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

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.

1.. HARD CASES 5. Legal Rights A. Legislation . . . We might therefore do well to consider how a philosophical judge might develop, in appropriate cases, theories of what legislative purpose and legal principles require. We shall find that he would construct these theories in the same manner as a philosophical referee would construct the character of a game. I have invented, for this purpose, a lawyer of superhuman skill, learning, patience and acumen, whom I shall call Hercules. I suppose that Hercules is a judge in some representative American jurisdiction. I assume that he accepts the main uncontroversial constitutive and regulative rules of the law in his jurisdiction. He accepts, that is, that statutes have the general power to create and extinguish legal rights, and that judges have the general duty to follow earlier decisions of their court or higher courts whose rationale, as l

Taking Rights Seriously

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim---either explicitly or implicitly---at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided.

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A Comprehensive Survey of Multiagent Reinforcement Learning

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query $q$ will succeed when there is an argument ${\mathcal A}$ for $q$ that is warranted, i.e. the argument ${\mathcal A}$ that supports $q$ is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.

/pdf/defeasible-logic-programming-an-argumentative-approach-4dhoye3g1q.pdf

Defeasible logic programming: an argumentative approach

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

/pdf/multiagent-systems-a-modern-approach-to-distributed-5fon0donq1.pdf

Multiagent systems: a modern approach to distributed artificial intelligence

http://cs.uns.edu.ar/~grs/Papers/SimariLoui-AIJ1992.pdf

A mathematical treatment of defeasible reasoning and its implementation

Argumentation in Artificial Intelligence examines the intersection between two fields of inquiry: Argumentation Theory and Artificial Intelligence. This book presents an overview of key concepts in argumentation theory and of formal models of argumentation in AI. Beginning with a review of the foundational issues in argumentation and formal argument modeling, the book then moves to more specialized topics, such as algorithmic issues, argumentation in multi-agent systems, and strategic aspects of argumentation. Finally, the volume addresses some practical applications of argumentation in AI and applications of AI in argumentation. Extensive examples are also provided to ensure that readers develop the right intuitions before they move from one topic to another. Knowledge of elementary logic is required, but the text contains an appendix furnishing such preliminaries.

/pdf/argumentation-in-artificial-intelligence-2c8asv1wud.pdf

Argumentation in Artificial Intelligence

The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.

/pdf/towards-an-argument-interchange-format-2iy8fwyrkc.pdf

Guillermo Ricardo Simari

Papers

Defeasible logic programming: an argumentative approach

Multiagent systems: a modern approach to distributed artificial intelligence

A mathematical treatment of defeasible reasoning and its implementation

Argumentation in Artificial Intelligence

Towards an argument interchange format