There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

http://tinf2.vub.ac.be/~dvermeir/courses/thesis/argumentation_frameworks.pdf

On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n -person games

An Introduction to MultiAgent Systems.

Abductive Logic Programming

https://www.doc.ic.ac.uk/%7Erak/papers/BDKTarg.pdf

An abstract, argumentation-theoretic approach to default reasoning

Computing ideal sceptical argumentation

https://www.cs.ait.ac.th/~dung/Site/Publications_files/ai06.pdf

Dialectic proof procedures for assumption-based, admissible argumentation

Assumption-Based Argumentation (ABA) [4, 3, 27, 9, 12, 20, 22] was developed, starting in the 90s, as a computational framework to reconcile and generalise most existing approaches to default reasoning [24, 25, 4, 3, 27, 26]. ABA was inspired by Dung’s preferred extension semantics for logic programming [10, 7], with its dialectical interpretation of the acceptability of negation-as-failure assumptions based on the notion of “no-evidence-to-the-contrary” [10, 7], by the Kakas, Kowalski and Toni interpretation of the preferred extension semantics in argumentation-theoretic terms [24, 25], and by Dung’s abstract argumentation (AA) [6, 8]. Because ABA is an instance of AA, all semantic notions for determining the “acceptability” of arguments in AA also apply to arguments in ABA. Moreover, like AA, ABA is a general-purpose argumentation framework that can be instantiated to support various applications and specialised frameworks, including: most default reasoning frameworks [4, 3, 27, 26] and problems in legal reasoning [27, 13], game-theory [8], practical reasoning and decision-theory [33, 29, 15, 28, 14]. However, whereas in AA arguments and attacks between arguments are abstract and primitive, in ABA arguments are deductions (using inference rules in an underlying logic) supported by assumptions. An attack by one argument against another is a deduction by the first argument of the contrary of an assumption supporting the second argument. Differently from a number of existing approaches to non-abstract argumentation (e.g. argumentation based on classical logic [2] and DeLP [23]) ABA does not have explicit rebuttals and does not impose the restriction that arguments have consistent and minimal supports. However, to a large extent, rebuttals can be obtained “for

/pdf/assumption-based-argumentation-4e567tgft0.pdf

Francesca Toni

Papers

Abductive Logic Programming

An abstract, argumentation-theoretic approach to default reasoning

Computing ideal sceptical argumentation

Dialectic proof procedures for assumption-based, admissible argumentation

Assumption-Based Argumentation