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An abstract framework for argumentation with structured arguments
- IN INFORMATION TECHNOLOGY & LAWYERS: ADVANCED TECHNOLOGY IN THE
, 2009
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On the evaluation of argumentation formalisms
, 2007
"... Argumentation theory has become an important topic in the field of AI. The basic idea is to construct arguments in favor and against a statement, to select the “acceptable” ones and, finally, to determine whether the original statement can be accepted or not. Several argumentation systems have been ..."
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Cited by 109 (21 self)
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Argumentation theory has become an important topic in the field of AI. The basic idea is to construct arguments in favor and against a statement, to select the “acceptable” ones and, finally, to determine whether the original statement can be accepted or not. Several argumentation systems have been proposed in the literature. Some of them, the so-called rule-based systems, use a particular logical language with strict and defeasible rules. While these systems are useful in different domains (e.g. legal reasoning), they unfortunately lead to very unintuitive results, as is discussed in this paper. In order to avoid such anomalies, in this paper we are interested in defining principles, called rationality postulates, that can be used to judge the quality of a rule-based argumentation system. In particular, we define two important rationality postulates that should be satisfied: the consistency and the closure of the results returned by that system. We then provide a relatively easy way in which these rationality postulates can be warranted for a particular rule-based argumentation system developed within a
REPRESENTING BUSINESS CONTRACTS IN RuleML
, 2005
"... This paper presents an approach for the specification and implementation of translating contracts from a human-oriented form into an executable representation for monitoring. This will be done in the setting of RuleML. The task of monitoring contract execution and performance requires a logical acco ..."
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Cited by 103 (49 self)
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This paper presents an approach for the specification and implementation of translating contracts from a human-oriented form into an executable representation for monitoring. This will be done in the setting of RuleML. The task of monitoring contract execution and performance requires a logical account of deontic and defeasible aspects of legal language; currently such aspects are not covered by RuleML; accordingly we show how to extend it to cover such notions. From its logical form, the contract will be thus transformed into a machine readable rule notation and eventually implemented as executable semantics via any mark-up languages depending on the client’s preference, for contract monitoring purposes.
Semi-Stable Semantics
, 2003
"... In this paper, we examine an argument-based semantics called semistable semantics. Semi-stable semantics is quite close to traditional stable semantics in the sense that every stable extension is also a semi-stable extension. One of the advantages of semi-stable semantics is that there exists at le ..."
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Cited by 93 (13 self)
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In this paper, we examine an argument-based semantics called semistable semantics. Semi-stable semantics is quite close to traditional stable semantics in the sense that every stable extension is also a semi-stable extension. One of the advantages of semi-stable semantics is that there exists at least one semi-stable extension. Furthermore, if there also exists at least one stable extension, then the semi-stable extensions coincide with the stable extensions. This, and other properties, make semi-stable semantics an attractive alternative for the more traditional stable semantics, which until now has been widely used in fields such as logic programming and answer set programming.
Propositional Defeasible Logic has Linear Complexity
- of Logic Programming
, 2001
"... Defeasible logic is a rule-based nonmonotonic logic, with both strict and defeasible rules, and a priority relation on rules. We show that inference in the propositional form of the logic can be performed in linear time. This contrasts markedly with most other propositional nonmonotonic logics, i ..."
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Cited by 82 (6 self)
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Defeasible logic is a rule-based nonmonotonic logic, with both strict and defeasible rules, and a priority relation on rules. We show that inference in the propositional form of the logic can be performed in linear time. This contrasts markedly with most other propositional nonmonotonic logics, in which inference is intractable. 1 Introduction Mostwork in non-monotonicreasoning has focussed on languages for whichpropositional inference is not tractable. Sceptical default reasoning is \Pi p 2 -hard, even for very simple classes of default rules, as is sceptical autoepistemic reasoning and propositional circumscription. The complexity of sceptical inference from logic programs with negation-as-failure varies according to the semantics of negation. For both the stable model semantics and the Clark completion, sceptical inference is co-NPhard. See [13, 9] for more details. Although such languages are very expressive, and this expressiveness has been exploited in answer-set progra...
