Algorithms for argument systems

Argument systems are computational models that enable an artificial intelligent agent to reason via argumentation. Basically, the computations in argument systems can be viewed as search problems. In general, for a wide range of such problems existing algorithms lack five important features. Firstly, there is no comprehensive study that shows which algorithm among existing others is the most efficient in solving a particular problem. Secondly, there is no work that establishes the use of cost-effective heuristics leading to more efficient algorithms. Thirdly, mechanisms for pruning the search space are understudied, and hence, further pruning techniques might be neglected. Fourthly, diverse decision problems, for extended models of argument systems, are left without dedicated algorithms fine-tuned to the specific requirements of the respective extended model. Fifthly, some existing algorithms are presented in a high level that leaves some aspects of the computations unspecified, and therefore, implementations are rendered open to different interpretations. The work presented in this thesis tries to address all these concerns. Concisely, the presented work is centered around a widely studied view of what computationally defines an argument system. According to this view, an argument system is a pair: a set of abstract arguments and a binary relation that captures the conflicting arguments. Then, to resolve an instance of argument systems the acceptable arguments must be decided according to a set of criteria that collectively define the argumentation semantics. For different motivations there are various argumentation semantics. Equally, several proposals in the literature present extended models that stretch the basic two components of an argument system usually by incorporating more elements and/or broadening the nature of the existing components. This work designs algorithms that solve decision problems in the basic form of argument systems as well as in some other extended models. Likewise, new algorithms are developed that deal with different argumentation semantics. We evaluate our algorithms against existing algorithms experimentally where sufficient indications highlight that the new algorithms are superior with respect to their running time

Les systèmes d'argumentation basés sur les préférences : application à la décision et à la négociation

L'argumentation est considérée comme un modèle de raisonnement basé sur la construction et l'évaluation d'arguments. Ces derniers sont sensés soutenir/expliquer/attaquer des assertions qui peuvent être des décisions, des avis, etc... Cette thèse contient trois parties. La première concerne la notion d'équivalence de systèmes d'argumentation. Nous avons proposé différents critères d'équivalence, étudié leurs liens et montré sous quelles conditions deux systèmes sont équivalents selon les critères proposés. La notion d'équivalence est ensuite utilisée pour calculer les noyaux d'un système d'argumentation. Un noyau est un sous-système fini d'un système d'argumentation et équivalent à celui-ci. La deuxième partie de la thèse concerne l'utilisation des préférences dans l'argumentation. Nous avons étudié les rôles que les préférences peuvent jouer dans un système d'argumentation. Deux rôles particuliers ont été identifiés. Nous avons montré que les travaux existant ont abordé seulement le premier rôle et les approches proposées peuvent retourner des résultats contre-intuitifs lorsque la relation d'attaque entre arguments n'est pas symétrique. Nous avons développé une approche qui pallie ces limites. La troisième partie applique notre modèle d'argumentation à la décision et à la négociation. Nous avons proposé une instanciation de notre modèle pour la décision argumentée. Puis, nous avons étudié la dynamique de cette instanciation. Plus précisément, nous avons montré comment le statut des options change à la lumière d'un nouvel argument. Nous avons également employé notre modèle afin de montrer les avantages de l'argumentation dans des dialogues de négociation.Argumentation is a promising approach for reasoning with uncertain or incoherent knowledge or more generally with common sense knowledge. It consists of constructing arguments and counter-arguments, comparing the different arguments and selecting the most acceptable among them. This thesis contains three parts. The first one concerns the notion of equivalence between two argumentation frameworks. We studied two families of equivalence: basic equivalence and strong equivalence. We proposed different equivalence criteria, investigated their links and showed under which conditions two frameworks are equivalent w.r.t. each of the proposed criteria. The notion of equivalence is then used in order to compute the core(s) of an argumentation framework. A core of a framework is its compact version, i.e. an equivalent sub-framework. The second part of the thesis concerns the use of preferences in argumentation. We investigated the roles that preferences may play in an argumentation framework. Two particular roles were identified. Besides, we showed that almost all the existing works have tackled only the first role. Moreover, the proposed approaches suffer from a drawback which consists of returning conflicting extensions. We proposed a general approach which solves this problem and takes into account both roles of preferences. The third part illustrates our preference-based argumentation frameworks (PAF) in case of decision making and negotiation. We proposed an instantiation of our PAF which rank-orders options in a decision making problem and studied the dynamics of this model. We also used our PAF in order to show the benefits of arguing in negotiation dialogues

