Linked Data MASE - A Maze-Based Multi-Agent Systems Environment for Testing and Visualizing Hypermedia Agents

Stefan Meier, Jérémy Lemée, Danai Vachtsevanou, Simon Mayer

Keywords: Agent Environments, Hypermedia Multi-Agent Systems, Linked Data

TL;DR: We present MASE, a lightweight Linked Data based maze environment customizable to support diverse evaluation requirements across heterogeneous agent architectures and experimental setups.

Abstract: As autonomous agents increasingly operate on the Web, researchers need reusable testbeds for controlled experimentation. Linked Data (LD) environments support uniform hypermedia interaction and have been used for the development of LD-based maze scenarios, suitable for studying diverse agent architectures under tasks such as navigation, task solving, collaboration, or adaptation to changing environmental conditions. However, existing implementations give insufficient consideration to agent embodiment and provide limited support for researchers to customize the environment for their experiments. We present MASE, a lightweight LD-based maze environment customizable through SPARQL queries to support diverse evaluation requirements across heterogeneous agent architectures and experimental setups. MASE enforces constraints related to embodiment and interaction, attributes each state change to the request and rules that caused it, and provides an event-driven visualization to support development and reduce entry barriers.

Paper Type: Tools / Testbeds / Demo paper

Demo: Yes, we would love to present a demo.

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Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 45