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A large number of evaluation methods, called semantics, have been proposed in the literature for assessing strength of arguments. This paper investigates their equivalence. It argues that for being equivalent, two semantics should have compatible evaluations of both individual arguments and pairs of arguments. The first requirement ensures that the two semantics judge an argument in the same way, while the second states that they provide the same ranking of arguments. We show that the two requirements are completely independent. The paper introduces three novel relations between semantics based on their rankings of arguments: weak equivalence, strong equivalence and refinement. They state respectively that two semantics do not disagree on their strict rankings; the rankings of the semantics coincide; one semantics agrees with the strict comparisons of the second and it may break some of its ties. We investigate the properties of the three relations and their links with existing principles of semantics, and study the nature of relations between most of the existing semantics. The results show that the main extensions semantics are pairwise weakly equivalent. The gradual semantics we considered are pairwise incompatible, however some pairs are strongly equivalent in case of flat graphs including Max-based (Mbs) and Euler-based (Ebs), for which we provide full characterizations in terms respectively of Fibonacci numbers and the numbers of an exponential series. Furthermore, we show that both semantics (Mbs, EMbs) refine the grounded semantics, and are weakly equivalent with the other extension semantics. We show also that in case of flat graphs, the two gradual semantics Trust-based and Iterative Schema characterize the grounded semantics, making thus bridges between gradual semantics and extension semantics. Finally, the other gradual semantics are incompatible with extension semantics.

The paper tackled the issue of arguments evaluation in weighted bipolar argumen-tation graphs (i.e., graphs whose arguments have basic strengths, and may be both supported and attacked). We introduce principles that an evaluation method (or semantics) could satisfy. Such principles are very useful for understanding the foundations of semantics, judging them, and comparing semantics. We then analyze existing semantics on the basis of our principles, and finally propose a new semantics for the class of acyclic graphs. We show that it satisfies all the principles.

Rule-based argumentation systems are developed for reasoning about defeasible information. As a major feature, their logical language distinguishes between strict rules (encoding strict information) and defeasible rules (describing general behavior with exceptional cases). They build arguments by chaining such rules, define attacks between them, use a semantics for evaluating the arguments and finally identify the plausible conclusions that follow from the rules. Focusing on the family of inconsistency-based attack relations, this paper presents the first study of the outcomes of such systems under various acceptability semantics, namely naive, stable, semi-stable, preferred, grounded and ideal. It starts by extending the existing list of rationality postulates that any rule-based system should satisfy. Then, it defines the key notion of option of a theory (a theory being a set of facts, a set of strict rules and a set of defeasible rules). For each of the cited semantics, it characterizes the extensions of a rule-based system that satisfies all the postulates in terms of options of the theory under which the system is built. It also fully characterizes the set of plausible conclusions of the system. The results show that designing a rule-based argumentation system requires great care.

Rule-based argumentation systems are developed for reasoning about defeasible information. They take as input a theory made of a set of facts , a set of strict rules , which encode strict information, and a set of defeasible rules which describe general behavior with exceptional cases. They build arguments by chaining such rules, define attacks between them, use a semantics for evaluating the arguments, and finally identify the plausible conclusions that follow from the theory. Undercutting is one of the main attack relations of such systems. It consists of blocking the application of defeasible rules when their exceptional cases hold. In this paper, we consider this relation for capturing all the different conflicts in a theory. We present the first argumentation system that uses only undercutting, and show that it satisfies the rationality postulates proposed in the literature. Finally, we fully characterize both its extensions and its plausible conclusions under various acceptability semantics. Indeed, we show full correspondences between extensions and sub-theories of the theory under which the argumentation system is built.

International audience; Recently, the notion of similarity between arguments, namely those built using propositional logic, has been investigated and several similarity measures have been defined. This paper shows that those measures may lead to inaccurate results when arguments are not concise, i.e., their supports contain information that is useless for inferring their conclusions. For circumventing this limitation, we start by refining arguments for making them concise. Then, we propose two families of similarity measures that extend existing ones and that deal with concise arguments.

International audience; Argumentation aims at increasing acceptability of claims by supporting them with arguments. Roughly speaking, an argument is a set of premises intended to establish a definite claim. Its strength depends on the plausibility of the premises, the nature of the link between the premises and claim, and the prior acceptability of the claim. It may generally be weakened by other arguments that undermine one or more of its three components. Evaluation of arguments is a crucial task, and a sizable amount of methods, called semantics, has been proposed in the literature. This paper discusses two classifications of the existing semantics: the first one is based on the type of semantics’ outcomes (sets of arguments, weighting, and preorder), the second is based on the goals pursued by the semantics (acceptability, strength, coalitions).

