18 citations · 82 across the 21 of their papers we have counts for
6 papers · 1 filter
Automatically Reinforcing a Game AI
David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu +2
A recent research trend in Artificial Intelligence (AI) is the combination of several programs into one single, stronger, program; this is termed portfolio methods. We here investi…
Learning opening books in partially observable games: using random seeds in Phantom Go
Tristan Cazenave, Jialin Liu, Fabien Teytaud +1
Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is…
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
Jialin Liu, Diego Pérez-Liébana, Simon M. Lucas
This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has…
Optimal resampling for the noisy OneMax problem
Jialin Liu, Michael Fairbank, Diego Pérez-Liébana +1
The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noi…
Scenario-based decision-making for power systems investment planning
Jialin Liu, Olivier Teytaud
The optimization of power systems involves complex uncertainties, such as technological progress, political context, geopolitical constraints. Negotiations at COP21 are complicated…
Bandit-Based Random Mutation Hill-Climbing
Jialin Liu, Diego Peŕez-Liebana, Simon M. Lucas
The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far…