AI in the 1980s and Beyond: An MIT Survey

History of artificial intelligence
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Rissland and Skalak purposely chose to address a reduced version of the problem of statutory interpretation that explicitly did not consider policies, principles and other important normative considerations. For instance, it creates structural analogies when the rules run out or are otherwise inconclusive to show a legal predicate has been satisfied. In our special issue, Branting uses his experience with GREBE to elucidate key aspects of legal argument structure having to do with "warrants", an idea originating in Toulmin 's classic work.

There was also some exploration of hybrid systems using blackboards e. These veins have not been much emphasized, although interest in such approaches regularly resurfaces. The theme of heuristic search in the service of argument creation became the focus of another project by Rissland and Skalak. Since the space of information is far too vast to explore profligately, and since one cannot conceivably use everything discovered, there is a need to control the search for and the accumulation of pieces of information. The BankXX project explored how knowledge about an evolving argument-its growing collection of supporting, contrary, leading, best cases, legal theories, prototypes, etc.

Ikbals, built by Vossos in conjunction with his Ph. Split-Up integrated neural nets and rules to determine property divisions in divorce settlements. By the mid s, the field was clearly well on its way to tackling some of the central issues in legal reasoning: reasoning with rules especially in the face of conflict between rules , reasoning with cases, and open texture in legal predicates.

There was now a well-established international community; several members of the second generation had fledged and left their Ph. The s saw a renewal of interest in legal information retrieval, in part because of improved retrieval engines, new learning-based information extraction techniques, and the dramatic rise of the World Wide Web. For instance, in , Hafner edited a special issue of Artificial Intelligence and Law devoted to intelligent legal text-based systems. This focus was represented in that issue by the Flexlaw system of J.

Smith and his group at the University of British Columbia. Also in that issue Graham Greenleaf and his colleagues made some pioneering reflections on the relationships between knowledge-based systems, databases, and hypertext systems. With the advent of the WWW this became a very important topic. Subsequently there have been additional efforts in this direction both in Europe and the US.

The article by Jackson et al. In Europe, conceptual retrieval, principled systems development, and sharing and reuse of knowledge based on ontologies were given additional impulse by the need to harmonize legislation across the polyglot countries of the European Union. Initiated through the Ph.


Since the s, a burgeoning community has focused on developing models of argumentation. Some researchers like Giovanni Sartor and Ron Loui have concentrated on models that address reasoning with norms. In the mid s, Tom Gordon developed a dialogue-based model of legal pleading, and Loui and Norman developed a discourse-based model of legal argumentation. Gordon developed his dialogue approach into the web-based ZENO system, which was used in Germany for facilitating public commentary on a planned high technology park and residential zone, and Loui developed his web-based "Room 5", which allowed users to argue about US Supreme Court cases involving such issues as freedom of speech.

Several doctoral theses and subsequent books from the energetic Dutch community, such as those of Jaap Hage, Henry Prakken and Bart Verheij, have made significant strides on developing models of argumentation. Over the years, work in this area has only intensified; hardly an issue of Artificial lmelligence and Law is published without some article addressing this topic. In I a special double issue of Artificial Intelligence and Law, edited by Prakken and Sartor, was devoted to logical models of legal argumentation, and in , another special double issue, edited by Feteris and Prakken, focused on formal and informal models of dialectical legal argument.

The article by Verheij shows how ideas about dialectical argument can be used to build environments to assist in the creation of arguments. Each reduction operator corresponds to a justification step, or warrant, available for use in arguments about other cases. This model shows how a decision's justification, not just its facts and outcome, influences how it can be used to make arguments about other cases. It also permits portions of multiple decisions to be combined to form new arguments.

Bench-Capon's and Sartor's article develops a logically-grounded account of how cases are used in legal reasoning, particularly for defeasible reasoning. According to their view, reasoning with cases is a process of theory construction, evaluation, and application.

They provide a definition of what constitutes a theory of a body of case law, and how competitive theories are constructed. Their approach also gives a central role to a notion of the purposes motivating legal theories, revealed in cases and used to ground preferences between rules.

He does this, not in the law, but in ethics, a related normative domain full of open texture, precedents, and hard questions but offering somewhat less structure and constraint than law for AI techniques to employ. He evaluates these ideas in the context of retrieval. Aleven presents the definitive report on his CATO project. He enriches the underlying HYPO model by refining and extending its representation and use of factors, by focusing on representing the reasons why factors matter as relevant similarities or differences among cases.

He evaluates both the contribution these reasons make to argument quality and how well CATO teaches law students to make such arguments. In the movie The Paper Chase, the character of Professor Kingsfield, a stereotypical curmudgeon of a law professor, throws down the gauntlet to his fresh-faced IL's first-year students by announcing that, "You come in here with a head full of mush and you leave thinking like a lawyer. Jackson and his colleagues give us a window on the use of intelligent information retrieval and extraction in the legal domain.

