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2009 | Buch

Argumentation Theory: A Very Short Introduction Kapitel lesen Erstes Kapitel lesen

Argumentation in Artificial Intelligence

herausgegeben von: Guillermo Simari, Iyad Rahwan

Verlag: Springer US

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Argumentation is all around us. Letters to the Editor often make points of cons- tency, and “Why” is one of the most frequent questions in language, asking for r- sons behind behaviour. And argumentation is more than ‘reasoning’ in the recesses of single minds, since it crucially involves interaction. It cements the coordinated social behaviour that has allowed us, in small bands of not particularly physically impressive primates, to dominate the planet, from the mammoth hunt all the way up to organized science. This volume puts argumentation on the map in the eld of Arti cial Intelligence. This theme has been coming for a while, and some famous pioneers are chapter authors, but we can now see a broader systematic area emerging in the sum of topics and results. As a logician, I nd this intriguing, since I see AI as ‘logic continued by other means’, reminding us of broader views of what my discipline is about. Logic arose originally out of re ection on many-agent practices of disputation, in Greek Ant- uity, but also in India and China. And logicians like me would like to return to this broader agenda of rational agency and intelligent interaction. Of course, Aristotle also gave us a formal systems methodology that deeply in uenced the eld, and eventually connected up happily with mathematical proof and foundations.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Argumentation Theory: A Very Short Introduction
Since the time of the ancient Greek philosophers and rhetoricians, argumentation theorists have searched for the requirements that make an argument correct, by some appropriate standard of proof, by examining the errors of reasoning we make when we try to use arguments. These errors have long been called fallacies, and the logic textbooks have for over 2000 years tried to help students to identify these fallacies, and to deal with them when they are encountered. The problem was that deductive logic did not seem to be much use for this purpose, and there seemed to be no other obvious formal structure that could usefully be applied to them.
Douglas Walton

Abstract Argument Systems

Frontmatter
Chapter 2. Semantics of Abstract Argument Systems
An abstract argument system or argumentation framework, as introduced in a seminal paper by Dung [13], is simply a pair
Pietro Baroni, Massimiliano Giacomin
Chapter 3. Abstract Argumentation and Values
Abstract argumentation frameworks, as described in Chapter 11 are directed towards determining whether a claim that some statement is true can be coherently maintained in the context of a set of conflicting arguments. For example, if we use preferred semantics, that an argument is a member of all preferred extensions establishes that its claim must be accepted as true, and membership of at least one preferred extension shows that the claim is at least tenable. In consequence, that admissible sets of arguments are conflict free is an important requirement under all the various semantics.
Trevor Bench-Capon, Katie Atkinson
Chapter 4. Bipolar abstract argumentation systems
In most existing argumentation systems, only one kind of interaction is considered between arguments. It is the so-called attack relation. However, recent studies on argumentation [23, 34, 35, 4] have shown that another kind of interaction may exist between the arguments. Indeed, an argument can attack another argument, but it can also support another one. This suggests a notion of bipolarity, i.e. the existence of two independent kinds of information which have a diametrically opposed nature and which represent repellent forces.
Claudette Cayrol, Marie-Christine Lagasquie-Schiex
Chapter 5. Complexity of Abstract Argumentation
The semantic models discussed in Chapter 11 provide an important element of the formal computational theory of abstract argumentation. Such models offer a variety of interpretations for “collection of acceptable arguments” but are unconcerned with issues relating to their implementation. In other words, the extension-based semantics described earlier distinguish different views of what it means for a set, S, of arguments to be acceptable, but do not consider the procedures by which such a set might be identified.
Paul E. Dunne, Michael Wooldridge
Chapter 6. Proof Theories and Algorithms for Abstract Argumentation Frameworks
Previous chapters have focussed on abstract argumentation frameworks and properties of sets of arguments defined under various extension-based semantics. The main focus of this chapter is on more procedural, proof-theoretic and algorithmic aspects of argumentation. In particular, Chapter 11 describes properties of extensions of a Dung argumentation framework.
Sanjay Modgil, Martin Caminada

