Ricerc@Sapienza

Robotic Process Automation (RPA) is a fast-emerging automation technology in the field of Artificial Intelligence that allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human user on the interface of a computer system, and then emulate their enactment in place of the user. In this paper, after an in-depth experimentation of the RPA tools available in the market, we developed a classification framework to categorize them on the basis of some key dimensions.

Robotic Process Automation (RPA) is a fast-emerging automation technology
that sits between the fields of Business Process Management (BPM) and
Artificial Intelligence (AI), and allows organizations to automate high volume
routines. RPA tools are able to capture the execution of such routines
previously performed by a human users on the interface of a computer system,
and then emulate their enactment in place of the user by means of a software
robot. Nowadays, in the BPM domain, only simple, predictable business processes

An Ontology-based Data Access system is constituted by an ontology, namely a description of the concepts and the relations in a domain of interest, a database storing facts about the domain, and a mapping between the data and the ontology. In this paper, we consider ontologies expressed in the popular DL-Lite family of Description Logic, and we address the problem of computing explanations for answers to queries in an OBDA system, where queries are either positive, in particular conjunctive queries, or negative, i.e., negation of conjunctive queries.

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems.

Smart health (s-health) is an emerging paradigm that brings together a whole new range of digital data, both personal and non-personal, in order to deliver a holistic approach to health that overcomes the boundaries of the traditional patient caring system. By including non-personal smart city data, mobile s-health applications can improve prediction, prevention, and prescriptive care, while generating feedback that make cities smarter when accounting for and adapting to individual needs.

Mathematical modeling represents a useful instrument to study the evolution of an epidemic spread and to determine the best control strategy to reduce the number of infected subjects. The computation of the singular solution for a SIR epidemic system with vaccination control is performed; a constructive algorithm for the computation of a bang-singular-bang optimal solution is proposed and, for the specific choice of the parameters typical for a SIR model, the two switching instants, as well as the singular profile, are determined.

An optimal control problem formulated to reduce the infection diffusion of an epidemic disease is solved in a locally linearised context. The approach has been driven by the necessity of using a state observer, since the state variable to be controlled is not directly measurable.

Human computer interface (HCI) has become more and more important in the last few years. This is mainly due to the increase in the technology and in the new possibilities in yielding a help to disabled people. Brain Computer Interfaces (BCI) represent a subset of the HCI systems which use measurements of the voluntary brain activity for driving a communication system mainly useful for severely disabled people. Electroencephalography (EEG) has been intensively used for the measurement of electrical signals related to the brain activity.

We study planning for LTLf and LDLf temporally extended goals in nondeterministic fully observable domains (FOND). We consider both strong and strong cyclic plans, and develop foundational automata-based techniques to deal with both cases. Using these techniques we provide the computational characterization of both problems, separating the complexity in the size of the domain specification from that in the size of the formula. Specifically we establish them to be EXPTIME-complete and 2EXPTIME-complete, respectively, for both problems.

Agent supervision is a form of control/customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. To facilitate supervision, it is often of interest to consider hierarchical models where a high level abstracts over low-level behavior details. We study hierarchical agent supervision in the context of the situation calculus and the ConGolog agent programming language, where we have a rich first-order representation of the agent state.