On the Issue of Reinstatement in Argumentation
, 2006
"... Dung’s theory of abstract argumentation frameworks [8] led to the formalization of various argument-based semantics, which are actually particular forms of dealing with the issue of reinstatement. In this paper, we re-examine the issue of semantics from the perspective of postulates. In particular, ..."
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Cited by 76 (21 self)
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Dung’s theory of abstract argumentation frameworks [8] led to the formalization of various argument-based semantics, which are actually particular forms of dealing with the issue of reinstatement. In this paper, we re-examine the issue of semantics from the perspective of postulates. In particular, we ask ourselves the question of which (minimal) requirements have to be fulfilled by any principle for handling reinstatement, and how this relates to Dung’s standard semantics. Our purpose is to shed new light on the ongoing discussion on which semantics is most appropriate.
Efficient Defeasible Reasoning Systems
- International Journal of Artificial Intelligence Tools
, 2001
"... For many years, the non-monotonic reasoning commu-nity has focussed on highly expressive logics. Such logics have tumed out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but ..."
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Cited by 69 (20 self)
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For many years, the non-monotonic reasoning commu-nity has focussed on highly expressive logics. Such logics have tumed out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient non-monotonic logic. We report on two new implemented systems for defeasible logic: a query answering system employing a backward-chaining approach, and a forward-chaining implementation that computes all conclusions. Our experimental evaluation demonstrates that the systems can deal with large theories (up to hundreds of thousands of rules). We show that defea-sible logic has linear complexity, which contrasts markedly with most other non-monotonic logics and helps to explain the impressive experimental results. We believe that defea-sible logic, with its eficiency and simplicity, is a good can-didate to be used as a modelling language for practical ap-plications, including modelling of regulations and business rules. 1
Temporalised normative positions in defeasible logic
- Procedings of the 10th International Conference on Artificial Intelligence and Law
, 2005
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An axiomatic account of formal argumentation
- In Proceedings of the AAAI-2005
, 2005
"... Argumentation theory has become an important topic in the field of AI. The basic idea is to construct arguments in favor and against a statement, to select the “acceptable ” ones and, finally, to determine whether the statement can be accepted or not. Dung’s elegant account of abstract argumentation ..."
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Cited by 64 (21 self)
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Argumentation theory has become an important topic in the field of AI. The basic idea is to construct arguments in favor and against a statement, to select the “acceptable ” ones and, finally, to determine whether the statement can be accepted or not. Dung’s elegant account of abstract argumentation (Dung 1995) may have caused some to believe that defining an argumentation formalism is simply a matter of determining how arguments and their defeat relation can be constructed from a given knowledge base. Unfortunately, things are not that simple; many straightforward instantiations of Dung’s theory can lead to very unintuitive results, as is discussed in this paper. In order to avoid such anomalies, in this paper we are interested in defining some rules, called rationality postulates or axioms, that govern the well definition of an argumentation system. In particular, we define two important rationality postulates that any system should satisfy: the consistency and the closeness of the results returned by that system. We then provide a relatively easy way in which these quality postulates can be warranted by our argumentation system.
Graduality in argumentation
- Journal of Artificial Intelligence Research
, 1998
"... Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce “graduality ” in the ..."
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Cited by 30 (0 self)
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Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce “graduality ” in the selection of the best arguments, i.e. to be able to partition the set of the arguments in more than the two usual subsets of “selected ” and “non-selected ” arguments in order to represent different levels of selection. Our basic idea is that an argument is all the more acceptable if it can be preferred to its attackers. First, we discuss general principles underlying a “gradual ” valuation of arguments based on their argumentation system. Then, we introduce “graduality ” in the concept of acceptability of arguments. We propose new acceptability classes and a refinement of existing classes taking advantage of an available “gradual ” valuation. 1.
