Purchasing products from online e-stores is getting popular with the advance of Internet infrastructure and network security. At current stage, most e-stores resemble vending machines rather than real stores because they lack clerks to persuade prospects into buying products and to bargain with the customers for making a good deal. This research aims to design an easy-to-use and autonomous sales-agent, called Isa, to act as a virtual clerk in an e-store. A new approach is proposed to enable the agent to dynamically adopt different persuasion and negotiation strategies according to different characteristics of human buyers. Additionally, this approach enables a sales-agent to learn the best strategies without seller¡¦s instructions. Both laboratory and field experiments are conducted to assess Isa¡¦s performance. The experimental results reveal that Isa can improve a seller¡¦s surplus and increase a buyer¡¦s product evaluation, willingness to pay more money for the product, and satisfaction with visiting the s-store.

This work undertakes an analysis of problems in the information model by which biomedical research is communicated on the Web, and proposes a semantic model by which these problems can be resolved. It uses and develops Activity Theory and Argumentation Theory as tools in this analysis, and produces a semantic model of biomedical communication on the Web, in OWL2, which it shows can be applied to current research articles and implemented in software. It makes contributions in three areas. This work contributes to Activity Theory, a model used in the Human-Computer Interaction (HCI) and Computer-Supported Collaborative Work (CSCW) domains, by resolving ambiguities and formalizing concepts previously obscure in the theory, and by reformulating it as an Activity Views Model. It contributes to Argumentation Theory, used in AI and Communications Theory, by integrating the work of Toulmin, Dung, and others, and applying it specifically to construct a semantic model of biomedical argumentation, which may be more generally applicable in scientific communications. And it contributes to improving scientific communications on the web, by developing a practical semantic model of biomedical communications, as arguments grounded in reproducible methods, materials and data, in OWL2.Lastly, this work demonstrates that our model can be (a) applied consistently to examples from the biomedical literature, with serialization in RDF; (b) applied independently and successfully, by biomedical research workers not specially trained in informatics; and (c) having published the model as an ontology, that it has been implemented in software, and is capable of further useful application in the biomedical communications ecosystem by others.

Argumentation framework is a common technique in Artificial Intelligence and related fields. It is a good way of formalizing, resolving conflicts and helping with defeasible reasoning. This thesis discusses the exploration of the quantitative bipolar argumentation framework applied in multi-agent systems. Different agents in a multi-agent systems have various capabilities, and they contribute in different ways to the system goal. The purpose of this study is to explore approaches of explaining the overall behavior and output from a multi-agent system and enable explainability in the multi-agent systems. By exploring the properties of the quantitative bipolar argumentation framework using some techniques from explainable Artificial Intelligence (AI), the system will generate output with explanations given by the argumentation framework. This thesis gives a general overview of argumentation frameworks and common techniques from explainable AI. The study mainly focuses on the exploration of properties and interactive algorithms of quantitative bipolar argumentation framework. It introduces explanation techniques to the quantitative bipolar argumentation framework. A Graphical User Interface (GUI) application is included in order to present the results of the explanation. / Argumentationsramar är en vanlig teknik inom artificiell intelligens och relaterade områden. Det är ett bra sätt att formalisera, lösa konflikter och hjälpa till med defekta resonemang. I den här avhandlingen diskuteras utforskningen av den kvantitativa bipolära argumentationsramen som tillämpas i fleragentsystem. Olika agenter i ett system med flera agenter har olika kapacitet och bidrar på olika sätt till systemets mål. Syftet med den här studien är att utforska metoder för att förklara det övergripande beteendet och resultatet från ett system med flera agenter och möjliggöra förklarbarhet i systemen med flera agenter. Genom att utforska egenskaperna hos den kvantitativa bipolära argumentationsramen med hjälp av vissa tekniker från förklaringsbara AI kommer systemet att generera utdata med förklaringar som ges av argumentationsramen. Denna avhandling ger en allmän översikt över argumentationsramar och vanliga tekniker från förklaringsbara AI. Studien fokuserar främst på utforskandet av egenskaper och interaktiva algoritmer för det kvantitativa bipolära argumentationsramverket och introducerar tillämpningen av förklaringstekniker på det kvantitativa bipolära argumentationsramverket. En GUI-applikation ingår för att presentera resultaten av förklaringen.