal and Humanitarian Law12人权与难民法Human Rights and Refugee Law序号课程中文名称课程英文名称13国际组织法Law of International Organizations 14法律文化与法律多元化Legal Culture and Legal Pluralism 15普通法Common Law16海商法Maritime and Shipping Law17电子商务和信息技术法E-commerce and Information Technology Law18模拟Mooting19研讨会系列Seminar Series20澳门法学概论Introduction to Macau Law21澳门与比较游戏法Macau and Comparative Gaming Law22国际贸易法高级问题Advanced Issues of International Trade Law23亚洲商法Asian Business Law 24公司法Company Law25法律与社会Law and Society26创意经济中的知识产权法Intellectual Property Law in the Creative Economy27洗钱与反腐败法Money Laundering and Anti-Corruption Law28国际私法Private International Law 29消费者保护法Consumer Protection Law 30商业合同Commercial Contracts* 澳门大学欧盟法,国际法及比较法研究生申请要求由 Mastermate 收集并整理,如果发现疏漏,请以学校官网为准。
科技与犯罪英语作文1Technology has brought about remarkable advancements and conveniences to our lives, but it has also provided new avenues for criminal activities. The rapid development of technology has significantly transformed the forms and means of crimes.One prominent example is the rise of cybercrime. Hackers, with their sophisticated skills, can breach the security systems of various websites and platforms, leading to the leakage of personal information of countless individuals. This not only poses a threat to people's privacy but also gives rise to potential risks such as identity theft and fraud.Another instance is the application of high-tech methods in financial fraud. Criminals can utilize advanced software and algorithms to manipulate financial transactions, deceive unsuspecting victims, and embezzle large sums of money. They may create fake online investment platforms or send phishing emails that appear legitimate, tricking people into revealing their financial details.The misuse of technology by criminals is a cause for serious concern. It requires us to enhance our technological defense mechanisms and strengthen law enforcement efforts. At the same time, individuals should also raise their awareness of protecting personal information and bevigilant against potential online threats. Only through the joint efforts of society as a whole can we effectively combat the criminal activities that take advantage of technological advancements and ensure the safety and stability of our lives.2Technology has become an indispensable tool in the fight against crime. In the modern era, advanced technologies have significantly enhanced the capabilities of law enforcement agencies and contributed to maintaining social security.Big data technology, for instance, has revolutionized crime-solving processes. By analyzing vast amounts of information from various sources such as social media, financial transactions, and communication records, the police can identify patterns and connections that might otherwise go unnoticed. This enables them to narrow down the list of potential suspects and focus their investigations more efficiently.Surveillance cameras also play a crucial role in providing key evidence for criminal cases. These cameras are installed in public places, commercial areas, and residential neighborhoods, constantly monitoring activities. In cases of theft, assault, or other crimes, the footage captured by these cameras can provide valuable clues about the identity of the perpetrator, the sequence of events, and even the escape route.Furthermore, forensic technologies like DNA analysis and fingerprintidentification have made it possible to accurately identify criminals and link them to the crime scene. This has greatly increased the chances of successful prosecutions and convictions.In conclusion, technology has not only made it more difficult for criminals to evade justice but also has given the public a greater sense of security. It is an essential ally in the battle to keep our societies safe and orderly.3Technology has become an integral part of our lives, but it has also brought new challenges in the field of crime. However, we can leverage technology to prevent criminal activities and create a safer society.One effective measure is the development of advanced identity verification systems. For instance, biometric identification techniques such as fingerprint or facial recognition can significantly reduce fraud. These systems make it much more difficult for criminals to assume false identities and carry out illegal activities.Another promising approach is the utilization of artificial intelligence technology to predict crime hotspots. By analyzing large amounts of data related to crime patterns, geographical locations, and social factors, AI can identify areas where crimes are more likely to occur. This enables law enforcement agencies to deploy resources more efficiently and proactively prevent crimes before they happen.Furthermore, surveillance systems equipped with intelligent analytics can help monitor public areas in real-time. They can detect suspicious behaviors and alert the authorities promptly, allowing for quick intervention and prevention of potential criminal acts.In conclusion, technology holds great potential in combating crime. By continuously innovating and applying these technological solutions, we can build a society where the risks of criminal activities are minimized, and