David M. Bossens

26 papers A* 2A 1Journal 18Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Lifetime policy reuse and the importance of task capacity. AI Commun. David M. Bossens, Adam J. Sobey
2024	—	`conf`	Low Variance Off-policy Evaluation with State-based Importance Sampling. CAI David M. Bossens, Philip S. Thomas
2024	J	`jnl`	Quantum Policy Gradient in Reproducing Kernel Hilbert Space. CoRR David M. Bossens, Kishor Bharti, Jayne Thompson
2024	—	`conf`	Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes. CAI David M. Bossens
2024	J	`jnl`	The Digital Ecosystem of Beliefs: does evolution favour AI over humans? CoRR David M. Bossens, Shanshan Feng, Yew-Soon Ong
2023	J	`jnl`	Explicit Explore, Exploit, or Escape (E Mach. Learn. David M. Bossens, Nicholas Bishop
2023	—	`ed.`	Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023) co-located with the 32nd International Joint Conference on Artificial Intelligence(IJCAI2023), Macau, China, August 21-22, 2023. AISafety/SafeRL@IJCAI Gabriel Pedroza, Xiaowei Huang, Xin Cynthia Chen, Andreas Theodorou, Huáscar Espinoza, Nikolaos Matragkas, José Hernández-Orallo, Mauricio Castillo-Effen, Richard Mallah, John A. McDermid, David M. Bossens, Bettina Könighofer, Sebastian Tschiatschek, Anqi Liu
2023	J	`jnl`	Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes. CoRR David M. Bossens
2022	J	`jnl`	Low Variance Off-policy Evaluation with State-based Importance Sampling. CoRR David M. Bossens, Philip Thomas
2022	J	`jnl`	Quality-Diversity Meta-Evolution: Customizing Behavior Spaces to a Meta-Objective. IEEE Trans. Evol. Comput. David M. Bossens, Danesh Tarapore
2022	J	`jnl`	Resilient robot teams: a review integrating decentralised control, change-detection, and learning. CoRR David M. Bossens, Sarvapali D. Ramchurn, Danesh Tarapore
2022	J	`jnl`	Trust in Language Grounding: a new AI challenge for human-robot teams. CoRR David M. Bossens, Christine Evers
2021	A*	`conf`	ASVLite: a high-performance simulator for autonomous surface vehicles. ICRA Toby Thomas, David M. Bossens, Danesh Tarapore
2021	J	`jnl`	Explicit Explore, Exploit, or Escape (E CoRR David M. Bossens, Nicholas Bishop
2021	J	`jnl`	Lifetime policy reuse and the importance of task capacity. CoRR David M. Bossens, Adam J. Sobey
2021	J	`jnl`	On the use of feature-maps and parameter control for improved quality-diversity meta-evolution. CoRR David M. Bossens, Danesh Tarapore
2021	—	`conf`	On the use of feature-maps for improved quality-diversity meta-evolution. GECCO Companion David M. Bossens, Danesh Tarapore
2021	J	`jnl`	QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms. IEEE Trans. Evol. Comput. David M. Bossens, Danesh Tarapore
2021	J	`jnl`	Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective. CoRR David M. Bossens, Danesh Tarapore
2021	A*	`conf`	Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. ICRA David M. Bossens, Danesh Tarapore
2020	J	`jnl`	ASV-Swarm: a high-performance simulator for the dynamics of a swarm of autonomous marine vehicles in waves. CoRR Toby Thomas, David M. Bossens, Danesh Tarapore
2020	A	`conf`	Learning behaviour-performance maps with meta-evolution. GECCO David M. Bossens, Jean-Baptiste Mouret, Danesh Tarapore
2020	J	`jnl`	QED: using Quality-Environment-Diversity to evolve resilient robot swarms. CoRR David M. Bossens, Danesh Tarapore
2020	J	`jnl`	Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. CoRR David M. Bossens, Danesh Tarapore
2020	—	`—`	Reinforcement learning with limited prior knowledge in long-term environments. David M. Bossens
2019	J	`jnl`	Learning to learn with active adaptive perception. Neural Networks David M. Bossens, Nicholas C. Townsend, Adam J. Sobey