Active teachers noted persistent problems in their classrooms, including low levels of student engagement and gaps in students’ use of evidence in forming arguments. Earth Science students provided written responses to two questions using the previously implemented Claim, Evidence, Reasoning (CER) framework. The first question was answered without a preceding discussion whereas the second question was answered following a discussion protocol—no discussion, discussion without Talk Science, and discussion with Talk Science. A ninth grade teacher more comprehensively implemented Talk Science by incorporating statements from all four goals whereas a seventh grade teacher did not. As a result, ninth grade students improved their use of evidence, reasoning, and content, but seventh graders did not show the same improvements. All students valued the discussion, but this study shows that Talk Science discussions can be used to improve students’ content knowledge and CER argumentation skills.

This thesis develops a novel account of judicial common-law reasoning by precedent. If a new case is relevantly similar to a precedent case, judges are generally bound to follow the decision made in the precedent case. Important differences between cases can justify deciding the new case differently. The literature offers two main approaches to reasoning by precedent. According to rule-based-approaches, every case is decided by either following an existing rule or establishing a new one. I show that rule-based approaches are untenable. Analogy-based approaches claim that similarities and differences between two cases are determined through reasoning by analogy. These approaches are problematic because some similarity or difference can always be found between two cases. Accounts suggested so far cannot explain how precedents can provide significant guidance to judges. My dissertation salvages analogy-based approaches by supplementing them with insights from argumentation theory. Analogies contain a figurative part that is used to make someone see the analogy‘s literal part in a new way. An arguer can manipulate her interlocutor‘s perception of the literal part through the way she describes the figurative part by rhetorically drawing attention to those similarities that she considers relevant. Arguments by analogy use this to convince interlocutors of conclusions about the literal part. I propose to see judges in the role of interlocutors, evaluating arguments by precedent. The opinion that documents the precedent case from the point of view of the former judge is the figurative part of an analogy. The literal part is the new case. They form an analogical argument for repeating the precedent decision. The judge evaluates the argument by considering a number of critical questions. If all the critical questions can be answered, the precedent is applicable and must be followed. Otherwise, the precedent is either not applicable or has to be distinguished. / Dissertation / Doctor of Philosophy (PhD) / This thesis describes reasoning by precedent in the common law. I discuss two important approaches to how reasoning by precedent works, rule-based theories and analogy-based theories. I reject rule-based theories as untenable. I describe the main problem analogy-based theories face: To show that precedents can constrain judicial reasoning so that judges cannot decide cases according to their own normative commitments. I use insights from psychological research into analogical reasoning and from argumentation theory to develop a new analogy-based account. I suggest that judges should be seen as interlocutors evaluating an argument by precedent. This argument contains an analogy between precedent case and present case, and a rule stating that the precedent decision needs to be followed if precedent case and present case are legally the same. The judge needs to first understand the analogy under the application of the principle of charity, and then evaluate it using critical questions.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Denna uppsats har som syfte att undersöka hur Hans Rosling i sina TED Talks använder bildspel och vilken funktion de fyller i hans presentationer. Genom att analysera Roslings bildmaterial i TED-föreläsningen ”The best stats you’ve ever seen” (2006) med utgångspunkt i Perelmans och Olbrechts-Tytecas argumentationsmodeller exempel och illustrationer har uppsatsens mål varit att svara på tre frågor: (1) på vilket sätt argumenterar Rosling i sina föreläsningar? (2) hur bidrar det visuella i föreläsningarna till argumentationen? och (3) finns skillnader mellan hur programmet Gapminder World och de ”vanliga” bildspelsbilderna fungerar i argumentationen? Analysen visar att Rosling i hög grad använder sitt bildmaterial för att driva argument med argumentationsmodellen exempel, men också med argumentationsmodellen  illustrationer. Detta gäller främst när Roslings bildspel visar animerade diagram i Gapminder World. Till viss del kan metaforiska visuella framställningar återfinnas vilka också kan ses som argumenterande. När bildspelet till största del består av text fyller det visuella inte någon argumenterande funktion utan förtydligar och stödjer främst Roslings muntliga framförande. Genom denna analys kommer uppsatsen fram till att det visuella bidrar till att driva argumentationen framåt på ett lättförståeligt sätt.

