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Porimon: An LLM-Based Pokémon Battle Agent Enhanced by Long/Short-Term Knowledge Augmented Generation
Dongyin Zhuo, Fengjunjie Pan, Nenad Petrovic +1
In this paper, we use Pokémon Battles as a case study to investigate how to improve the performance of LLM-based agents in tasks that require opponent-aware planning without additi…
Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152
Vahid Zolfaghari, Nenad Petrovic, AndrÉ Schamschurko +1
Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long,…
Dr. AGENTONOMICS: A Didactic Experiment of AGENTONOMICS
Fengjunjie Pan, Alois Knoll
AGENTONOMICS is a framework that treats AI agents as economic entities that can be designed, managed, and governed through an integrated management architecture. Dr. AGENTONOMICS i…