Novelty search

Doncieux, Stéphane; Laflaquière, Alban; Coninx, Alexandre

doi:10.1145/3321707.3321752

Cited by 39 publications

(6 citation statements)

References 25 publications

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“…As mentioned above, similar work [27] would use K-Nearest Neighbours when evaluating for diversity, but the technique used to measure the diversity among multiple behaviours was handled differently for this research. While other work would use the Euclidean distances between the characterization vectors (with the characterization vector components reflecting each behaviour) [49], this research used sum of ranks to achieve a score for the distances of each separate component in the characterization vectors, as mentioned in Chapter 3. The differences between these techniques were not the focus of this research, but empirical analysis suggests that the use of sum of ranks mitigates the risk of particular behaviours dominating the score.…”

Section: Multi-objectivizationmentioning

confidence: 99%

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Cowan,

Ross

2024

Lecture Notes in Computer Science

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Evolutionary algorithms have a tendency to overuse and exploit particular behaviours in their search for optimality, even across separate runs. The resulting set of monotonous solutions caused by this tendency is a problem in many applications. This research explores different strategies designed to encourage an interesting set of diverse behaviours while still maintaining an appreciable level of efficacy. Embodied agents are situated within an open plane and play against each other in various pursuit game scenarios. The pursuit games consist of a single predator agent and twenty prey agents, with the goal always requiring the predator to catch as many prey as possible before the time limit is reached. The predator's controller is evolved through genetic programming while the preys' controllers are hand-crafted. The fitness of a solution is first calculated in a traditional manner. Inspired by Lehman and Stanley's novelty search strategy, the fitness is then combined with the diversity of the solution to produce the final fitness score.The original fitness score is determined by the number of captured prey, and the diversity score is determined through the combination of four behaviour measurements. Among many promising results, a particular diversity-based evaluation strategy and weighting combination was found to provide solutions that exhibit an excellent balance between diversity and efficacy. The results were analyzed quantitatively and qualitatively, showing the emergence of diverse and effective behaviours.Writing a thesis during a pandemic is no easy task. As the lines between work, school, and life became blurred, focusing on what needed to be done became increasingly difficult.Fortunately, I had the assistance of many wonderful people throughout my research. It's thanks to all of you that I was able to complete this thesis. First, I'd like to thank my supervisor, Dr. Brian J. Ross, for recommending that I consider enrolling as a Master of Science. I am forever grateful for him taking me under his wing, teaching me what he knows about the concepts used throughout this research, and providing me with continual support and assistance. I'd like to thank Illya Bakurov, Michael Gircys, and Doug Ord for their contributions toward the implementation of my systems. Their experience in genetic programming, multi-objectivization, and game-development saved me plenty of time during the development stages of this research. I'd like to thank my friends, Mitchell Clark and Jordan Maslen. The three of us met within our first month as post-secondary students and we road out the entire journey together. I have absolutely no idea where I would be today had I not met you two, so, for the many all-nighters and crunch sessions, thank you. I'd like to express heartfelt gratitude to my significant other, the most incredible woman I have ever met: Sara Testani. Your endless support and encouragement has helped me in so many ways throughout this thesis, and you've helped me find myself throughout this incredibly stressful pandem...

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Section: Multi-objectivizationmentioning

confidence: 99%

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Cowan,

Ross

2024

Lecture Notes in Computer Science

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“…Novelty provides a deterministic solution to this trade-off [116]. Novelty search at steady state would allow for uniform sampling of the reachable phenotypic space [43]. Compare this with sampling the design/genotype space, which is easy to do with random mutagenesis or recombination, but provides no guarantees about observing diverse functionalities, a property of the more complex phenotypic space.…”

Section: Novelty Before Fitnessmentioning

confidence: 99%

Open-endedness in synthetic biology: a route to continual innovation for biological design

Stock¹,

Gorochowski²

2023

Preprint

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Design in synthetic biology is typically goal-oriented: aiming to repurpose and optimize existing biological functions for new contexts, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances over the past two decades, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design that aims to continuously generate innovative solutions capable of overcoming such hurdles. The fundamental principle we put forward is that the novelty of designed biology, be it a protein, genetic construct or organism, is at least as important as how well it fulfils its goal. Designing with novelty in mind, rather than a sole focus on optimization towards a single ``best'' design, may allow us to move beyond the diminishing returns we see in performance for most engineered biology to date. Research from the artificial life and evolutionary computing communities has demonstrated that embracing novelty can allow for the automatic generation of innovative and surprising solutions to challenging problems that move us beyond local optima. Synthetic biology now offers the ideal playground to test these ideas in living systems and explore more creative approaches to biological design.

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“…Throughout exploration, the IMGEP dynamically refines its Z-traversal strategy based on the knowledge acquired by its discoveries. Here we opt for a simple IMGEP variant such that the exploration process can be seen as performing novelty search in behavior space Z [104]. The pseudocode of our IMGEP pipeline is shown in Figure 1-c and details about the internal models are provided in Materials and Methods.…”

Section: Curiosity Search Uncovers a Diversity Of Reachable Goal Statesmentioning

confidence: 99%

AI-driven Automated Discovery Tools Reveal Diverse Behavioral Competencies of Biological Networks

Etcheverry,

Moulin-Frier,

Oudeyer

et al. 2023

Preprint

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Many applications in biomedicine and synthetic bioengineering depend on the ability to understand, map, predict, and control the complex, context-sensitive behavior of chemical and genetic networks. The emerging field of diverse intelligence has offered frameworks with which to investigate and exploit surprising problem-solving capacities of unconventional agents. However, for systems that are not conventional animals used in behavior science, there are few quantitative tools that facilitate exploration of their competencies, especially when their complexity makes it infeasible to use unguided exploration . Here, we formalize and investigate a view of gene regulatory networks as agents navigating a problem space. We develop automated tools to efficiently map the repertoire of robust goal states that GRNs can reach despite perturbations. These tools rely on two main contributions that we make in this paper: (1) Using curiosity-driven exploration algorithms, originating from the AI community to explore the range of behavioral abilities of a given system, that we adapt and leverage to automatically discover the range of reachable goal states of GRNs and (2) Proposing a battery of empirical tests inspired by implementation-agnostic behaviorist approaches to assess their navigation competencies. Our data reveal that models inferred from real biological data can reach a surprisingly wide spectrum of steady states, while showcasing various competencies that living agents often exhibit, in physiological network dynamics and that do not require structural changes of network properties or connectivity. Furthermore, we investigate the applicability of the discovered “behavioral catalogs” for comparing the evolved competencies across classes of evolved biological networks, as well as for the design of drug interventions in biomedical contexts or for the design of synthetic gene networks in bioengineering. Altogether, these automated tools and the resulting emphasis on behavior-shaping and exploitation of innate competencies open the path to better interrogation platforms for exploring the complex behavior of biological networks in an efficient and cost-effective manner. To read the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

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Novelty search

Cited by 39 publications

References 25 publications

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Open-endedness in synthetic biology: a route to continual innovation for biological design

AI-driven Automated Discovery Tools Reveal Diverse Behavioral Competencies of Biological Networks

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