I denna undersökning granskas 31 texter skrivna av elever i årskurs 7 höstterminen 2011. Samtliga texter är skrivna efter samma på förhand fastställda uppgift. Metodmässigt har texterna granskats genom en närläsning strukturerad av ett analysschema.  Undersökningen syftar till att besvara följande frågeställningar: - Vilka är de texttypsmässiga styrkorna och svagheterna i elevtexterna? - Hur ser samspelet ut mellan berättandet och argumenterandet i elevtexterna? - Vilka typer av kunskaper i argumentation visar sig i elevtexterna? Sammanfattningsvis visar studien att eleverna kan föra fram en tes och argument som stödjer tesen. Dock finns det utrymme för förbättring vad det gäller skapandet av disposition. Undersökningen visar också att en mindre del av eleverna valde att skriva en diskuterande text istället för en renodlad argumentation. Eleverna har i de flesta fall en god känsla för blandningen av den argumenterande och berättande texttypen. I diskussionen redogörs för hur texterna skulle kunna användas i ett formativt arbetssätt för att diskutera kunskaps-kvalitéer. / In this thesis 31 student texts are examined by close reading. The texts were written in seventh grade on the fall semester of 2011. The study of close reading were structured by an analytic scheme and aimed to answer the following questions: - Which are the strengths and weaknesses in the student’s text in relation to text type? -  How is the relationship between narrative and argumentative text types represented? -  Which signs of knowledge about written argument appear in the student’s texts?  The study shows that most students can formulate a thesis and argument to support it.   There is however room for improvement when it comes to the disposition of the text. The study also shows that some of the students wrote a text as a discussion rather than an argument. In most cases the students are successful in combining narrative and argumentative text types. The thesis is concluded by a discussion about how these results could be helpful in the frame of formative assessment and guide students to develop their argumentative skill.

Univ., Habil.-Schr.--Graz, 2003. / Literaturverz. S. [362] - 398.

Zugl.: Würzburg, Universiẗat, Diss., 2009.

