My research interests are in the area of Machine Learning and Evolutionary computation.

Learning Classifier Systems (Genetics-Based Machine Learning)

Learning classifier systems are a machine learning paradigm introduced by Holland in 1976 based on evolutionary computation. In learning classifier systems, the learning is viewed a process of ongoing adaptation to an unknown environment which provides feedback in terms of numerical reward. Learning classifier systems use the incoming reward to guide the evolution of a population of condition-action-prediction rules, called classifiers, which represents the solution to the target problem. Each classifier represents a small piece of the overall solution: the condition identifies a problem subspace; the action represents a decision to take in the problem subspace identified by the classifier condition; the prediction estimates how valuable the classifier is in terms of problem solution. In particular, my research focused on classifier prediction models:

• how prediction model affect the overall performance?
• which prediction model should be used (e.g., neural networks or support vector machines) ?
• how to exploit the genetic algorithms to adapt the prediction model to the problem ?

Machine Learning for Modern Computer Games

Machine learning has many interesting application in modern computer games. The research aims to investigate the following issues:

• exploiting machine learning for modeling human players
• applying machine learning for developing interesting behaviors of non player characters
• using machine learning to adapt games to user preference and to improve game experience

More details will be available on the CIG@PoliMI web site.

Machine Learning in Finance

I am interested in the application of machine learning techniques for solving relevant problems in the financial domain. Two interesting examples are the portfolio optimization and the the one-way trading. Both these problems have been modeled as reinforcement learning problems and therefore they can be solved either with usual reinforcement learning or with genetics-based machine learning techniques, like learning classifier systems.

ML and EC for Embedded Systems Design

Today embedded system are widely used and more and more complex. This turns out in an increasing need of automated tool for the design of such systems. In general, the design of an embedded system involves the solution of several difficult and highly interdependent problems. In this scenario evolutionary computation and machine learning can be applied very effectively. My research focus is mainly on:

• multi-objective genetic algorithms for the design space exploration to deal with conflicting objectives
• probabilistic genetic algorithms for the resource allocation in the design of heterogeneous and reconfigurable embedded systems
• machine learning for modeling and estimating the cost and the performance of embedded systems