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Formal Reasoning Methods

One of the main goals of Artificial Intelligence is to build intelligent systems with common sense reasoning capabilities. According to McCarthy [1960], "A [software] program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows".

In our research, we aim to develop and use formal reasoning theories such as Answer Set Programming and formal argumentation for building intelligent software programs with common sense reasoning capabilities.

From the theoretical point of view, we have extended and study different theories of non-monotonic reasoning, i.e. logic programming semantics with negation as failure and argumentation semantics.
We have extended non-monotonic reasoning paradigms such as Answer Set Programming and formal argumentation for capturing incomplete, uncertain and inconsistent information. For instance, we have combined Possibilistic Logic, Answer Set Programming (ASP) and formal argumentation. Our non-monotonic reasoning theories have been prototyped for implementing decision support systems in domains such as wastewater systems management, medical diagnosis and intelligent coaching systems for supporting human activities.

In the setting of autonomous agents and practical reasoning models such as the Belief Desires Intention (BDI) model, we have introduced different models of decision-making and deliberation. Moreover, we have introduced formal models for dealing with distributed decision decision-making processes in the settings of IoT, Smart Grid, and autonomous agents.

Currently, we are coining the paradigm of activity reasoning. Activity reasoning is a formal reasoning approach based on Activity Theory that aims to provide common sense reasoning capabilities to interactive and intelligent systems, which function as tools or collaborators in humans' activities.