International audience; Several argument-based logics have been defined for handling inconsistency in propositional knowledge bases. We show that they may miss intuitive consequences, and discuss two sources of this drawback: the definition of logical argument i) may prevent formulas from being justified, and ii) may allow irrelevant information in argument's support. We circumvent these two issues by considering a general definition of argument and compiling each argument. A compilation amounts to forgetting in an argument's support any irrelevant variable. This operation returns zero, one or several concise arguments, which we then use in an instance of Dung's abstract framework. We show that the resulting logic satisfies existing rationality postulates, namely consistency and closure under deduction. Furthermore, it is more productive than the existing argument-based and coherence-based logics.

This is an abridged version of a paper titled “Evaluation of ar-guments on weighted bipolar graphs” which won a best-paper awardat ECSQARU-17 conference.; International audience; The paper studies how arguments can be eval-uated inweighted bipolar argumentation graphs(i.e., graphs whose arguments have basic weightsand may be supported and attacked). It introducesprinciplesthat an evaluation method (orsemantics)would satisfy, analyzes existing semantics with re-spect to them, and finally proposes a new semanticsfor the class of non-maximal acyclic graph

International audience; In an argumentation setting, a semantics evaluates the overall acceptability of arguments. Consequently , it reveals the global loss incurred by each argument due to attacks. However, it does not say anything on the contribution of each attack to that loss. This paper introduces the novel concept of contribution measure for evaluating those contributions. It starts by defining a set of axioms that a reasonable measure would satisfy, then shows that the Shapley value is the unique measure that satisfies them. Finally, it investigates the properties of the latter under some existing semantics.

International audience; The paper studies semantics that evaluate arguments in argumentation graphs, where each argument has a basic strength, and may be attacked by other arguments. It starts by defining a set of principles , each of which is a property that a semantics could satisfy. It provides the first formal analysis and comparison of existing semantics. Finally, it defines three novel semantics that satisfy more principles than existing ones.

International audience; The aim of this paper is to evaluate to what extent an argumentation graph (a set of arguments and attacks between them) is conflicting. For that purpose , we introduce the novel notion of disagreement measure as well as a set of principles that such a measure should satisfy. We propose some intuitive measures and show that they fail to satisfy some of the principles. Then, we come up with a more discriminating measure which satisfies them all. Finally, we relate some measures to those quantifying inconsistency in knowledge bases.

International audience; This paper investigates when two abstract logic-based argumentation systems are equivalent. It defines various equivalence criteria, investigates the links between them, and identifies cases where two systems are equivalent with respect to each of the proposed criteria. In particular, it shows that under some reasonable conditions on the logic underlying an argumentation system, the latter has an equivalent finite subsystem, called core. This core constitutes a threshold under which arguments of the system have not yet attained their final status and consequently adding a new argument may result in status change. From that threshold, the statuses of all arguments become stable.

International audience; Dung’s (1995) argumentation framework takes as input two abstract entities: a set of arguments and a binary relation encoding attacks between these arguments. It returns acceptable sets of arguments, called extensions, w.r.t. a given semantics. While the abstract nature of this setting is seen as a great advantage, it induces a big gap with the application that it is used to. This raises some questions about the compatibility of the setting with a logical formalism (i.e., whether it is possible to instantiate it properly from a logical knowledge base), and about the significance of the various semantics in the application context. In this paper we tackle the above questions. We first propose to fill in the previous gap by extending Dung’s (1995) framework. The idea is to consider all the ingredients involved in an argumentation process. We start with the notion of an abstract monotonic logic which consists of a language (defining the formulas) and a consequence operator. We show how to build, in a systematic way, arguments from a knowledge base formalised in such a logic. We then recall some basic postulates that any instantiation should satisfy. We study how to choose an attack relation so that the instantiation satisfies the postulates. We show that symmetric attack relations are generally not suitable. However, we identify at least one ‘appropriate’ attack relation. Next, we investigate under stable, semi-stable, preferred, grounded and ideal semantics the outputs of logic-based instantiations that satisfy the postulates. For each semantics, we delimit the number of extensions an argumentation system may have, characterise the extensions in terms of subsets of the knowledge base, and finally characterise the set of conclusions that are drawn from the knowledge base. The study reveals that stable, semi-stable and preferred semantics either lead to counter-intuitive results or provide no added value w.r.t. naive semantics. Besides, naive semantics either leads to arbitrary results or generalises the coherence-based approach initially developed by Rescher and Manor (1970). Ideal and grounded semantics either coincide and generalise the free consequence relation developed by Benferhat, Dubois, and Prade (1997), or return arbitrary results. Consequently, Dung’s (1995) framework seems problematic when applied over deductive logical formalisms.