They report on their efforts to apply information extraction techniques to full text court opinions in order to ferret out the linkages between cases. Linkages are used to identify and summarize the procedural history of an individual case as it makes its way through the court system, and to discern how a case is commented upon and viewed in subsequent cases that discuss it and how it interprets prior cases that it cites.

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Main article: Big Data. Featured Conferences. About Help Legal. Bill and mandatory secondary education last century, helping American workers move out of agriculture and into other sectors. They advocated building intelligence "from the bottom up. On 11 May , Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.

As anyone with even the most casual contact with natural language understanding knows, this is far from a simple task, since cases can be cited and referred to in a daunting variety of ways, and from widely disparate portions of a text. Developing a system that once trained does this automatically and to a level that meets commercial standards is a challenge. England in ; his proposal to employ logic for legal information retrieval and inference was rightly termed..

This conference contained a large number of watershed AI papers including those by Minsky o. McCarthy on his Advice Taker. Selfridge on his Pandemonium. Law Applications''. New York. Marek Sergot's.. Academic Press. Buchanan and others were looking for a domain to try out ideas about the emerging techniques of expert systems honed from building the DENDRAL system. They chose medicine. Allen advocates using normalization techniques to remediate syntactic difficulties e.

These ideas have been used to draft some actual statutes e. All of these projects are described in Computing Power and Legal Reasoning.

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Cases that get settled before they are litigated are typically easy, and those that become court cases. Those that end up in the Supreme Court are very hard. Theirs were back-to-back papers in the same session. CEG was in fact an early example of adaptive CBR: retrieve a good enough example that matches as many of the desiderata as possible from an Examples-space a Case Base and then try to satisfy other goals with modifications.

They discussed many of the enduring issues-like open texture and the complementarity of CBR and RBR-still of interest today.

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They were cautious about the creation of intelligent aids for legal practitioners. Proceedings from the conferences are available from the ACM. I contained articles on a theory of case-based argument Skalak and Rissland , deontic logic as a representation of law Jones and Sergol , legal knowledge-based systems Bench-Capon and Coenen , legal practice systems Lauritsen , and a review of Ashley's book.

Such an interesting mix of topics is typical. However, her cases were not concrete cases i. An earlier doctoral project at MIT by Jeffrey Meldman in on the law of assault and battery aJso used rules and cases, but the cases were represented as rules that encoded their rationes decidendi. Verheij, Rules. Send us an email. Skip to main content. In this article we provided examples of ways in which AI is being pioneered and applied in education. Out of those provided, intelligent tutoring systems ITS seem to have made the most progress over the last 20 years, as one of the original concepts for applications of AI in education.

All have the potential to help shape a next generation of more personalized learning and responsive teaching.

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Content Technologies, Inc. Learning platforms for the modern workplace are designed to allow employees to master additional skills and receive continuous and automated feedback, and when used strategically have the potential to help improve performance and increase production. Intelligent tutoring systems ITS have made much progress since their early counterparts. While it seems obvious that no one in education is eager for virtual humans to come and replace educators, the idea of creating virtual human guides and facilitators for use in a variety of educational and therapeutic environments is a promising area of development.

Though not yet a reality, the ultimate goal in this field is to create virtual human-like characters who can think, act, react, and interact in a natural way, responding to and using both verbal and nonverbal communication.

AI in the 1980s and Beyond: An MIT Survey

The University of Southern California USC Institute for Creative Technologies is a pioneer in creating smart virtual environments and applications that draw on AI, 3-D gaming, and computer animation to develop authentic virtual characters and realistic social interactions.

Captivating Virtual Instruction for Training CVIT , for example, is a distributed learning strategy that aims to integrate live classroom methods with best-fit virtual technologies—including virtual facilitators, augmented reality, intelligent tutor, and others—in remote learning and training programs.

Education is a domain largely ruled by human-to-human interaction, and integration of AI has been slower to develop the necessary human-like attributes of responsiveness, adaptability, and understanding. Woolf, et al. Not to be overlooked is the apparent fear that human educators can or will be replaced by AI technologies in the coming decade. In , ten of the world's leading electrical engineers convened on the burgeoning topic of "Artificial Intelligence" - which was far from being recognized as a field.

A decade later, the progress of early AI efforts had some researchers envisioning a relatively easy glide to human-level intelligence in machines. The Internet of Things IoT has the potential to fall into the general pit of buzzword-vagueness.

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Artificial intelligence AI often falls into the same trap, particularly with the advent of new terms such as "machine learning," "deep learning," "genetic algorithms," and more. Growing numbers of students are taking college entrance exams such as the SAT and tutoring industry giants such as Khan Academy are helping students meet their test prep goals.

What are examples of artificial intelligence that you're already using—right now?

What is artificial intelligence AI? And are the technologies that we have today really reflective of all that this term implies? Emerj Artificial Intelligence Research helps enterprises and executives survive and thrive in AI disruption with in-depth AI research, advisory, and insight.