people can live in peace and security.4Technology has undoubtedly revolutionized our lives in countless ways, but it has also brought about a complex web of issues when it comes to crime. The rapid advancement of technology has created new legal and moral predicaments. For instance, the digital realm has given rise to cybercrimes such as hacking, online fraud, and identity theft. These crimes are often difficult to detect and prosecute due to the anonymous nature of the internet and the sophistication of the techniques employed by criminals.At the same time, technology has also provided law enforcement agencies with powerful tools to combat crime. Sophisticated surveillance systems, data analytics, and forensic techniques have enhanced the ability to investigate and solve crimes. However, this raises concerns about the potential infringement of citizens' rights and privacy.To strike a balance between using technology to fight crime andsafeguarding citizens' rights is a challenging task. We need clear and strict legal frameworks that define the boundaries of technological use in law enforcement. Also, there should be independent oversight to ensure that these powers are not abused.In conclusion, technology is a double-edged sword in the context of crime. It has both enabled new forms of criminal activity and offered means to counter them. The key lies in finding the right equilibrium through thoughtful legislation, ethical use of technology, and vigilant protection of individual rights.5In today's highly technological world, the relationship between technology and crime has become increasingly complex. Different countries have adopted various approaches to combat crime using technology, with varying degrees of success.Take developed countries, for instance. They have made significant progress in the field of counter-terrorism through the use of advanced technologies. Sophisticated surveillance systems, data analytics, and artificial intelligence have enabled them to detect and prevent potential terrorist attacks before they occur. These countries have also invested heavily in research and development to stay ahead of the ever-evolving tactics of terrorists.On the other hand, developing countries often face numerouschallenges when it comes to dealing with cyber crimes. Limited resources, technological infrastructure, and a lack of skilled personnel pose significant obstacles. Despite their efforts to strengthen cyber security, they struggle to keep up with the rapid pace of technological advancements that cyber criminals exploit.From these comparisons, several valuable lessons can be drawn. Firstly, a comprehensive and integrated approach that combines technological innovation with effective law enforcement is essential. Secondly, international cooperation is crucial to sharing best practices and resources. Finally, continuous investment in education and training to build a skilled workforce capable of handling technological crime is of paramount importance.In conclusion, as technology continues to advance at an unprecedented pace, it is imperative for all countries to adapt and evolve their strategies to effectively combat crime and ensure the safety and security of their citizens.。
LLM专业在美国留学申请中可谓是非常热门的专业之一,那么作为美国常青藤盟校之一的耶鲁大学,它的LLM专业怎么样呢?接下来,86留学网为大家详细介绍一下美国耶鲁大学法学院LLM专业怎么样。
美国耶鲁大学法学院LLM项目简介美国耶鲁大学法学院LLM项目Master of Laws (LLM),每年耶鲁录取的LL.M.学生极少。
这个LLM项目旨在培养学生将来致力于法学教学职业(a career in teaching law)。
没有固定的课程安排。
所有的课程都是可选性的。
学生可以根据自己的专业领域兴趣来选择课程,设计自己的LLM专业。
为了达到教学要求,学生可以选择耶鲁法学院的任何教学资源,从而为将来的法学教研工作做好充分准备。
学生们也很享受学校设置的小规模班级,以及与国际生、J.D.学生与耶鲁法学院教职员工之间的友谊。
说到小规模班级,一般每年22到25人。
近4年来,我们的LLM学生来自于30多个国家。
Interest方向包括:·Administrative Law·Comparative Administrative Law·Constitutional Law·Corporate & Commercial Law·Environmental Law·Human Rights Law·Information Technology Law·International Law·Law & Health·Law & Media·Law Teaching·Public Interest Law美国耶鲁大学法学院LLM临床与体验学习Clinical andExperiential Learning耶鲁法学院有一个强大且独一无二的临床实践项目。
不同于其他学校,学生可以在第一学年的春季考试这个临床时间项目或者出庭。
The Impact of Technology on the Legal IndustryIn recent years, the legal industry has undergone significant transformations, primarily driven by advancements in technology. These advancements have not only revolutionized the way lawyers operate but have also altered the entire legal landscape, making it more efficient, accessible, and sometimes, more complex.Firstly, technology has dramatically improved the efficiency of legal practices. The introduction of electronic document management systems has allowed lawyers to store, retrieve, and share documents with unprecedented ease. This has eliminated the need for physical filing cabinets and reduced the amount of time spent on administrative tasks, freeing up lawyers to focus on more substantive legal work.Furthermore, the rise of artificial intelligence (AI) and machine learning has enabled lawyers to process vast amounts of data quickly and accurately. AI-powered tools can assist in tasks such as contract review, legal research, and even predicting legal outcomes. These technologies have the potential to streamline the legal process, reducing costs and improving client satisfaction.However, the impact of technology on the legal industry