Leonardo LAMANNA

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.
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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.
 

 
Presentation of the first-year research activities (November 25, 2020)


 
 

Curriculum: Computer Science/Engineering and Control Systems

Tutor: Alfonso GEREVINI, Alessandro SAETTI, Paolo TRAVERSO, Luciano SERAFINI

email: l.lamanna@unibs.it

 

Link to the research group web page

Link to publication list

 

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