Luigi is a Postdoctoral Research Assistant in AI for Robot Decision-Making at the Oxford Robotics Institute (ORI).
His doctoral research focused on the field of AI called Automated Planning, which addresses the problem of deciding what to do to satisfy a set of goals. Specifically, He developed effective approaches for handling non-Markovian specifications on how goals are achieved.
Before joining the ORI, Luigi earned a Ph.D. in Information Engineering from the University of Brescia, where he also completed his Bachelor's and Master's degrees in Computer Science and Engineering.
Most Recent Publications
Planning for temporally extended goals in pure-past linear temporal logic
Planning for temporally extended goals in pure-past linear temporal logic
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)
FOND planning for pure-past linear temporal logic goals
FOND planning for pure-past linear temporal logic goals
Planning for temporally extended goals in pure-past linear temporal logic
Planning for temporally extended goals in pure-past linear temporal logic
On using lazy greedy best-first search with subgoaling relaxation in numeric planning problems
On using lazy greedy best-first search with subgoaling relaxation in numeric planning problems
Most Recent Publications
Planning for temporally extended goals in pure-past linear temporal logic
Planning for temporally extended goals in pure-past linear temporal logic
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)
FOND planning for pure-past linear temporal logic goals
FOND planning for pure-past linear temporal logic goals
Planning for temporally extended goals in pure-past linear temporal logic
Planning for temporally extended goals in pure-past linear temporal logic
On using lazy greedy best-first search with subgoaling relaxation in numeric planning problems
On using lazy greedy best-first search with subgoaling relaxation in numeric planning problems