is not all positive. On one hand, it has made legal services more accessible to the general public. Online legal resources and platforms provide individuals with access to legal information and even legal representation at a lower cost. This has democratized the legal system, allowing more people to seek legal redress.On the other hand, the increasing reliance on technology has also raised concerns about data privacy and security. Lawyers and law firms are entrusted with sensitive client information, and any breach of this data can have serious consequences. As such, lawyers must remain vigilant in protecting client data and ensuring that their technology systems are secure.Moreover, the rapid pace of technological change has also posed challenges for lawyers. The need to stay updated with the latest technological advancements and understand how they can be applied to legal practice has become increasingly important. Lawyers must be willing to embrace new technologies and adapt their practices accordingly, or they may fall behind in an increasingly competitive market.In conclusion, technology has had a profound impact on the legal industry, both positive and negative. While it has improved efficiency and accessibility, it has also raised concerns about data privacy and security. Lawyers must remain vigilant in protecting client data while embracing new technologies and adapting their practices to stay competitive in a rapidly changing market.。
63 Towards the Application of Association Rules for Defeasible Rule DiscoveryGuido Governatori Cooperative Information SystemsResearch Centre, Queensland University of Technology, Brisbane,Queensland,Australiaernatori@.auAndrew Stranieri Donald Berman Laboratory for Information Technology and Law, La Trobe University, Bundoora,Victoria,Australiastranier@.auAbstract.In this paper we investigate the feasibility of Knowledge Discovery fromDatabases(KDD)in order to facilitate the discovery of defeasible rules that representthe ratio decidendi underpinning legal decision making.Moreover we will argue infavour of Defeasible Logic as an appropriate formal system in which the extractedprinciples should be encoded.1IntroductionOne of the main and most controversial issues in the legal domain is the identification of the ratio decidendi.In other words,given a collection of similar cases we want to determine the principles(rules)leading to the adjudication of the cases.On the other hand the defeasibil-ity of normative reasoning is a very well established phenomenon,so the rules(principles) extracted from the cases should be defeasible and we have to identify the appropriate non-monotonic reasoning mechanism.The central aim of this paper is to demonstrate that thefield of knowledge discovery from databases(KDD)can be applied to facilitate the discovery of defeasible rules that represent the ratio decidendi underpinning legal decision making.According to[13]KDD techniques can be grouped into four categories;•Classification.The aim of classification techniques is to group data into predefined cate-gories such as‘pro-plaintiff’or‘pro-defendant’.KDD classification techniques have been applied to law by[33],[32],[35],[20]and[5].•Clustering.The aim of clustering techniques is to analyze data in order to group the data meaningfully.Clustering techniques have been applied to the law by[29],[27],[19],[31],[8],[34],[24]and[10].•Series Analysis.The aim of series analysis to discover sequences within the data.Very few studies have been performed that analyze sequences of data in law.The study by[28] is an exception.Guido Governatori and Andrew Stranieri,‘Towards the Application of Association Rules for Defeasible Rule Discovery’in Bart Verheij, Arno R.Lodder,Ronald P.Loui and Antoinette J.Muntjewerff(eds.),Legal Knowledge and Information Systems.Jurix2001:The Fourteenth Annual Conference.Amsterdam:IOS Press,2001,pp.63-75.ernatori and A.Stranieri•Association.The objective of association techniques is to discover ways in which data elements are associated with other data elements.For example,an association between the gender of litigants and the outcome of their cases may surprise analysts and stimulate hypotheses to explain the phenomena.Association techniques have not been applied to law to the same extent as classification and clustering techniques.[30]have illustrated that association rule generators can highlight interesting associations in a small dataset in family law.In that study,the Apriori algorithm advanced by[1]was applied to suggest hypotheses for future investigation.The present study differs in that it applies association rules generated directly from data to facilitate the discovery of defeasible rules.One of the main applications of the KDD process is the discovery of rules(principles) from a set of relevant cases.It is indeed customary in Common Law to examine precedents in order to extract the ratio decidendi,and very often the principles leading to the adjudication can be expressed as a set of defeasible rules.However,it is seldom the case that precedents consist only of facts;more often the facts are supplemented by some principles.In some cases the explicitly listed principles are not enough to justify the conclusions.Thus the proposed methodology can be used to identify hidden principles.In this study we represent legal reasoning using defeasible logic.First of all Defeasible Logic has been developed by Nute[25,26]over several years with a particular concern about computational efficiency(indeed,its efficiency is linear cf.