Ivan Bravi has obtained his B.Sc and M.Sc in Engineering of Computer Systems at the Politecnico di Milano, Italy.
From January to July 2016 he was Visiting Scholar at the NYU’s Game Innovation Lab in New York, under the supervision of Prof. Julian Togelius.
Since October 2017 he's an IGGI PhD student at Queen Mary University of London under the supervision of Simon Lucas.
Ivan has published several workshop and conference papers in different venues such as IJCAI, Evostar, CIG, FDG, AAAI and CoG.
Automatic playtesting of games can significantly streamline the process of designing, developing and releasing a game. It is also a possible application of Artificial General Intelligence (AGI): having a set of flexible algorithms that can play games regardless of their type decouples the two problems (playtesting and developing AGI algorithms) advancing both independently. When it comes to developing new AGI algorithms for game-playing a crucial characteristic is the ability of expressing different behaviours. Most of the research has focused on peak performance game-playing agents, this research project instead focuses on producing agents that are able to show different playing styles (behaviours) with no explicit domain information embedded in the algorithm.
Behavioural expressivity arises from the parameterisable components of an algorithm. In classical Statistical Forward Planning (SFP) it is very straightforward to adjust these, e.g. how far ahead it's planning. A very important component of SFP algorithms is the heuristic function used to evaluate the quality of game states. Being able to define heuristics in a game-agnostic manner is a key element in maintaining the algorithms generally.