Arguments with Structure

Frontmatter
Chapter 7. Argumentation Based on Classical Logic
Argumentation is an important cognitive process for dealing with conflicting information by generating and/or comparing arguments. Often it is based on constructing and comparing deductive arguments. These are arguments that involve some premises (which we refer to as the support of the argument) and a conclusion (which we refer to as the claim of the argument) such that the support deductively entails the claim.
Philippe Besnard, Anthony Hunter
Chapter 8. Argument-based Logic Programming
In this chapter we describe several formalisms for integrating Logic ProgrammingandArgumentation. Research on the relation between logic programming and argumentation has been and still is fruitful in both directions: Some argumentation formalisms were used to define semantics for logic programming and also logic programming was used for providing an underlying representational language for non-abstract argumentation formalisms.
Alejandro J. García, Jürgen Dix, Guillermo R. Simari
Chapter 9. A Recursive Semantics for Defeasible Reasoning
One of the most striking characteristics of human beings is their ability to function successfully in complex environments about which they know very little. Reflect on how little you really know about all the individual matters of fact that characterize the world. What, other than vague generalizations, do you know about the apples on the trees of China, individual grains of sand, or even the residents of Cincinnati? But that does not prevent you from eating an apple while visiting China, lying on the beach in Hawaii, or giving a lecture in Cincinnati. Our ignorance of individual matters of fact is many orders of magnitude greater than our knowledge. And the situation does not improve when we turn to knowledge of general facts. Modern science apprises us of some generalizations, and our experience teaches us numerous higher-level although less precise general truths, but surely we are ignorant of most general truths.
John L. Pollock
Chapter 10. Assumption-Based Argumentation
Assumption-Based Argumentation (ABA) [4, 3, 27, 11, 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 [9, 7], with its dialectical interpretation of the acceptability of negation-as-failure assumptions based on the notion of “no-evidence-to-the-contrary” [9, 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].
Phan Minh Dung, Robert A. Kowalski, Francesca Toni
Chapter 11. The Toulmin Argument Model in Artificial Intelligence
Or: how semi-formal, defeasible argumentation schemes creep into logic
In 1958, Toulmin published The Uses of Argument. Although this anti-formalistic monograph initially received mixed reviews (see section 2 of [20] for Toulmin’s own recounting of the reception of his book), it has become a classical text on argumentation, and the number of references to the book (when writing these words1 —by a nice numerological coincidence—1958) continues to grow (see [7] and the special issue of Argumentation 2005; Vol. 19, No. 3). Also the field of Artificial Intelligence has discovered Toulmin’s work. Especially four of Toulmin’s themes have found follow-up in Artificial Intelligence.
Bart Verheij
Chapter 12. Proof Burdens and Standards
This chapter explains the role of proof burdens and standards in argumentation, illustrates them using legal procedures, and surveys the history of research on computational models of these concepts. It also presents an original computational model which aims to integrate the features of these prior systems.
Thomas F. Gordon, Douglas Walton

Argumentation in Multi-Agent Systems

Frontmatter
Chapter 13. Dialogue Games for Agent Argumentation
The rise of the Internet and the growth of distributed computing have led to a major paradigm shift in software engineering and computer science. Until recently, the notion of computation has been variously construed as numerical calculation, as information processing, or as intelligent symbol analysis, but increasingly, it is now viewed as distributed cognition and interaction between intelligent entities [60]. This new view has major implications for the conceptualization, design, engineering and control of software systems, most profoundly expressed in the concept of systems of intelligent software agents, or multi-agent systems [99]. Agents are software entities with control over their own execution; the design of such agents, and of multi-agent systems of them, presents major research and software engineering challenges to computer scientists.
Peter McBurney, Simon Parsons
Chapter 14. Models of Persuasion Dialogue
This chapter1 reviews formal dialogue systems for persuasion. In persuasion dialogues two or more participants try to resolve a conflict of opinion, each trying to persuade the other participants to adopt their point of view. Dialogue systems for persuasion regulate how such dialogues can be conducted and what their outcome is. Good dialogue systems ensure that conflicts of view can be resolved in a fair and effective way [6]. The term ‘persuasion dialogue’ was coined by Walton [13] as part of his influential classification of dialogues into six types according to their goal. While persuasion aims to resolve a difference of opinion, negotiation tries to resolve a conflict of interest by reaching a deal, information seeking aims at transferring information, deliberationdeliberation wants to reach a decision on a course of action, inquiry is aimed at “growth of knowledge and agreement” and quarrel is the verbal substitute of a fight. This classification leaves room for shifts of dialogues of one type to another. In particular, other types of dialogues can shift to persuasion when a conflict of opinion arises. For example, in information-seeking a conflict of opinion could arise on the credibility of a source of information, in deliberation the participants may disagree about likely effects of plans or actions and in negotiation they may disagree about the reasons why a proposal is in one’s interest.
Henry Prakken
Chapter 15. Argumentation for Decision Making
Decision making, often viewed as a form of reasoning toward action, has raised the interest of many scholars including economists, psychologists, and computer scientists for a long time. Any decision problem amounts to selecting the “best” or sufficiently “good” action(s) that are feasible among different alternatives, given some available information about the current state of the world and the consequences of potential actions. Available information may be incomplete or pervaded with uncertainty. Besides, the goodness of an action is judged by estimating how much its possible consequences fit the preferences of the decision maker. This agent is assumed to behave in a rational way [29] amgoud-woold, at least in the sense that his decisions should be as much as possible consistent with his preferences.
Leila Amgoud
Chapter 16. Argumentation and Game Theory
In a large class of multi-agent systems, agents are self-interested in the sense that each agent is interested only in furthering its individual goals, which may or may not coincide with others’ goals. When such agents engage in argument, they would be expected to argue strategically in such a way that makes it more likely for their argumentative goals to be achieved. What we mean by arguing strategically is that instead of making arbitrary arguments, an agent would carefully choose its argumentative moves in order to further its own objectives.
Iyad Rahwan, Kate Larson
Chapter 17. Belief Revision and Argumentation Theory
Belief revision is the process of changing beliefs to adapt the epistemic state of an agent to a new piece of information. The logical formalization of belief revision is a topic of research in philosophy, logic, and in computer science, in areas such as databases or artificial intelligence. On the other hand, argumentation is concerned primarily with the evaluation of claims based on premises in order to reach conclusions. Both provide basic and substantial techniques for the art of reasoning, as it is performed by human beings in everyday life situations and which goes far beyond logical deduction. Reasoning, in this sense, makes possible to deal successfully with problems in uncertain, dynamic environments and has been promoting the development of human societies.
Marcelo Alejandro Falappa, Gabriele Kern-Isberner, Guillermo Ricardo Simari