[22])and ease of implementation (nowadays several implementations exist[9,23]and some of them can deal with theories consisting of over100,000propositional rules[23]).In[3]it was shown that Defeasible logic isflexible enough to deal with several intuitions of non-monotonic reasoning,and it has been applied to legal reasoning[4]and legal negotiation[16].Moreover various variants of Defeasible Logic have been characterised in terms of argumentation semantics[17,18]; thus it is possible to establish correspondences with other non-monotonic systems for legal reasoning.Defeasible rules are sourced from regulations,statutes,precedents and expert heuristics. This is a very manually intensive task that involves skilled knowledge engineers and experts. As a consequence,the knowledge acquisition phase is typically extensive.The objective of the present study is to apply KDD so as to automatically suggest plausible defeasible rules. This will facilitate knowledge engineer-expert interaction and lead to the development of improved knowledge bases in less time.Often,in the knowledge acquisition phase,the focus is not on the discovery of rules.The principles are known,but we have to identify the most appropriate rules for the case at hand, especially when the rules are conflicting.In such cases we have to establish a preference order among available rules.KDD techniques explored in this study can help to identify rule preferences.Another benefit in the application of KDD to facilitate the generation of defeasible rules involves the validation of decision making practice against explicitly given normative codes. Both precedents and statutes are frequently subject to opposite interpretations;thus a method to determine rules from cases can be used to see whether an interpretation corresponds to actual legal practice.Finally the same approach can be applied to the measure the degree of legal efficacy.Towards the Application of Association Rules for Defeasible Rule Discovery65[21]describes the application of KDD in the form of inductive logic programming(ILP) to learning in two settings,predictive and descriptive.The aim of an ILP approach for pre-dictive discovery is to induce a theory that explains positive and negative examples and back-ground knowledge.This has been applied to learning default rules by[12].The descriptive setting usually includes only positive examples and relaxes the strict notion of explanation. This enables ILP to be applied to describe patterns in a dataset that meet criteria such as as-sociativity by enhancing algorithms for generating association rules[11].Although ILP has been applied to discover default rules and also to enhance the discovery of association rules, ILP is not used in this study.Instead,association rules that are discovered using conventional algorithms are applied to help identify plausible groups of defeasible rules.The KDD technique known as Association Rules is described in the next section of this paper.Following that,Defeasible Logic is described.In the subsequent section the application of association rules to facilitate the identification of useful defeasible rules will be discussed with examples from a hypothetical data set constructed for the purpose.2Association RulesAn association rule identifies a link between two or more attributes in a dataset.A famous,yet unsubstantiated example of an association rule that is generated from a supermarket database of purchases is:Nappies→Beer(Support=30%,Confidence=75%)The support represents the number of times Nappies and Beer are purchased together as a proportion of the total transactions.The support for Nappies and Beer is the proportion of itemsets(Nappies,Beer)in the dataset,i.e.,frequency(Nappies,Beer)/n where an itemset is a group of items such as(Nappies,Beer)within a transaction.The confidence is interpreted as the percentage of transactions where Beer is purchased given that Nappies are purchased.For example,of the10transactions four involved Nappies and,of those3also purchased Beer then the confidence is75%.The confidence of a rule A→B is the conditional probability that a transaction contains B given that it contains A and is calculated as Support(A∪B)/Support(A)An association rule is drawn directly from data.It is not a generalization from the data but merely identifies an association between the purchase of features.As such,association rules are typically used to identify patterns in data-sets that can be used to suggest hypotheses.Difficulties in the generation of association rules in large datasets involve the combina-torial explosion in the number of rules to be assessed.For example,if a database has only2 boolean attributes A,B then the support of6itemsets representing the following rules need to be calculated:A→;A→B;A→A;B→;B→A;B→B;If the the database has3boolean attributes the support of25itemsets need to be calculated.In a real world database containing many multi-valued attributes,and large numbers of records, the support calculations become extremely demanding of memory and processor resources.