[EN] Multi-Agent Systems (MAS), Argumentation and Automated Planning are three lines of investigations within the field of Artificial Intelligence (AI) that have been extensively studied over the last years. A MAS is a system composed of multiple intelligent agents that interact with each other and it is used to solve problems whose solution requires the presence of various functional and autonomous entities. Multi-agent systems can be used to solve problems that are difficult or impossible to resolve for an individual agent. On the other hand, Argumentation refers to the construction and subsequent exchange (iteratively) of arguments between a group of agents, with the aim of arguing for or against a particular proposal. Regarding Automated Planning, given an initial state of the world, a goal to achieve, and a set of possible actions, the goal is to build programs that can automatically calculate a plan to reach the final state from the initial state. The main objective of this thesis is to propose a model that combines and integrates these three research lines. More specifically, we consider a MAS as a team of agents with planning and argumentation capabilities. In that sense, given a planning problem with a set of objectives, (cooperative) agents jointly construct a plan to satisfy the objectives of the problem while they defeasibly reason about the environmental conditions so as to provide a stronger guarantee of success of the plan at execution time. Therefore, the goal is to use the planning knowledge to build a plan while agents beliefs about the impact of unexpected environmental conditions is used to select the plan which is less likely to fail at execution time. Thus, the system is intended to return collaborative plans that are more robust and adapted to the circumstances of the execution environment. In this thesis, we designed, built and evaluated a model of argumentation based on defeasible reasoning for planning cooperative multi-agent system. The designed system is independent of the domain, thus demonstrating the ability to solve problems in different application contexts. Specifically, the system has been tested in context sensitive domains such as Ambient Intelligence as well as with problems used in the International Planning Competitions. / [ES] Dentro de la Inteligencia Artificial (IA), existen tres ramas que han sido ampliamente estudiadas en los últimos años: Sistemas Multi-Agente (SMA), Argumentación y Planificación Automática. Un SMA es un sistema compuesto por múltiples agentes inteligentes que interactúan entre sí y se utilizan para resolver problemas cuya solución requiere la presencia de diversas entidades funcionales y autónomas. Los sistemas multiagente pueden ser utilizados para resolver problemas que son difíciles o imposibles de resolver para un agente individual. Por otra parte, la Argumentación consiste en la construcción y posterior intercambio (iterativamente) de argumentos entre un conjunto de agentes, con el objetivo de razonar a favor o en contra de una determinada propuesta. Con respecto a la Planificación Automática, dado un estado inicial del mundo, un objetivo a alcanzar, y un conjunto de acciones posibles, el objetivo es construir programas capaces de calcular de forma automática un plan que permita alcanzar el estado final a partir del estado inicial. El principal objetivo de esta tesis es proponer un modelo que combine e integre las tres líneas anteriores. Más específicamente, nosotros consideramos un SMA como un equipo de agentes con capacidades de planificación y argumentación. En ese sentido, dado un problema de planificación con un conjunto de objetivos, los agentes (cooperativos) construyen conjuntamente un plan para resolver los objetivos del problema y, al mismo tiempo, razonan sobre la viabilidad de los planes, utilizando como herramienta de diálogo la Argumentación. Por tanto, el objetivo no es sólo obtener automáticamente un plan solución generado de forma colaborativa entre los agentes, sino también utilizar las creencias de los agentes sobre la información del contexto para razonar acerca de la viabilidad de los planes en su futura etapa de ejecución. De esta forma, se pretende que el sistema sea capaz de devolver planes colaborativos más robustos y adaptados a las circunstancias del entorno de ejecución. En esta tesis se diseña, construye y evalúa un modelo de argumentación basado en razonamiento defeasible para un sistema de planificación cooperativa multiagente. El sistema diseñado es independiente del dominio, demostrando así la capacidad de resolver problemas en diferentes contextos de aplicación. Concretamente el sistema se ha evaluado en dominios sensibles al contexto como es la Inteligencia Ambiental y en problemas de las competiciones internacionales de planificación. / [CAT] Dins de la intel·ligència artificial (IA), hi han tres branques que han sigut àmpliament estudiades en els últims anys: Sistemes Multi-Agent (SMA), Argumentació i Planificació Automàtica. Un SMA es un sistema compost per múltiples agents intel·ligents que interactúen entre si i s'utilitzen per a resoldre problemas la solución dels quals requereix la presència de diverses entitats funcionals i autònomes. Els sistemes multiagente poden ser utilitzats per a resoldre problemes que són difícils o impossibles de resoldre per a un agent individual. D'altra banda, l'Argumentació consistiex en la construcció i posterior intercanvi (iterativament) d'arguments entre un conjunt d'agents, amb l'objectiu de raonar a favor o en contra d'una determinada proposta. Respecte a la Planificació Automàtica, donat un estat inicial del món, un objectiu a aconseguir, i un conjunt d'accions possibles, l'objectiu és construir programes capaços de calcular de forma automàtica un pla que permeta aconseguir l'estat final a partir de l'estat inicial. El principal objectiu d'aquesta tesi és proposar un model que combine i integre les tres línies anteriors. Més específicament, nosaltres considerem un SMA com un equip d'agents amb capacitats de planificació i argumentació. En aquest sentit, donat un problema de planificació amb un conjunt d'objectius, els agents (cooperatius) construeixen conjuntament un pla per a resoldre els objectius del problema i, al mateix temps, raonen sobre la viabilitat dels plans, utilitzant com a ferramenta de diàleg l'Argumentació. Per tant, l'objectiu no és només obtindre automàticament un pla solució generat de forma col·laborativa entre els agents, sinó també utilitzar les creences dels agents sobre la informació del context per a raonar sobre la viabilitat dels plans en la seua futura etapa d'execució. D'aquesta manera, es pretén que el sistema siga capaç de tornar plans col·laboratius més robustos i adaptats a les circumstàncies de l'entorn d'execució. En aquesta tesi es dissenya, construeix i avalua un model d'argumentació basat en raonament defeasible per a un sistema de planificació cooperativa multiagent. El sistema dissenyat és independent del domini, demostrant així la capacitat de resoldre problemes en diferents contextos d'aplicació. Concretament el sistema s'ha avaluat en dominis sensibles al context com és la inte·ligència Ambiental i en problemes de les competicions internacionals de planificació. / Pajares Ferrando, S. (2016). Defeasible Argumentation for Cooperative Multi-Agent Planning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/60159 / TESIS

