Search Results for author:
Found 11 papers, 4 papers with code
Resilient robot teams: a review integrating decentralised control, change-detection, and learning
Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective
To illuminate the elite solutions for a space of behaviours, QD algorithms require the definition of a suitable behaviour space.
Large-scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints
The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload.
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution
In Quality-Diversity (QD) algorithms, which evolve a behaviourally diverse archive of high-performing solutions, the behaviour space is a difficult design choice that should be tailored to the target application.
Multi-Agent Routing and Scheduling Through Coalition Formation
Our results constitute the first large-scale benchmark for the MARSC problem.
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
We also investigate disturbances in the operating environment of the swarm, where the swarm has to adapt to drastic changes in the number of resources available in the environment, and to one of the robots behaving disruptively towards the rest of the swarm, with 30 unique conditions for each such perturbation.
Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints
Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be anytime, efficient and with provable guarantees, called Cluster-based Coalition Formation (CCF).
QED: using Quality-Environment-Diversity to evolve resilient robot swarms
To allow fault recovery from randomly injected faults to different robots in a swarm, a model-free approach may be preferable due to the accumulation of faults in models and the difficulty to predict the behaviour of neighbouring robots.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
Evolvability signatures of generative encodings: beyond standard performance benchmarks
and fitness values (how different is the fitness after a mutation?).
Robots that can adapt like animals
As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged.