[1]first described the Apriori algorithm for discovering association rules from large databases.This algorithm reduced the computational complexity by avoiding the explorationernatori and A.Stranieriof all rules.This was achieved by inviting the user to specify threshold confidence and sup-port values.If a rule A,B→D did not meet the threshold values then the Apriori algorithm avoids an analysis of the rule A,B,C→D and all other rules that have A,B in the an-tecedent.The application of association rules to facilitate the discovery of defeasible rules advanced in this paper,relies on the calculation of a support for every itemset in a database.For the trials performed in this study,the Apriori implementation by Christian Borgelt(http://www.cs.uni-magdeburg.de/∼borgelt)was used with the support and confidence thresholds set to0.This did not present a resource problem because the data set was small.However,for the method advanced here to scale to large datasets an approach that can generate the support of all itemsets in real time must be adopted.Fortunately,[15]has recently advanced brute force algorithms that are tractable in large datasets.3Defeasible LogicIn this section we describe Defeasible Logic formally following the presentation of[6].De-feasible logic is a sceptical formalism,meaning that it does not support contradictory con-clusions.Instead it seeks to resolve differences.In cases where there is some support for concluding A but also support for concluding not A(¬A),the logic does not conclude either of them(thus the name“sceptical”).If the support for A has priority over the support for¬A then A would be concluded.Sceptical reasoning,in general,is appropriate for the study of normative reasoning.A set of norms(rules)will be represented as a defeasible theory.A defeasible theory,i.e., a knowledge base in Defeasible Logic,consists offive different kinds of knowledge:facts, strict rules,defeasible rules,defeaters,and a superiority relation defined over the rules.Facts denote simple pieces of information that are deemed to be true regardless of other knowledge items.A typical fact is that John is a minor:minor(John).Briefly,strict and defeasible rules are represented,respectively,by expressions of the form A1,...,A n→B and A1,...,A n⇒B,where A1,...,A n is a possibly empty set of prerequisites and B is the conclusion of the rule.Strict rules are rules in the classical sense:whenever the premises of a rule are given, we are allowed to apply the rule and get a conclusion.When the premises are indisputable (e.g.,facts)then so is the conclusion.An example of a strict rule is“every minor is a person”. Written formally:minor(X)→person(X).Defeasible rules are rules that can be defeated by contrary evidence.An example of such a rule is“every person has the capacity to perform legal acts to the extent that the law does not provide otherwise”;written formally:person(X)⇒hasLegalCapacity(X).The idea is that if we know that someone is a person,then we may conclude that he/she has legal capacity,unless there is other evidence suggesting that he/she has not.Defeaters are a special kind of rules.They are used to prevent conclusions not to support them.For example:WeakEvidence ¬guilty This rule states that if pieces of evidence are assessed as weak,then they can prevent the derivation of a“guilty”verdict;on the other hand they cannot be used to support a“not guilty”conclusion.The superiority relation among rules is used to define priorities among rules,that is,whereTowards the Application of Association Rules for Defeasible Rule Discovery67one rule may override the conclusion of another rule.For example,given the defeasible rulesr:person(X)⇒hasLegalCapacity(X)r :minor(X)⇒¬hasLegalCapacity(X)which contradict one another,no conclusive decision can be made about whether a minor has legal capacity.But if we introduce a superiority relation>with r >r,then we can indeed conclude that the minor does not have legal capacity.It turns out that we only need to define the superiority relation over rules with contradic-tory conclusions.Also notice that a cycle in the superiority relation is counter-intuitive from the knowledge representation perspective.In the above example,it makes no sense to have both r>r and r >r.Consequently,the defeasible logic we discuss requires an acyclic superiority relation.Now we present formally defeasible logics.A rule r consists of its antecedents(or body) A(r)which is afinite set of literals,an arrow,and its consequent(or head)C(r)which is a literal.There are three kinds of arrows,→,⇒and which correspond,respectively,to strict rules,defeasible rules and defeaters.Where the body of a rule is empty or consists of one formula only,set notation may be omitted in examples.Given a set R of rules,we denote the set of all strict rules in R by R s,the set of strict and defeasible rules in R by R sd,the set of defeasible rules in R by R d,and the set of defeaters in R by R d ft.R[q]denotes the set of rules in R with consequent q.A defeasible theory D is a structure D=(F,R,>)where F is afinite set of facts,R is a finite set of rules,>is a binary relation over R.A conclusion of D is a tagged literal,where a tag is either∂or∆,that may have positive or negative polarity.