When: Thursday, February 4, 2021, 3pm (GMT+1).
Where: Zoom online, check the address in the Google Calendar Event.
Topic: Planning in Smart Manufacturing.
Speaker: Francesco Leotta
Planning in Smart Manufacturing
When: Thursday, January 28, 2021, 3pm (GMT+1).
Where: Zoom online, check the address in the Google Calendar Event.
Topic: Fully Compositional LTLf/LDLf to DFA.
Speaker: Marco Favorito
Fully Compositional LTLf/LDLf to DFA
When: Thursday, July 2, 2020, 3pm.
Where: Google Meet, check the address in the Google Calendar Event.
Topic: Introduction to LTLf/LDLf to Automata Algorithms.
Speaker: Marco Favorito
Introduction to LTLf/LDLf to Automata Algorithms.
When: Thursday, June 18, 2020, 3pm.
Where: Google Meet, check the address in the Google Calendar Event.
Topic: Learning Restraining Bolts by Imitation with L*.
Speaker: Fabio Patrizi
Learning Restraining Bolts by Imitation with L*.
When: Thursday, June 11, 2020, 3pm.
Where: Google Meet, check the address in the Google Calendar Event.
Topic: Program Synthesis of Linear Temporal Logic over Finite Traces.
Speaker: Shufang Zhu
Program synthesis is an approach of automatically designing a system that interacts continuously with an environment, using a declarative specification of the system’s behaviors. A popular language for providing such a specification is Linear Temporal Logic, or LTL for short. Standard LTL synthesis, however, is a hard problem to solve in practice. Therefore, many works have targeted on developing specific techniques for certain subclasses of LTL and obtained significant results, for example, GR(1). In this work, we focus on a new logic, Linear Temporal Logic over finite traces, or LTLf, which interprets LTL formulas semantically on finite traces. Such interpretation allows for arbitrarily long but finite executions of the system, and is adequate for finite-horizon problems in Computer Science and Artificial Intelligence. In particular, we study here the problem of LTLf synthesis. The contributions of this dissertation are summarized as follows:
When: Thursday, June 4, 2020, 3pm.
Where: Google Meet, check the address in the Google Calendar Event.
Topic: Goal Recognition over Imperfect Domain Models.
Speaker: Ramon F. Pereira
Goal recognition is the problem of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this thesis, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well-defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evaluation over these two types of imperfect domains models show that our novel goal recognition approaches are accurate in comparison to baseline approaches from the literature, at several levels of observability and imperfections.