Leonardo Lamanna

Research Topics

Artificial Intelligence and Machine Learning for Autonomous Agents That Learn to Plan and Operate in Unpredictable Dynamic Environments

My major research activity focuses on the integration of acting, learning and planning. The main objective is to build a system that is capable to learn how to plan and act in a dynamic and complex environment. On the learning side, I’m interested in developing algorithms that allow an artificial agent to learn an abstract model of the dynamics of the environment (e.g. an explicit model like a deterministic finite state machine or a model description in a language to express planning domains). In addition to learning the abstract model, I’m interested in learning probabilistic (generative) models that connects the abstract model with the perceptions of the artificial agents. On the acting and planning side, the artificial agent decides how to act by means of state-of-art planners (e.g. Fastforward). With its own experience, it enriches the planner knowledge, as well as the learned model of the environment.

A New Kernel Search variant for the Multidimensional Multiple-choice Knapsack Problem

The Multidimensional Multiple Choice Knapsack Problem (MMKP) is a complex combinatorial optimization problem for which finding high quality feasible solutions is a very challenging task. Despite several heuristic approaches have been proposed for its solution, many benchmark instances for the MMKP still remain unclosed to optimality. I developed a new variant of the well-known heuristic framework called Kernel Search and applied it to the MMKP.

Presentations

First year presentation

Bio

Leonardo Lamanna was born on 22nd April 1996, Italy. He graduated in bachelor computer engineering in 13th September 2017 with the final grade of 90/110 at Università degli Studi di Brescia. His batchelor’s Thesis was about applying a optimization metaheuristic to solve a NP-hard problem. Then he graduated in master computer engineering in 11th September 2019 with the final grade of 110/110 cum laude at Università degi Studi di Brescia. His master’s Thesis was about integrating Mathematical Programming and Artificial Intelligence techniques to solve integer linear optimization problems. He is a Computer Science Ph.D. student in collaboration with the Fondazione Bruno Kessler (FBK) research center in Trento. His Ph.D. topic is the integration of learning and planning in dynamic environments.