+∆q which is intended to mean that q is definitely provable in D(i.e.,using only strict rules).−∆q which is intended to mean that we have proved that q is not definitely provable in D. +∂q which is intended to mean that q is defeasibly provable in D.−∂q which is intended to mean that we have proved that q is not defeasibly provable in D.Basically a conclusion B is supported if there is a rule whose conclusion is B,the prereq-uisites are either supported or given in the case at hand,and a stronger rule whose conclusion is not B has prerequisites that fail to be supported.Provability is based on the concept of a derivation(or proof)in D=R.A derivation is afinite sequence P=(P(1),...,P(n))of tagged literals satisfying four conditions(which correspond to inference rules for each of the four kinds of conclusion).In the following P(1..i)denotes the initial part of the sequence P of length i.+∆:If P(i+1)=+∆q then∃r∈R s[q]∀a∈A(r):+∆a∈P(1..i)−∆:If P(i+1)=−∆q then∀r∈R s[q]∃a∈A(r):−∆a∈P(1..i)ernatori and A.StranieriThe definition of∆describes just forward chaining of strict rules.For a literal q to be definitely provable we need tofind a strict rule with head q,of which all antecedents have been definitely proved previously.And to establish that q cannot be proven definitely we must establish that for every strict rule with head q there is at least one antecedent which has been shown to be non-provable.Now we turn to the more complex case of defeasible provability.Before giving its formal definition we provide the idea behind such a notion.A defeasible proof of a literal p consists of three phases.In thefirst phase either a strict or defeasible rule is put forth in order to support a conclusion p;then we consider an attack on this conclusion using the rules for its negation ¬p.The attack fails if each rule for¬p is either discarded(it is possible to prove that part of the antecedent is not defeasibly provable)or if we can provide a stronger counterattack, that is,if there is an applicable strict or defeasible rule stronger than the rule attacking p.It is worth noting that defeaters cannot be used in the last phase.+∂:If P(i+1)=+∂q then either+∆q∈P(1..i)or(1)∃r∈R sd[q]∀a∈A(r):+∂a∈P(1..i)and(2)−∆∼q∈P(1..i)and(3)∀s∈R[∼q]either(a)∃a∈A(s):−∂a∈P(1..i)or(b)∃t∈R sd[q]such that∀a∈A(t):+∂a∈P(1..i)and t>s.−∂:If P(i+1)=−∂q then−∆q∈P(1..i)and(1)∀r∈R sd[q]∃a∈A(r):−∂a∈P(1..i)or(2)+∆∼q∈P(1..i)or(3)∃s∈R[∼q]such that(a)∀a∈A(s):+∂a∈P(1..i)and(b)∀t∈R sd[q]either∃a∈A(t):−∂a∈P(1..i)or t>s.Let us work through the condition for+∂,an analogous explanation holds for−∂.To show that q is provable defeasibly we have two choices:(1)We show that q is already definitely provable;or(2)we need to argue using the defeasible part of D as well.In particular,we require that there must be a strict or defeasible rule with head q which can be applied(2.1). But now we need to consider possible“attacks”,that is,reasoning chains in support of a complementary of q.To be more specific:to prove q defeasibly we must show that every complementary literal is not definitely provable(2.2).Also(2.3)we must consider the set of all rules for∼q which are not known to be inapplicable(note that here we consider defeaters, too,whereas they could not be used to support the conclusion q;this is in line with the motivation of defeaters.Essentially each such rule s attacks the conclusion q.For q to be provable,each such rule s must be counterattacked by a rule t with head q with the following properties:(i)t must be applicable at this point,and(ii)t must be stronger than s.Thus each attack on the conclusion q must be counterattacked by a stronger rule.It is worth noting that defeaters can be simulated in term of the other elements of Defea-sible Logic[2],thus we can consider theories without defeaters.To explain the mechanism of defeasible derivations–showing at the same time the ap-propriateness of Defeasible Logic for normative reasoning–we consider rule162of the Aus-tralian Civil Aviation Regulations1988:“When two aircraft are on converging headings at approximately the same height,the aircraft that has the other on its right shall give way, except that(a)power-driven heavier-than-air aircraft shall give way to airships,gliders andTowards the Application of Association Rules for Defeasible Rule Discovery69balloons;...”This norm can be represented in defeasible logic as follows:r1:¬rightOfWay(Y,X)⇒rightOfWay(X,Y)r2:onTheRightOf(X,Y)⇒rightOfWay(X,Y)Thefirst rule states that,given two aircraft,if one of the aircraft does not have right of way then the other aircraft does,while the second states that the aircraft X has right of way over the aircraft Y if X is on the right of Y;r3:powerDriven(X),¬powerDriven(Y)⇒¬rightOfWay(X,Y)The idea of the above rules is that a power-driven aircraft does not have right of way over a non-power-driven one.r4:balloon(X)→¬powerDriven(X)r5:glider(X)→¬powerDriven(X)r4and r5classify balloons and gliders as