Trace-based multi-criteria preselection approach for decision making in interactive applications like video games

Abstract

The decision-making in games is essential to make them more automated and smart. A decision algorithm performs its calculations on the set of all the possible solutions. This increases the computation time and may become a combinatorial explosion problem if we have a huge solution space. To overcome this problem, we present our work on relevant solutions preselection before making a decision. We propose a two-steps strategy: i) the first step analyses the system's traces (users past executions) to identify all the potential solutions; ii) the second step aims to estimate the relevance, called utility, of each of these potential solutions. We get a set of alternative solutions that can be used as an input to any decision algorithm. We illustrate our approach on the Tamagotchi game.