My research interests are in the area of Machine Learning and Evolutionary computation.
Genetics-Based Machine Learning
Genetics-Based Machine Learning (GBML) is a machine learning paradigm introduced by Holland in 1976 based on evolutionary computation. In this paradigm, the learning is viewed as a process of ongoing adaptation to an unknown environment which provides feedback in terms of numerical reward. The incoming reward is then used 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. My research in this area focused mainly on the following topics:
- theoretical analysis of the GBML systems
- design and extension of the classifier prediction model
- adapting the classifier prediction model to the problem
- GBML systems applied to the design space exploration of embedded systems
- implementation of GBML systems on GPUs
Computational Intelligence and Games
The Electronic Entertainment industry grew very fast and attracted a lot of researchers in the recent years. In this area, my research interests are articulated in two main directions: video games as testbed for Computa- tional Intelligence (CI) methods and the automatic game content generation.
Video Game as Testbed for CI
Modern video games are at the same time a fascinating application domain and an ideal testbed for the CI methods. My main contribution in this area is the design and the organization of the Simulated Car Racing Competition, a scientific competition where the goal is developing (by means of a CI approach) a controller for The Open Racing Car Simulator (TORCS), an open-source racing game. So far, the Simulated Car Racing has been used as research platform in approximately 20 published works (in proceedings of international conferences as well in international journals) in the game research community.
Automatic Game Content Generation
During the development of a modern game a major part of the avail- able resources is used to create the game content, such as the game mechanics, the environments and the characters. In order to develop ground-breaking new games, the industry is in need of reliable and effective tools for creating contents capable of engaging the customers. Moreover, the broadening of the customer base poses new additional challenges to the game industry and demands and for individualization to the abilities and needs of the single customer. In this scenario, my research interests involve the application of CI methods (i) to develop characters at the same time challenging and believable, (ii) to enable learning and adaptivity in games, and (iii) to generate game content, that is both innovative and entertaining.