non-power-driven aircraft andr6:⇒powerDriven(X)assumes aircraft to be power-driven unless further information is given.The superiority rela-tion is determined as follows:r3>r2because r3is an exception to r2.By specificity r4>r6 and r5>r6.Moreover r1>r3.Let us examine the following cases:1)two aircraft of the same type(power-driven,non-power-driven)are converging2)a power-driven aircraft and a non-power-driven aircraft are converging.In thefirst case we can apply r2since the prerequisites of r3do not hold and we cannot prove the antecedent of r1.In the second case we can apply r3given that both of the two prerequisites hold,and after the conclusion of r3has been established we can apply r1to derive that the non-power-driven aircraft has the right of way over the power-drive one.It is worth noting that in this example all the rules have been drawn from the same nor-mative code,and the superiority relation is explicitly given.However,thefirst is not a serious limitation in so far as it is possible to merge several codes in the same defeasible theory; for the second[7]proposed a variant of Defeasible Logic where the superiority relation is computed dynamically according to some principles encoded as defeasible rules.4Identifying Defeasible Rule Sets using Association RulesAssociation rules automatically generated from a dataset can facilitate the discovery of strict rules,defeasible rules and rule preferences.We must assume that the data set is a high quality set in that there are no missing values and all values present are correctly recorded(i.e.,no noise).The extent to which these assumptions can be violated is left to future research.In order to illustrate the application of association rules to the discovery of defeasible rule,we adopt a hypothetical sample dataset that relates to the aircraft example illustrated in the previous section.Table1represents three records from36situations that were regarded as plausible recordings of air traffic data.For example,Case1illustrates a situation in whichernatori and A.StranieriCase Right of way Y power driven X power driven position x Type y Type 1rightOfWayX yPowerDriven xPowerDriven X on right Xairship Yairship 2rightOfWayY yPowerDriven xPowerDriven Y on right Xairship Yairship 3rightOfWayX yPowerDriven-xPowerDriven X on right Xglider YairshipTable1:Sample data for aircraft data setItemset Attribute Attribute Attribute Support1.X on right0.492.X on right¬yPowerDriven0.223.rightOfWayX X on right0.4054.rightOfWayX X on right¬yPowerDriven0.1355.¬yPowerDriven X on right rightOfWayX0Table2:Sample itemsets for aircraft data setaircraft X was granted right of way over Y.In that situation both X and Y were power driven and X was on the right of Y.As discussed above,association rules are typically deployed to suggest hypotheses with datasets so large that the generation of all possible rules is not normally feasible in real time. Instead,a threshold level of support and confidence are set by the user,so that only those rules that exceed the thresholds are sought.However,for the purposes of this study,the confidence and support thresholds are set to0in order to generate all itemsets.Table2illustrates a sample of the1057itemsets with up to four antecedent and one consequent attribute(rightOfWay)generated from the aircraft dataset.Itemset1in that table indicates that49%of records in the set included the attribute X on right;Itemset4reflects that13.5%of records contains the itemset triplet(rightOfWayX,X on right,-yPowerDriven).Recall that the confidence of a rule is the conditional probability that the consequent will occur given the antecedent has been observed.The confidence of the rule A⇒B isSupport(A,B)Support(A).Table3lists two rules along with their confidence,support and a measure called interest.According to[14]the“interestingness”of a rule refers to the degree to which a discovered pattern is of interest to the user.The interest metric aims to highlight those rules that are more interesting than others.Various metrics have been advanced but a conventional metric for the calculation of interest for the rule A→B is theSupport(A,B).Support(A)∗Support(B)Although the mere generation of association rules may arguably be a useful tool for the engagement of a domain expert in the process of eliciting defeasible rules,the sheer number of rules generated mitigates against this.A closer analysis of the itemsets and rules may go some way toward facilitating the automatic generation of strict and defeasible rules from association rules.Towards the Application of Association Rules for Defeasible Rule Discovery71 Rule Rule Confidence Support Interest ar1.X on right⇒rightOfW ayX0.680.405 1.15ar2.X on right,−yP owerDriven⇒¬rightOfW ayX0.620.135 1.63Table3:Sample rules from aircraft data setFor the association rule A→B generated from a non-noisy data-set,to be interpreted as a Strict rule in defeasible reasoning,we assume the confidence of the rule to be100%. Every time A is observed B is also observed.For example,itemsets(A,¬B),(A,C,¬B), are not present in the data-set.We assume the closed world assumption and conclude that these itemsets do not exist because their presence could represent exceptions to