Argumentation is a promising approach for defeasible reasoning. It consists of justifying each plausible conclusion by arguments. Since the available information may be inconsistent, a conclusion and its negation may both be justified. The arguments are thus said to be conflicting. The main issue is how to evaluate the arguments. Several semantics were proposed for that purpose. The most important ones are: stable, preferred, complete, grounded and admissible. A semantics is a set of criteria that should be satisfied by a set of arguments, called extension, in order to be acceptable. Different decision problems related to these semantics were defined (like whether an argumentation framework has a stable extension). It was also shown that most of these problems are intractable. Consequently, developing algorithms for these problems is not trivial and thus the implementation of argumentation systems not obvious. Recently, some solutions to this problem were found. The idea is to use a reduction method where a given problem is translated in another one like SAT or ASP. This paper follows this line of research. It studies how to encode the problem of computing the extensions of an argumentation framework (under each of the previous semantics) as a constraint satisfaction problem (CSP). Such encoding is of great importance since it makes it possible to use the very efficient solvers (developed by the CSP community) for computing the extensions. Our encodings take advantage of existing reductions to SAT problems in the case of Dung's abstract framework. Among the various ways of translating a SAT problem into a CSP one, we propose the most appropriate one in the argumentation context. We also provide encodings in case two other families of argumentation frameworks: the constrained version of Dung's abstract framework and preference-based argumentation framework.

Dung's argumentation framework consists of a set of arguments and an attack relation among them. Arguments are evaluated and acceptable sets of them, called extensions, are computed using a given semantics. Each extension represents a coherent position. In the literature, several proposals have extended this framework in order to take into account the strength of arguments. The basic idea is to ignore an attack if the attacked argument is stronger than (or preferred to) its attacker. Semantics are then applied using only the remaining attacks. In this paper, we show that those proposals behave correctly when the attack relation is symmetric. However, when it is asymmetric, conflicting extensions may be computed leading to unintended conclusions. We propose an approach that guarantees conflict-free extensions. This approach presents two novelties: the first one is that it takes into account preferences at the semantics level rather than the attack level. The idea is to extend existing semantics with preferences. In case preferences are not available or do not conflict with the attacks, the extensions of the new semantics coincide with those of the basic ones. The second novelty of our approach is that a semantics is defined as a dominance relation on the powerset of the set of arguments. The extensions (under a semantics) are the maximal elements of the dominance relation. Such an approach makes it possible not only to compute the extensions of a framework but also to compare its non-extensions. We start by proposing three dominance relations that generalize respectively stable, preferred and grounded semantics with preferences. Then, we focus on stable semantics and provide full characterizations of its dominance relations and those of its generalized versions. Complexity results are provided. Finally, we show that an instance of the proposed framework retrieves the preferred sub-theories which were proposed in the context of handling inconsistency in weighted knowledge bases.

Decision making is usually based on the comparative evaluation of different options by means of a decision criterion. Recently, the qualitative pessimistic criterion was articulated in terms of a four-step argumentation process: (i) to build arguments in favour/against each option, (ii) to compare and evaluate those arguments, (iii) to assign a status for each option, and (iv) to rank order the options on the basis of their status. Thus, the argumentative counter-part of the pessimistic criterion provides not only the ‘best’ option to the user but also the reasons justifying this recommendation. The aim of this article is to study the dynamics of this argumentation model. The idea is to study how the ordering on options changes in light of a new argument. For this purpose, we study under which conditions an option may change its status, and under which conditions the new argument has no impact on the status of options, and consequently, on the ordering. This amounts to study how the acceptability of arguments evolves when the decision system is extended by new arguments. In the article, we focus on two acceptability semantics the skeptical grounded semantics and the credulous preferred semantics.

