Learning to learn with active adaptive perception. - David M. Bossens; Nicholas C. Townsend; Adam J. Sobey (Neural Networks, 2019)

Learning behaviour-performance maps with meta-evolution. - David M. Bossens; Jean-Baptiste Mouret; Danesh Tarapore (GECCO, 2020)

ASV-Swarm: a high-performance simulator for the dynamics of a swarm of autonomous marine vehicles in waves. - Toby Thomas; David M. Bossens; Danesh Tarapore (CoRR, 2020)

Reinforcement learning with limited prior knowledge in long-term environments. - David M. Bossens (2020)

On the use of feature-maps for improved quality-diversity meta-evolution. - David M. Bossens; Danesh Tarapore (GECCO Companion, 2021)

Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. - David M. Bossens; Danesh Tarapore (ICRA, 2021)

ASVLite: a high-performance simulator for autonomous surface vehicles. - Toby Thomas; David M. Bossens; Danesh Tarapore (ICRA, 2021)

On the use of feature-maps and parameter control for improved quality-diversity meta-evolution. - David M. Bossens; Danesh Tarapore (CoRR, 2021)

Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective. - David M. Bossens; Danesh Tarapore (CoRR, 2021)

QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms. - David M. Bossens; Danesh Tarapore (IEEE Trans. Evol. Comput., 2021)

Resilient robot teams: a review integrating decentralised control, change-detection, and learning. - David M. Bossens; Sarvapali D. Ramchurn; Danesh Tarapore (CoRR, 2022)

Trust in Language Grounding: a new AI challenge for human-robot teams. - David M. Bossens; Christine Evers (CoRR, 2022)

Low Variance Off-policy Evaluation with State-based Importance Sampling. - David M. Bossens; Philip Thomas (CoRR, 2022)

Quality-Diversity Meta-Evolution: Customizing Behavior Spaces to a Meta-Objective. - David M. Bossens; Danesh Tarapore (IEEE Trans. Evol. Comput., 2022)

Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes. - David M. Bossens (CoRR, 2023)

Explicit Explore, Exploit, or Escape (E4): near-optimal safety-constrained reinforcement learning in polynomial time. - David M. Bossens; Nicholas Bishop (Mach. Learn., 2023)

Lifetime policy reuse and the importance of task capacity. - David M. Bossens; Adam J. Sobey (AI Commun., 2024)

Quantum Policy Gradient in Reproducing Kernel Hilbert Space. - David M. Bossens; Kishor Bharti; Jayne Thompson (CoRR, 2024)

The Digital Ecosystem of Beliefs: does evolution favour AI over humans? - David M. Bossens; Shanshan Feng 001; Yew-Soon Ong (CoRR, 2024)