Building Intelligent Agents able to take decisions with vague, conflicting and uncertain Information

Sciences Without Borders Project
Two ubiquitous characteristics of human decisions are the following: they often happen in a situation of conflicting information and the concepts involved in the process are often vague and partially true.
Practitioners in AI have developed sophisticated intelligent systems to manage uncertainty in decisions-making. Multi-valued and fuzzy logic are suitable to model vague and partially true information, while recent works in Abstract Argumentation Theory have produced a set of elegant and sound semantics to manage conflicts resolution between conflicting evidence.
However, abstract argumentation is only able to deal with Boolean evidence, and it has little or no support for any sort of fuzzy, uncertain or partially true argument. On the other side, fuzzy logic systems do not embed any mechanisms of conflict resolution comparable with the ones produced by abstract argumentation.
The aim of this PhD project is to extend the state-of-the-art of reasoning system by investigating how fuzzy reasoning can be integrated into abstract argumentation theory. The challenges are to define a framework with the following characteristics: sound in the decisions produced, able to explain and justify its decisions, able to integrate different multi-valued logic semantics, and computationally feasible. The framework will be integrated with complementary uncertainty management approach, such as probability and possibility theory.
Implemented versions of the framework will be evaluated in a diverse set of fields such as clinical decision making process and financial risk assessment.