International audience; Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent/incomplete/uncertain knowledge, based on the construction and the comparison of arguments. In this paper, we apply this approach to the classification problem, whose purpose is to construct from a set of training examples a model that assigns a class to any new example. We propose a formal argumentation-based model that constructs arguments in favor of each possible classification of an example, evaluates them, and determines among the conflicting arguments the acceptable ones. Finally, a “valid” classification of the example is suggested. Thus, not only the class of the example is given, but also the reasons behind that classification are provided to the user as well in a form that is easy to grasp. We show that such an argumentation-based approach for classification offers other advantages, like for instance classifying examples even when the set of training examples is inconsistent, and considering more general preference relations between hypotheses. In the particular case of concept learning, the results of version space theory developed by Mitchell are retrieved in an elegant way in our argumentation framework. Finally, we show that the model satisfies the rationality postulates identified in argumentation literature. This ensures that the model delivers sound results.

An argument is seen as reason in favour of a claim. It is made of three parts: a set of premises representing the reason, a conclusion representing the supported claim, and a connection showing how the premises lead to the conclusion. Arguments are frequently exchanged by human agents in natural language (spoken or written) in discussion, debate, negotiation, persuasion, etc. They may be very different in that their three components may have various forms. In this paper, we propose a language for representing such arguments. We show that it is general enough to capture the various forms of arguments encountered in natural language, and that it is possible to represent attack and support relations between arguments as formulas of the same language.

International audience; Rule-based argumentation systems are developed for reasoning about defeasible information. They take as input a theory made of a set of strict rules, which encode strict information, and a set of defeasible rules which describe general behaviour with exceptional cases. They build arguments by chaining such rules, define attacks between them, use a semantics for evaluating the arguments, and finally identify the plausible conclusions that follow from the rules. One of the main attack relations of such systems is the so-called undercutting which blocks the application of defeasible rules in some contexts. In this paper, we show that this relation is powerful enough to capture alone all the different conflicts in a theory. We present the first argumentation system that uses only undercutting and fully characterize both its extensions and its plausible conclusions under various acceptability semantics.

International audience; This paper presents a target language for representing arguments mined from natural language. The key features are the connection between possible reasons and possible claims and recursive embedding of such connections. Given a base of these arguments and counterarguments mined from texts or dialogues, we want be able combine them, deconstruct them, and to analyse them (for instance to check whether the set is inconsistent). To address these needs, we propose a formal language for representing reasons and claims, and a framework for inferencing with the arguments and counterarguments in this formal language.

International audience; Logic-based argumentation systems are developed for reasoning with inconsistent information. Starting from a knowledge base encoded in a logical language, they define arguments and attacks between them using the consequence operator associated with the language. Finally, a semantics is used for evaluating the arguments. In this paper, we focus on systems that are based on deductive logics and that use Dung's semantics. We investigate rationality postulates that such systems should satisfy. We define five intuitive postulates: consistency and closure under the consequence operator of the underlying logic of the set of conclusions of arguments of each extension, closure under sub-arguments and exhaustiveness of the extensions, and a free precedence postulate ensuring that the free formulas of the knowledge base (i.e., the ones that are not involved in inconsistency) are conclusions of arguments in every extension. We study the links between the postulates and explore conditions under which they are guaranteed or violated.

This paper studies argumentation-based dialogues between agents. It defines a set of locutions by which agents can trade arguments, a set of agent attitudes which relate what arguments an agent can build and what locutions it can make, and a set of protocols by which dialogues can be carried out. The paper then considers some properties of dialogues under the protocols, in particular termination, dialogue outcomes, and complexity, and shows how these relate to the agent attitudes.

We propose a dialogue game protocol for purchase negotiation dialogues which identifies appropriate speech acts, defines constraints on their utterances, and specifies the different sub-tasks agents need to perform in order to engage in dialogues according to this protocol. Our formalism combines a dialogue game similar to those in the philosophy of argumentation with a model of rational consumer purchase decision behaviour adopted from marketing theory. In addition to the dialogue game protocol, we present a portfolio of decision mechanisms for the participating agents engaged in the dialogue and use these to provide our formalism with an operational semantics. We show that these decision mechanisms are sufficient to generate automated purchase decision dialogues between autonomous software agents interacting according to our proposed dialogue game protocol.

Argumentation is a promising approach to handle inconsistent knowledge bases, based on the justification of plausible conclusions by arguments. Because of inconsistency, however, arguments may be defeated by counterarguments (or defeaters). The problem is thus to select the most acceptable arguments. In this paper we investigate preference-based acceptability. The basic idea is to accept undefeated arguments and also arguments that are preferred to their defeaters. We say that these arguments defend themselves against their defeaters. We define argumentation frameworks based on that preference-based acceptability. Finally, we study associated inference relations for reasoning with inconsistent knowledge bases.