Graph Models for Rational Social Interaction

A fundamental issue in multi-agent systems is to extract a consensus from a group of agents with different perspectives. Even if the bilateral relationships (reflecting the outcomes of disputes, product comparisons, or evaluation of political candidates) are rational, the collective output may be irrational (e.g., intransitivity of group preferences). This motivates AI’s research for devising social outcomes compatible with individual positions. Frequently, such situations are modeled as graphs. While the preponderance of formal theoretical studies of such graph based-models has addressed semantic concerns for defining a desirable output in order to formalize some high-level intuition, results relating to algorithmic and computational complexity are also of great significance from the computational point of view. The first Part of this thesis is devoted to combinatorial aspects of Argumentation Frameworks related to computational issues. These abstract frameworks, introduced by Dung in 1995, are directed graphs with nodes interpreted as arguments and the directed edges as attacks between the arguments. By designing a conflict-resolution formalism to make distinction among acceptable and unacceptable arguments, Dung initiated an important area of research in Artificial Intelligence. I prove that any argumentation framework can be syntactically augmented into a normal form preserving the semantic properties of the original arguments, by using a cubic time rewriting technique. I introduce polyhedral labellings for an argumentation frameworks, which is a polytope with the property that its integral points are exactly the incidence vectors of specific types of Dung’s outcome. Also, a new notion of acceptability of arguments is considered – deliberative acceptability – and I provide it’s time computational complexity analysis. This part extends and improves some of the results from the my Master thesis. In the second Part, I introduce a novel graph-based model for aggregating preferences. By using graph operations to describe properties of the aggregators, axiomatic characterizations of aggregators corresponding to usual majority or approval & disapproval rule are given. Integrating Dung’s semantics into our model provides a novel qualitative approach to classical social choice: argumentative aggregation of individual preferences. Also, a functional framework abstracting many-to-many two-sided markets is considered: the study of the existence of a Stable Choice Matching in a Bipartite Choice System is reduced to the study of the existence of Stable Common Fixed Points of two choice functions. A generalization of the Gale-Shapley algorithm is designed and, in order to prove its correctness, a new characterization of path independence choice functions is given. Finally, in the third Part, we extend Dung’s Argumentation Frameworks to Opposition Frameworks, reducing the gap between Structured and Abstract Argumentation. A guarded attack calculus is developed, giving proper generalizations of Dung’s extensions.Ein grundlegendes Problem von Multiagentensystemen ist, eine Gruppe von Agenten mit unterschiedlichen Perspektiven zum Konsens zu bringen.W¨ahrend die bilaterale Ergebnisse von Rechtsstreitigkeiten, Produktvergleichen sowie die Bewertung von politischen Kandidaten wiederspiegelnden Beziehungen rational sein sollten, k¨onnte der