1 citations · 1 across the 3 of their papers we have counts for
5 papers
Modelling Strategic Deceptive Planning in Adversarial Multi-Agent Systems
Lyndon Benke, Michael Papasimeon, Tim Miller
Deception is virtually ubiquitous in warfare, and should be a central consideration for military operations research. However, studies of agent behaviour in simulated operations ha…
Multi-Agent Simulation for AI Behaviour Discovery in Operations Research
Michael Papasimeon, Lyndon Benke
We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. Specifically, ACE0 was design…
Text Generation with Deep Variational GAN
Mahmoud Hossam, Trung Le, Michael Papasimeon +2
Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence…
Discrete-to-Deep Supervised Policy Learning
Budi Kurniawan, Peter Vamplew, Michael Papasimeon +2
Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have g…
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
Mahmoud Hossam, Trung Le, Viet Huynh +2
One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly gener…