The following projects can be either bachelor (triennale) or master (magistrale) thesis or even PhD opportunities. Some is more specific some is more open depending on the level of involvement of the student. All projects are at the frontier of automated planning and artificial intelligence in general. Students willing to cooperate have the chance to engage with an international research panorama for a better understanding of the science behind the computers and how this can be used to build intelligent systems. Our group is very active scientifically worldwide on all problems related to automated planning, scheduling and Artificial Intelligence in general.
Non exhaustive list of projects:
Title: Powerful Heuristics for State Space Search Planners
Abstract: Development of innovative relaxation-based heuristics in the context of hybrid planning. These heuristics can pave the way of a more prominent use of automated planning in real-world application, from robotics, urban traffic control, games and personalized medication. Attention is going to be paid to those numeric planning problems exhibiting state-dependent numeric dependencies between variables.
Supervisors: Enrico Scala
Title: Declarative Simulators and Controllersthrough PDDL+
Abstract: Development of an automatic translator from Simulink/Stateflow models (part of the Matlab environment) to PDDL+ planning. The translator aims to bridge the gap between industry prototyping and automated planning by converting dynamic system models into Planning Domain Definition Language (PDDL+) representations. This enables the application of advanced planning techniques to simulate and analyze model behavior. The project will involve identifying a fragment of relevant Simulink/Stateflow operators, defining a translation algorithm, implementing the translator, and validating its effectiveness on a range of models. The work by this thesis holds the promise to enable the automated verification and analysis of complex dynamical systems through the use of automated planning systems.
Supervisors: Diego Aineto, Ivan Serina, Enrico Scala
Title: Action Model Learning for Planning
Abstract: Design and theoretical study of an approach that learns automatically models of the environment expressed using action centered formalisms based on first-order-logic. This work aims at deepening our understanding on what can be learnt and with which guarantees. The action model learning takes as an input some flow of demonstrations that collect the experience of the agent. The ouput is a model that can be used by any off-the-shelf planner. Emphasis will be given to expressive models of the world
Supervisors: Diego Aineto, Enrico Scala
Title: Model-Free Planning
Abstract: Design of an algorithm that solves planning problem with no model description given as an input. The idea is to navigate the search space of the planning problem using a simulator and devise heuristics on the basis of the information collected during search. Heuristics can be either syntactic or the result of an incumbent planning model that is learnt whilst palnning.
Supervisors: Diego Aineto, Enrico Scala
Title: Hybrid Planning with Trajectory Constraints
Abstract: In many domains, agents have to deal with trajectory constraints all along the way, for instance to enforce that the battery is up for the entire course of the activities, or perhaps that the connection link is open while some operation is carried on. To take into account this problem, one can use metric temporal constraints. In this thesis we expect to advance the state of the art in all the technlogies fo the automated synthesis of plans that take all trajectory constraints into account too. We are aiming to look at signal temporal logic formulae and PDDL+ representation languages, but other alternatives can be considered.
Supervisors: Diego Aineto, Enrico Scala