International audience; An argumentation framework is seen as a directed graph whose nodes are arguments and arcs are attacks between the arguments. Acceptable sets of arguments, called extensions, are computed using a semantics. Existing semantics are solely based on the attacks and do not take into account other important criteria like the intrinsic strengths of arguments. The contribution of this paper is three fold. First, we study how preferences issued from differences in strengths of arguments can help in argumentation frameworks. We show that they play two distinct and complementary roles: (i) to repair the attack relation between arguments, (ii) to refine the evaluation of arguments. Despite the importance of both roles, only the first one is tackled in existing literature. In a second part of this paper, we start by showing that existing models that repair the attack relation with preferences do not perform well in certain situations and may return counter-intuitive results. We then propose a new abstract and general framework which treats properly both roles of preferences. The third part of this work is devoted to defining a bridge between the argumentation-based and the coherence-based approaches for handling inconsistency in knowledge bases, in particular when priorities between formulae are available. We focus on two well-known models, namely the preferred sub-theories introduced by Brewka and the demo-preferred sets defined by Cayrol, Royer and Saurel. For each of these models, we provide an instantiation of our abstract framework which is in full correspondence with it.

International audience; During a dialogue, agents exchange information with each other and need thus to deal with incoming information. For that purpose, they should be able to reason effectively about trustworthiness of information sources. This paper proposes an argument-based system that allows an agent to reason about its own beliefs and information received from other sources. An agent's beliefs are of two kinds: beliefs about the environment (like the window is closed) and beliefs about trusting sources (like agent i trusts agent j). Six basic forms of trust are discussed in the paper including the most common one on sincerity. Starting with a base which contains such information, the system builds two types of arguments: arguments in favour of trusting a given source of information and arguments in favour of believing statements which may be received from other agents. We discuss how the different arguments interact and how an agent may decide to trust another source and thus to accept information coming from that source. The system is then extended in order to deal with graded trust (like agent i trusts to some extent agent j).

International audience; Several systems were developed for supporting public persuasion dialogs where two agents with conflicting opinions try to convince an audience. For computing the outcomes of dialogs, these systems use (abstract or structured) argumentation systems that were initially developed for nonmonotonic reasoning. Despite the increasing number of such systems, there are almost no work on high level properties they should satisfy. This paper is a first attempt for defining postulates that guide the well-definition of dialog systems and that allow their comparison. We propose six basic postulates (including e.g. the finiteness of generated dialogs). We then show that this set of postulates is incompatible with those proposed for argumentation systems devoted for nonmonotonic reasoning. This incompatibility confirms the differences between persuading and reasoning. It also suggests that reasoning systems are not suitable for computing the outcomes of dialogs.

International audience; An argumentation system consists of a set of interacting arguments and a semantics for evaluating them. This paper proposes a new family of semantics which rank-orders arguments from the most acceptable to the weakest one(s). The new semantics enjoy two other main features: i) an attack weakens its target but does not kill it, ii) the number of attackers has a great impact on the acceptability of an argument.We start by proposing a set of rational postulates that such semantics could satisfy, then construct various semantics that enjoy them.

The paper presents a parallel between two important theories for the treatment of information which address questions that are apparently unrelated and that are studied by different research communities: an enriched view of formal concept analysis and abstract argumentation. Both theories exploit a binary relation (expressing object-property links, attacks between arguments). We show that when an argumentation framework rather considers the complementary relation does not attack, then its stable extensions can be seen as the exact counterparts of formal concepts. This leads to a cube of oppositions, a generalization of the well-known square of oppositions, between eight remarkable sets of arguments. This provides a richer view for argumentation in cases of bi-valued attack relations and fuzzy ones. © 2013 Springer-Verlag Berlin Heidelberg.

