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arXiv AI By Maxim Gumin, Hsueh-Ti Derek Liu, Victor Zordan, Daniel Ritchie

Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Search

Read the original on arXiv AI →

The paper introduces a method for creating reactive character behaviors in continuous games as compact, human‑readable programs. It searches over a domain‑specific language that uses reactive geometric decisions and higher‑order constructs to discretize continuous behavior space, while eliminating redundant program forms through synthesis antipatterns. The approach, called agentic sketching, combines bottom‑up symbolic enumeration with top‑down guidance from a coding agent, and outperforms either technique alone on a benchmark of 14 continuous games.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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