In this thesis we are concerned with the role of formal argumentation in artificial intelligence, in particular in the field of knowledge engineering. The intuition behind argumentation is that one can reason with imperfect information by constructing and weighing up arguements intended to give support in favour or against alternative conclusions. In dynamic argumentation, such arguements may be revised and strengthened in order yo increase to decrease the acceptability of controversial positions. This thesis studies the theory, architecture, development and applications of formal arguementation systems from the procedural perspective of actually generating argumentation processes. First, the types of problems that can be tackled via the argumentation paradigm in knowledge engineering are characterised. Second, an abstract formal framework are built from an underlying set of axioms, represented here as executatble logic programs. Finally an architecture for dynamic arguementation systems is defined, and domain-specific applications are presented within different domaind, thus grounding problems with very distinctive characteristics into a similar source in argumentation. The methods and definitions desribed in this thesis have been assessed on various bases, including the reconstruction of informal arguements and of arguments captured by existing formalisms, the relation between our framework and these formalisms, and examples of dynamic argumentation applications in the safety-engineering and multi-agent domains.

Through cooperation, agents can transcend their individual capabilities and achieve goals that would be unattainable otherwise. Existing multiagent planning work considers each agent’s action capabilities, but does not account for distributed knowledge and the incompatible views agents may have of the planning domain. These divergent views can be a result of faulty sensors, local and incomplete knowledge, and outdated information, or simply because each agent has conducted different inferences and their beliefs are not aligned. This thesis is concerned with Multi-Perspective Cooperative Planning (MPCP), the problem of synthesising a plan for multiple agents which share a goal but hold different views about the state of the environment and the specification of the actions they can perform to affect it. Reaching agreement on a mutually acceptable plan is important, since cautious autonomous agents will not subscribe to plans that they individually believe to be inappropriate or even potentially hazardous. We specify the MPCP problem by adapting standard set-theoretic planning notation. Based on argumentation theory we define a new notion of plan acceptability, and introduce a novel formalism that combines defeasible logic programming and situation calculus that enables the succinct axiomatisation of contradictory planning theories and allows deductive argumentation-based inference. Our work bridges research in argumentation, reasoning about action and classical planning. We present practical methods for reasoning and planning with MPCP problems that exploit the inherent structure of planning domains and efficient planning heuristics. Finally, in order to allow distribution of tasks, we introduce a family of argumentation-based dialogue protocols that enable the agents to reach agreement on plans in a decentralised manner. Based on the concrete foundation of deductive argumentation we analytically investigate important properties of our methods illustrating the correctness of the proposed planning mechanisms. We also empirically evaluate the efficiency of our algorithms in benchmark planning domains. Our results illustrate that our methods can synthesise acceptable plans within reasonable time in large-scale domains, while maintaining a level of expressiveness comparable to that of modern automated planning.