This paper proposes the notion of experience to help situate agents in their environment, providing a link on how the continually evolving environment impacts the evolution of an agent's BDI model and vice versa. Then, using the notion of shared experience as a primitive construct, we develop a novel formal model of shared intention which we believe more adequately describes social behaviour than traditional BDI logics that focus on individual agents. Whilst many philosophers have argued that collective intentionality cannot always be equated to the collection of the individual agents' intentions, there has been no AI model that addresses this issue. We believe this is the first attempt to develop an explicit notion of shared experience from an AI perspective. © 2013 IEEE., This work is supported by: the ACE ERA-Net project; the CBIT project (funded by the Spanish Ministry of Science & Innovation TIN2010-16306); and the Agreement Technologies project (CONSOLIDER CSD 2007-0022, INGENIO 2010)

International audience; Starting from a typology of argumentative forms proposed in linguistics by Apothéloz, and observing that the four basic forms can be organized in a square of oppositions, we present a logical language, somewhat inspired from generalized possibilistic logic, where these basic forms can be expressed. We further analyze the interplay between the formulas of this language by means of two hexagons of oppositions. We then outline the inference machinery underlying this logic, and discuss its interest for argumentation.

Formal AI models of argumentation define arguments as reasons that support claims (which may be beliefs, decisions, actions, etc.). Such arguments may be attacked by other arguments. The main issue is then to identify the accepted ones. Several semantics were thus proposed for evaluating the arguments. Works in linguistics focus mainly on understanding the notion of argument, identifying its types, and describing different forms of counter-argumentation. This paper advocates that such typologies are instrumental for capturing real argumentations. It shows that some of the forms cannot be handled properly by AI models. Finally, it shows that the use of square of oppositions (a very old logical device) illuminates the interrelations between the different forms of argumentation. Copyright © 2012, IGI Global.

Logic-based argumentation systems are developed for reasoning with inconsistent information. They consist of a set of arguments, attacks among them and a semantics for the evaluation of arguments. Preferred semantics is favored in the literature since it ensures the existence of extensions (i.e., acceptable sets of arguments), and it guarantees a kind of maximality, accepting thus arguments whenever possible. This paper proposes the first study on the outcomes under preferred semantics of logic-based argumentation systems that satisfy basic rationality postulates. It focuses on systems that are grounded on Tarskian logics, and delimits the number of preferred extensions they may have. It also characterizes both their extensions and their sets of conclusions that are drawn from knowledge bases. The results are disappointing since they show that in the best case, the preferred extensions of a system are computed from the maximal consistent subbases of the knowledge base under study. In this case, the system is coherent, that is preferred extensions are stable ones. Moreover, we show that both semantics are useless in thic case since they ensure exactly the same result as naive semantics. Apart from this case, the outcomes of argumentation systems are counter-intuitive.

Many works have identified the potential benefits of using argumentation to address a large variety of multiagent problems. In this paper we take this idea one step further and develop the concept of a fully integrated argumentation-based agent architecture that allows us to develop agents that are coherently designed on an underlying argumentation based foundation. Under this architecture, an agent is composed of a collection of modules each of which is equipped with a local argumentation theory. Similarly, the intra-agent control of the agent is governed by local argumentation theories that are sensitive to the current situation of the agent through dynamically enabled feasibility arguments.

This paper proposes a system that allows a group of human users to share their cultural experiences online, like buying together a gift from a museum or browsing simultaneously the collection of this museum. We show that such application involves two multiple criteria decision problems for choosing between different alternatives (e.g. possible gifts): one at the level of each user, and one at the level of the group for making joint decisions. The former is made manually by the users via the WeShare interface. This interface displays an image with tags reflecting some features (criteria) of the image. Each user expresses then his opinion by rating the image and each tag. A user may change his choices in light of a report provided by his WeShare agent on the opinion of the group. Joint decisions are made in an automatic way. We provide a negotiation protocol which shows how they are reached. Both types of decisions are based on the notion of argument. Indeed, a tag which is liked by a user constitutes an argument pro the corresponding image whereas a tag which is disliked gives birth to a cons argument. These arguments may have different strengths since a user may express to what extent he likes/dislikes a given tag. Finally, the opinion analysis performed by a WeShare agent consists of aggregating the arguments of the users. © 2012 Springer-Verlag.

Practical reasoning (PR), which is concerned with the generic question of what to do, is generally seen as a two steps process: (1) deliberation, in which an agent decides what state of affairs it wants to reach – that is, its desires; and (2) means-ends reasoning, in which the agent looks for plans for achieving these desires. The agent’s intentions are a consistent set of desires that are achievable together.This paper proposes the first argumentation system for PR that computes in one step the possible intentions of an agent, avoiding thus the drawbacks of the existing systems. The proposed system is grounded on a recent work on constrained argumentation systems, and satisfies the rationality postulates identified in argumentation literature, namely the consistency and the completeness of the results.