kollektive Ausgang irrational sein z.B. durch die Intransitivit¨at von Pr¨aferenzen der Gruppe. Das motiviert die KI-Forschung zur Entwicklung von sozialen Ergebnissen, welche mit individuellen Einstellungen kompatibel sind. H¨aufig werden solche Situationen als Graphen modelliert. W¨ahrend die meisten formalen theoretischen Studien von Graphmodellen sich mit semantischen Aspekten f¨ur die Definition eines w¨unschenswerten Ausgangs zur Formalisierung auf hohem Intuitionsniveau besch¨aftigen, ist es ebenfalls von großer Bedeutung, die Komplexit¨at von Algorithmen und Berechnungen zu verstehen. Der erste Teil der vorliegenden Arbeit widmet sich den kombinatorischen Aspekten von Argumentation Frameworks im Zusammenhang mit rechnerischen Fragen. Diese von Dung in 1995 eingef ¨uhrten abstrakten Frameworks sind gerichtete Graphen mit als Argumenten zu interpretierenden Knoten, wobei die gerichteten Kanten Angriffe zwischen den Argumenten sind. Somit hat Dung mit seiner Gestaltung eines Konfliktl¨osungsformalismus zur Unterscheidung zwischen akzeptablen und inakzeptablen Argumenten f¨ur einen wichtigen Bereich von Forschung in KI den Grundstein gelegt. Die Verfasserin hat bewiesen, dass jedes Argumentation Framework sich in einer die semantischen Eigenschaften der originalen Argumente bewahrenden normalen Form syntaktisch erweitern l¨asst, indem man eine mit kubischer Laufzeit umwandelnde Technik verwendet. Neu eingef¨urt werden hier Polyhedrische Etiketten f¨ur Argumentation Frameworks. Dabei handelt es sich um einen Polytop, wessen ganze Punkte genau die Inzidenzvektoren von bestimmten Arten von Dungs Ausgabe sind. Weiterhin wird ein neuer Begriff der Akzeptanz von Argumenten gepr¨agt, n¨amlich - deliberative Akzeptanz - und dessen Komplexit¨at analysiert. Dieser Teil erweitert und verfeinert einige ihrer Ergebnisse aus der Masterarbeit. Im zweiten Teil wurde ein neuartiges graphenbasiertes Modell f¨ur die Aggregation von Pr¨aferenzen erarbeitet. Hier werden axiomatische Charakterisierungen von Aggregatoren neu eingef¨uhrt, und zwar durch die Verwendung von Graphoperationen zur Beschreibung der Eigenschaften von Aggregatoren. Sie entsprechen dem ¨ublichen Mehrheitsprinzip bzw. der Genehmigungs- & Ablehnungsregel. Einen neuartigen, qualitativen Ansatz im Vergleich zu der klassischen Sozialwahltheorie bietet die Integration der Semantik von Dung in dem neuen Modell, und zwar argumentative Aggregation individueller Pr¨aferenzen. Desweiteren wird ein funktionales many to many zweiseitige M¨arkte abstrahierendes Framework untersucht, indem statt die Existenz einer Stabilen Wahl Matching in einem Bipartite Wahlsystem zu studieren, wird die Existenz von Stable Common Fixed Points auf zwei Wahlfunktionen erforscht. Im n¨achsten Schritt wird eine neue Verallgemeinerung des Gale-Shapley Algorithmus entworfen und eine neue Charakterisierung der Wegunabh¨angigkeitsfunktion gegeben, die einen Korrektheitsbeweis f¨ur den Algorithmus erm¨oglicht. Im dritten Teil werden schließlich Dungs Argumentation Frameworks auf Opposition Frameworks erweitert und dadurch die in der gegenw¨artigen Forschung bestehende L¨ucke zwischen strukturierter und abstrakter Argumentation verringert. Daf¨ur wird ein bewachter Angriffskalk¨ul entwickelt, welches strikten Verallgemeinerungen von Dungs echten Erweiterungen f¨uhrt

Argumentation in biology : exploration and analysis through a gene expression use case

