English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

PINSKY is a system for open-ended learning through neuroevolution ingame-based domains. It builds on the Paired Open-Ended Trailblazer (POET)system, which originally explored learning and environment generation forbipedal walkers, and adapts it to games in the General Video Game AI (GVGAI)system. Previous work showed that by co-evolving levels and neural networkpolicies, levels could be found for which successful policies could not becreated via optimization alone. Studied in the realm of Artificial Life as apotentially open-ended alternative to gradient-based fitness, minimal criteria(MC)-based selection helps foster diversity in evolutionary populations. Themain question addressed by this paper is how the open-ended learning actuallyworks, focusing in particular on the role of transfer of policies from oneevolutionary branch ("species") to another. We analyze the dynamics of thesystem through creating phylogenetic trees, analyzing evolutionary trajectoriesof policies, and temporally breaking down transfers according to species type.Furthermore, we analyze the impact of the minimal criterion on generated leveldiversity and inter-species transfer. The most insightful finding is thatinter-species transfer, while rare, is crucial to the system's success.

Transfer Dynamics in Emergent Evolutionary Curricula