We are interested by argumentation systems which build their arguments from a propositional knowledge base (KB), and evaluate them using Dung's acceptability semantics. We start by showing that such systems are infinite, i.e. from a finite KB, an infinite set of arguments and an infinite set of attacks among them are generated. While the construction of arguments under propositional logic is costly even in the finite case, the fact that those systems are infinite makes them completely useless. Then, we provide a procedure which, given an argumentation system, computes its finite sub-system, called core. A core considers a finite subset of arguments and a finite subset of attacks, and returns all the results of the original system. This means that a finite subset of arguments is sufficient to draw all the expected conclusions from a KB.

An argumentation system consists mainly of a set of interacting arguments and a semantics for evaluating them. In this paper, we study when two argumentation systems are \emph{equivalent}. We focus on argumentation systems defined around a Tarskian logic. We propose different equivalence criteria, study their links and finally show under which conditions two systems are equivalent wrt each of the proposed criteria.

In this paper, we show that preferences intervene twice in argumentation frameworks: i) to compute standard solutions (i.e. extensions), and ii) to refine those solutions (i.e. to return only the preferred extensions). The two roles are independent and obey to distinct postulates. After introducing and studying the postulates, we provide an example of a formal framework which models the two roles and verifies all the proposed postulates.

Equivalence between two argumentation systems means mainly that the two systems return the same outputs. It can be used for different purposes, namely in order to show whether two systems that are built over the same knowledge base but with distinct attack relations return the same outputs, and more importantly to check whether an infinite system can be reduced into a finite one. Recently, the equivalence between abstract argumentation systems was investigated. Two categories of equivalence criteria were particularly proposed. The first category compares directly the outputs of the two systems (e.g. their extensions) while the second compares the outputs of their extended versions (i.e. the systems augmented by the same set of arguments). It was shown that only identical systems are equivalent w.r.t. those criteria. In this paper, we study when two logic-based argumentation systems are equivalent. We refine existing criteria by considering the internal structure of arguments and propose new ones. Then, we identify cases where two systems are equivalent. In particular, we show that under some reasonable conditions on the logic underlying an argumentation system, the latter has an equivalent finite subsystem. This subsystem constitutes a threshold under which arguments of the system have not yet attained their final status and consequently adding a new argument may result in status change. From that threshold, the statuses of all arguments become stable.

The aim of this paper is to study how preferences, which are used to model intrinsic strengths of arguments, can be used in argumentation. We show that they play two roles: i) to repair the attack relation between arguments, and ii) to refine the evaluation of arguments. Then, we point out that the existing approaches for preference-based argumentation model only the first role. They may also return non conflict-free extensions. We propose a general framework that overcomes those limitations.

Argumentation is a promising approach for handling inconsistent knowledge bases, based on the justification of plausible conclusions by arguments. Due to inconsistency, arguments may be attacked by counterarguments. The problem is thus to evaluate the arguments in order to select the most acceptable ones. The aim of this paper is to make a bridge between the argumentation-based and the coherence-based approaches for handling inconsistency. We are particularly interested by the case where priorities between the formulas of an inconsistent knowledge base are available. For that purpose, we will use the rich preference-based argumentation framework (PAF) we have proposed in an earlier work. A rich PAF has two main advantages: i) it overcomes the limits of existing PAFs, and ii) it encodes two different roles of preferences between arguments (handling critical attacks and refining the evaluation of arguments). We show that there exist full correspondences between particular cases of these PAF and two well known coherence-based approaches, namely the preferred sub-theories and the democratic as well.

Argumentation has been acknowledged as a powerful mechanism for automated decision making. In this context several recent works have studied the problem of accommodating preference information in argumentation. The majority of these studies rely on Dung's abstract argumentation framework and its underlying acceptability semantics. In this paper we show that Dung's acceptability semantics, when applied to a preference-based argumentation framework for decision making purposes, may lead to counter intuitive results, as it does not take appropriately into account the preference information. To remedy this we propose a new acceptability semantics, called super-stable extension semantics, and present some of its properties. Moreover, we show that argumentation can be understood as a multiple criteria decision problem, making in this way results from decision theory applicable to argumentation.