Argumentation theory conceptualises the human practice of debating. Implemented as computational argumentation it enables a computer to perform a virtual debate. Using existing knowledge from research into argumentation theory, this thesis investigates the potential of computational argumentation within biology. As a form of non-monotonic reasoning, argumentation can be used to tackle inconsistent and incomplete information - two common problems for the users of biological data. Exploration of argumentation shall be conducted by examining these issues within one biological subdomain: in situ gene expression information for the developmental mouse. Due to the complex and often contradictory nature of biology, occasionally it is not apparent whether or not a particular gene is involved in the development of a particular tissue. Expert biological knowledge is recorded, and used to generate arguments relating to this matter. These arguments are presented to the user in order to help him/her decide whether or not the gene is expressed. In order to do this, the notion of argumentation schemes has been borrowed from philosophy, and combined with ideas and technologies from arti cial intelligence. The resulting conceptualisation is implemented and evaluated in order to understand the issues related to applying computational argumentation within biology. Ultimately, this work concludes with a discussion of Argudas - a real world tool developed for the biological community, and based on the knowledge gained during this work

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

Argumentation-based dialogues over cooperative plans

If autonomous agents operating with other agents in open systems are to fulfil their goals and design objectives, the need to discuss and agree upon plans of action is imperative. In this thesis I present work covering both theoretical research and practical development related to the use of argumentation-based dialogues as a way to coordinate actions in multi-agent planning scenarios. The necessity of coordination in multi-agent systems requires the development of mechanisms to propose, modify, share, monitor, and argue about plans. In this thesis I present an argumentation scheme to propose multi-agent plans and associated critical questions to critique the proposal. Such a detailed consideration of multi-agent plan composition contains the right characteristics to enable the justification of plans.This research builds upon research on practical reasoning for action proposals and considers multi-agent plan proposals where plans require several agents for their execution. A dialogue game protocol is also presented which is based on proposal framework. The protocol allows agents to engage in dialogues to agree on and modify plans based on persuasion and deliberation protocols. The detail encompassed by the argumentation scheme and critical questions means that there is a large number of critical questions, and so dialogues may be very lengthy. To overcome this issue, I investigated the issue of strategies for use with this dialogue game in terms of the different possible orderings in which critiques can be posed. The thesis presents an implementation that realises the theoretical framework in terms of a agents engaging in simulated dialogues to share and agree on a plan. The experiments allow us to investigate the effects of such strategies in terms of the number of questions issued to reach an agreement. Overall, the framework presented in this thesis allow agents to engage in dialogues over cooperative plan proposals in a structured way using well-founded argumentative principles

Tracking and judging debates using argumentation.

Using argumentation to debate and reach conclusions is a particularly human activity relevant to many professions and applications. Debates exist not only in the Houses of Parliament, but also in such disciplines as medicine and law. In this theoretical thesis I explore three new logical constructs for realistic debate modelling, namely: confirmation, preclusion and reflection. Confirmation is two or more arguments for a claim, used to provide corroboration of evidence. Preclusion is an attacking argument that says 'one or other of your arguments is wrong' an argumentation technique used adeptly by Sherlock Holmes and many politicians. Reflection is a way of identifying logical redundancies (i.e. predictable patterns) in the argument data structure of a debate. A reflection originates from an unpredictable 'reflector' argument and gives rise to the predictable or 'reflected' argument. One type of reflection can be said to 'flow down' a tree of arguments, where the reflector is nearer the root and the reflected arguments further from the root, while another kind 'flows up' the tree in the reverse direction. Incorporating preclusion into the model of reflection increases this to four distinct types of reflection, two up-tree and two down-tree. The value of identifying and removing reflections is to ensure intuitive, or arguably 'correct', results when judging debates, be that judgement based on the existence or number of arguments. Re moving reflection also aids human comprehension of the debate as it reduces the number of arguments involved. This logical analysis of reflection and preclusion leads to the definition of a reflection-free, preclusion-aware, debate-tracking tree. Finally, the framework addresses judging the tree to determine who won the debate, with a proposal that takes confirmation into account when reaching conclusions. Confirmation assessment is helpful in resolving inconsistencies. Out of scope are notions of alternating moves by competing players and computational complexity