Applications

Frontmatter
Chapter 18. Argumentation in Legal Reasoning
A popular view of what Artificial Intelligence can do for lawyers is that it can do no more than deduce the consequences from a precisely stated set of facts and legal rules. This immediately makes many lawyers sceptical about the usefulness of such systems: this mechanical approach seems to leave out most of what is important in legal reasoning. A case does not appear as a set of facts, but rather as a story told by a client. For example, a man may come to his lawyer saying that he had developed an innovative product while working for Company A. Now Company B has made him an offer of a job, to develop a similar product for them. Can he do this? The lawyer firstly must interpret this story, in the context, so that it can be made to fit the framework of applicable law. Several interpretations may be possible. In our example it could be seen as being governed by his contract of employment, or as an issue in Trade Secrets law.
Trevor Bench-Capon, Henry Prakken, Giovanni Sartor
Chapter 19. The Argument Interchange Format
While significant progress has been made in understanding the theoretical properties of different argumentation logics and in specifying argumentation dialogues, there remain major barriers to the development and practical deployment of argumentation systems. One of these barriers is the lack of a shared, agreed notation or “interchange format” for argumentation and arguments. In the last years a number of different argument mark-up languages have been proposed in the context of tools developed for argument visualisation and construction (see [10] for a review). Thus, for example, the Assurance and Safety Case Environment (ASCE)1 is a graphical and narrative authoring tool for developing and managing assurance cases, safety cases and other complex project documentation.
Iyad Rahwan, Chris Reed
Chapter 20. Empowering Recommendation Technologies Through Argumentation
User support systems have evolved in the last years as specialized tools to assist users in a plethora of computer-mediated tasks by providing guidelines or hints 19. Recommender systems are a special class of user support tools that act in cooperation with users, complementing their abilities and augmenting their performance by offering proactive or on-demand, context-sensitive support. Recommender systems are mostly based on machine learning and information retrieval algorithms, providing typically suggestions based on quantitative evidence (i.e. measures of similarity between objects or users). The inference process which led to such suggestions is mostly unknown (i.e. ‘black-box’ metaphor). Although the effectiveness of existing recommenders is remarkable, they still have some serious limitations.
CarlosIván Chesñevar, Ana Gabriela Maguitman, María Paula González
Chapter 21. Arguing on the Semantic Grid
In the last decade, the rapid evolution of Internet technologies has opened new perspectives, created new application areas, provided new social environments for communication and posed new challenges. Among the most influential domains of Internet sciences to date we find Web services, Grid computing, the Web 2.0, and the Semantic Web. These are components of a wider vision, which we call the Semantic Grid.
Paolo Torroni, Marco Gavanelli, Federico Chesani
Chapter 22. Towards Probabilistic Argumentation
All arguments share certain key similarities: they have a goal and some support for the goal, although the form of the goal and support may vary dramatically. Human argumentation is also typically enthymematic, i.e., people produce and expect arguments that omit easily inferable information. In this chapter, we draw on the insights obtained from a decade of research to formulate requirements common to computational systems that interpret human arguments and generate their own arguments. To ground our discussion, we describe how some of these requirements are addressed by two probabilistic argumentation systems developed by the User Modeling and Natural Language (UMNL) Group at Monash University: the argument generation system nag (Nice Argument Generator) [18, 19, 20, 38, 39, 40], and the argument interpretation system bias (Bayesian Interactive Argumentation System) [7, 8, 34, 35, 36, 37].
Ingrid Zukerman
Chapter 23. Argument-Based Machine Learning
The most common form of machine learning (ML) is learning from examples, also called inductive learning . Usually the problem of learning from examples is stated as: Given examples, find a theory that is consistent with the examples. We say that such a theory is induced from the examples. Roughly, we say that a theory is consistent with the examples if the examples can be derived from the theory. In the case of learning from imperfect, noisy data, we may not insist on perfect consistency between the examples and the theory. In such cases, a shorter and only “approximately” consistent theory may be more appropriate.
Ivan Bratko, Jure Žabkar, Martin Možina
Backmatter
Metadaten
Titel
Argumentation in Artificial Intelligence
herausgegeben von
Guillermo Simari
Iyad Rahwan
Copyright-Jahr
2009
Verlag
Springer US
Electronic ISBN
978-0-387-98197-0
Print ISBN
978-0-387-98196-3
DOI
https://doi.org/10.1007/978-0-387-98197-0

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