the A→B rule and render it defeasible.For the association rule A→B generated from a non-noisy data-set,to be interpreted as a defeasible rule the confidence of the rule A→B is greater than0but less than100%.On some occasions that A is also observed,B is observed and on others¬B is observed.There are typically many rules with a confidence between0and100so additional effort is required in order to identify rules that may be considered meaningful in a defeasible rule set. To identify plausible defeasible rules we look for groups of rules that represent the following situations:Simple Exceptions;Separate Vs.Aggregate rules;General Vs.Specific rules;and Apparent irrelevance.A simple exception occurs when a rule is an exception to a base rule that results in the opposite conclusion.Consider two defeasible rules,ar3:A⇒P and ar4:A,B⇒¬P. ar4is an exception to ar3because A is associated with P unless in the presence of B, it is associated with¬P.The necessary conditions for this situation to be identified using association rules are:•the support for the itemsets(A,P),and(A,B,¬P)is greater than0.This indicates that there the rules A⇒P and A,B⇒¬P are represented in the dataset•the support for the itemset(A,B,P)should be0.This confirms that A,in the presence of B never leads to P.The rules illustrated in Table3meet the criteria of a simple exception group.The rule ar1in that table is taken to be the base rule and ar2is the exception.If these conditions are met we identify the base rule and determine a rule preference using the“interestingness”metric where the exception rule has a higher preference than the rule. In the example above ar1is the base rule so the preference relation is ar1<ar2.A variant of simple exception,refers to the situation where there is a clear defeasible rule say,ar5:A⇒P and many different exceptions such as ar6:A,B⇒¬P,ar7:A,C⇒¬P,ar8:A,D⇒¬P.Each single exception has a very limited distribution.The necessary conditions above still apply to identify these rules as a group with a base rule and several exceptions.The procedure for identifying rule pairs(or groups)that meet simple exception criteria is currently performed using a brute force approach on the small hypothetical dataset.This procedure is cumbersome in that any defeasible rule(i.e.,those that correspond to associationernatori and A.Stranierirules with a confidence between0and100)could be a base rule in an exception group. Therefore,rules that may be exceptions to the base candidate must be sought according to the criteria above.This task explodes combinatorially with large numbers of candidate rules. Future research aims to develop more effective mechanisms than brute force that can be applied to large datasets in real time.Another situation that association rules can help to automatically identify involves sepa-rate and aggregate rules.In this situation A and B are,independently,sufficient reasons for P,but,very often they occur simultaneously.In this case we want to conclude that there are two rules for P,i.e.,A⇒P and B⇒P instead of A,B⇒P.We identify this situation from the support of itemsets as follows:Support(A,P)∗Support(B,P)>tSupport(A,B,P)where t is the aggregate threshold defined by an user.The next situation addressed involves general vs specific rules.Consider two defeasible rules,ar8:A⇒P and ar9:A,B⇒P.Rule ar9is more specific than ar8because it applies to fewer cases than ar8does.However,in some contexts the more specific(and complex)defeasible rule is preferred and in others the more general(and simpler)rule is preferred.Here again,we include a user defined minimum threshold support for the general rule.If the minimum support is reached then the rule with the highest confidence is selected. For example,the rule ar10:X on right⇒rightOfWayX has a support of40.5%and confidence83%the rule ar11:X on right and¬yPowerDriven⇒rightOfWayX has a support of13.5%and confidence62%.If the minimum support for a general rule is set by the user to less than40%then ar10is preferred because the confidence of ar10is greater than that for ar11.However,if the minimum support is greater than40.5%the general rule does not reach the minimum support for general rules and ar11is selected.A preference for a more general rule over a specific rule underpins the situation we call Apparent irrelevance.Let us suppose that A alone is not a sufficient reason for D,but together with B it is:the general rule ar12:A⇒D does not prevail over the more specific rule ar13:A,B⇒D. At the same time C alone is not enough to conclude¬D,but¬D follows from B and C together:ar13:C,B⇒¬D prevails over ar14:C⇒¬D.The use of association rules to facilitate the discovery of defeasible rules is limited in a number of ways.If the antecedent and consequent are not in the same record in the dataset then no association rule corresponding to the desired defeasible rule can be found.For exam-ple r1above cannot be discovered by association rules because the antecedent and consequent are not separate attributes in the sample dataset:r1:¬rightOfWay(Y,X)⇒rightOfWay(X,Y)r2:onTheRightOf(X,Y)⇒rightOfWay(X,Y)r3:powerDriven(X),¬powerDriven(Y)⇒¬rightOfWay(X,Y)r4:balloon(X)→¬powerDriven(X)r5:glider(X)→¬powerDriven(X)。