In persuasion dialogs, agents exchange arguments on a subject on which they disagree. Thus, each agent tries to persuade the others to change their minds. Several systems, grounded on argumentation theory, have been proposed in the literature for modeling persuasion dialogs. It is important to be able to analyze the quality of these dialogs. Hence, quality criteria have to be defined in order to perform this analysis. This paper tackles this important problem and proposes one criterion that concerns the conciseness of a dialog. A dialog is concise if all its moves are relevant and useful in order to reach the same outcome as the original dialog. From a given persuasion dialog, in this paper we compute its corresponding "ideal" dialog. This ideal dialog is concise. A persuasion dialog is thus interesting if it is close to its ideal dialog.

Dung's argumentation system takes as input a set of arguments and a binary relation encoding attacks among these arguments, and returns different extensions of arguments. However, no indication is given on how to instantiate this setting, i.e. how to build arguments from a knowledge base and how to choose an appropriate attack relation. This leads in some cases to undesirable results like inconsistent extensions (i.e. the set of formulas forming an extension is inconsistent). This is due to the gap between the abstract setting and the knowledge base from which it is defined. The purpose of this paper is twofold: First it proposes to fill in this gap by extending Dung's system. The idea is to consider all the ingredients involved in an argumentation problem. We start with an abstract monotonic logic which consists of a set of formulas and a consequence operator. We show how to build arguments from a knowledge base using the consequence operator of the logic. Second, we show that the choice of an attack relation is crucial for ensuring consistent results, and should not be arbitrary. In particular, we argue that an attack relation should be at least grounded on the minimal conflicts contained in the knowledge base. Moreover, due to the binary character of this relation, some attack relations may lead to unintended results. Namely, symmetric relations are not suitable when ternary (or more) minimal conflicts are in the knowledge base. We propose then the characteristics of attack relations that ensure sound results.

Decision making amounts to define a preorder (usually a complete one) on a set of options. Argumentation has been introduced in decision making analysis. In particular, an argument-based decision system has been proposed recently by Amgoud et al. The system is a variant of Dung's abstract framework. It takes as input a set of options, different arguments and a defeat relation among them, and returns as outputs a status for each option, and a total preorder on the set of options. The status is defined on the basis of the acceptability of their supporting arguments. The aim of this paper is to study the revision of this decision system in light of a new argument. We will study under which conditions an option may change its status when a new argument is received and under which conditions this new argument is useless. This amounts to study how the acceptability of arguments evolves when the decision system is extended by new arguments.

The present paper unifies and develops the content of several conference papers [L. Amgoud, J.-F. Bonnefon, H. Prade, An argumentation-based approach to multiple criteria decision, in: Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'05), 2005, pp. 269–280; L. Amgoud, H. Prade, A bipolar argumentation-based decision framework, in: Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'06), 2006, pp. 323–330; L. Amgoud, H. Prade, Comparing decisions in an argumentation-based setting, in: Proceedings of the 11th International Workshop on Non-Monotonic Reasoning (NMR'06), 2006; L. Amgoud, H. Prade, Explaining qualitative decision under uncertainty by argumentation, in: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), 2006, pp. 219–224; International audience; Arguments play two different roles in day life decisions, as well as in the discussion of more crucial issues. Namely, they help to select one or several alternatives, or to explain and justify an already adopted choice. This paper proposes the first general and abstract argument-based framework for decision making. This framework follows two main steps. At the first step, arguments for beliefs and arguments for options are built and evaluated using classical acceptability semantics. At the second step, pairs of options are compared using decision principles. Decision principles are based on the accepted arguments supporting the options. Three classes of decision principles are distinguished: unipolar, bipolar or non-polar principles depending on whether i) only arguments pros or only arguments cons, or ii) both types, or iii) an aggregation of them into a meta-argument are used. The abstract model is then instantiated by expressing formally the mental states (beliefs and preferences) of a decision maker. In the proposed framework, information is given in the form of a stratified set of beliefs. The bipolar nature of preferences is emphasized by making an explicit distinction between prioritized goals to be pursued, and prioritized rejections that are stumbling blocks to be avoided. A typology that identifies four types of argument is proposed. Indeed, each decision is supported by arguments emphasizing its positive consequences in terms of goals certainly satisfied and rejections certainly avoided. A decision can also be attacked by arguments emphasizing its negative consequences in terms of certainly missed goals, or rejections certainly led to by that decision. Finally, this paper articulates the optimistic and pessimistic decision criteria defined in qualitative decision making under uncertainty, in terms of an argumentation process. Similarly, different decision principles identified in multiple criteria decision making are restated in our argumentation-based framework.

International audience