All Outputs (15)

An Open Intent Discovery Evaluation Framework (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). An Open Intent Discovery Evaluation Framework. Presented at SIGDIAL 2024, Kyoto, Japan

In the development of dialog systems the discovery of the set of target intents to identify is a crucial first step that is often overlooked. Most intent detection works assume that a labelled dataset already exists, however creating these datasets i... Read More about An Open Intent Discovery Evaluation Framework.

Automated Human-Readable Label Generation in Open Intent Discovery (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). Automated Human-Readable Label Generation in Open Intent Discovery. Presented at Interspeech 2024, Kos, Greece

The correct determination of user intent is key in dialog systems. However, an intent classifier often requires a large, labelled training dataset to identify a set of known intents. The creation of such a dataset is a complex and time-consuming task... Read More about Automated Human-Readable Label Generation in Open Intent Discovery.

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances (2024)
Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September). Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria

Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations which avoid designing and calculating features. Mounting evidence fro... Read More about Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances.

Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, September). Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection. Presented at 18th International Conference, PPSN 2024, Hagenberg, Austria

Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an analysis phase, e.g. where features are computed by sampling solution... Read More about Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection.

Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture (2024)
Presentation / Conference Contribution
Le Goff, L., & Hart, E. (2024, July). Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture. Presented at GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop, Melbourne, Australia

Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is rea... Read More about Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture.

Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, July). Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, Australia

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajec-tories obtained from running a solver as input... Read More about Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples.

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Presentation / Conference Contribution
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (2024, July). Understanding fitness landscapes in morpho-evolution via local optima networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Pre... Read More about Understanding fitness landscapes in morpho-evolution via local optima networks.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2024, July). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. Presented at GECCO '24: Genetic and Evolutionary Computation Conference, Melbourne, Australia

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

Ealain: A Camera Simulation Tool to Generate Instances for Multiple Classes of Optimisation Problem (2024)
Presentation / Conference Contribution
Renau, Q., Dreo, J., & Hart, E. (2024, July). Ealain: A Camera Simulation Tool to Generate Instances for Multiple Classes of Optimisation Problem. Presented at GECCO '24: Genetic and Evolutionary Computation Conference, Melbourne, Australia

Artificial benchmark datasets are common in both numerical and discrete optimisation domains. Existing benchmarks cover a broad range of classes of optimisation, but as a general rule have limited value due to their poor resemblance to real-world pro... Read More about Ealain: A Camera Simulation Tool to Generate Instances for Multiple Classes of Optimisation Problem.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains (2024)
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (online). Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation, https://doi.org/10.1162/evco_a_00350

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well w... Read More about Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation (2024)
Presentation / Conference Contribution
Stone, C., Renau, Q., Miguel, I., & Hart, E. (2024, June). An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation. Presented at 18TH Learning and Intelligent Optimization Conference, Ischia, Italy

We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by developing a more general classifier that does not require specialised info... Read More about An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation.

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, April). On the Utility of Probing Trajectories for Algorithm-Selection. Presented at EvoStar 2024, Aberystwyth, UK

Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Presentation / Conference Contribution
Montague, K., Hart, E., & Paechter, B. (2024, April). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. Presented at EvoStar 2024, Aberystwyth

Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and to the fact that they can be easily interpreted by humans. From an algorithmic perspective, an additional advantage is that extra modules can e... Read More about A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control.

Generalized Early Stopping in Evolutionary Direct Policy Search (2024)
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (2024). Generalized Early Stopping in Evolutionary Direct Policy Search. ACM Transactions on Evolutionary Learning and Optimization, 4(3), Article 14. https://doi.org/10.1145/3653024

Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, e.g. in robotics applications. Often when evaluating solution over a fixe... Read More about Generalized Early Stopping in Evolutionary Direct Policy Search.

Evolving Behavior Allocations in Robot Swarms (2024)
Presentation / Conference Contribution
Hallauer, S., Nitschke, G., & Hart, E. (2023, December). Evolving Behavior Allocations in Robot Swarms. Presented at IEEE Symposium Series on Computational Intelligence (SSCI 2023), Mexico City, Mexico

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate... Read More about Evolving Behavior Allocations in Robot Swarms.