mlsanityData to 14 Oct 2026

Planning from video game descriptions

arXiv 2109.00449 ↗First posted 1 Sep 2021cs.AIcited by 0

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Abstract (arXiv)

This project proposes a methodology for the automatic generation of action models from video game dynamics descriptions, as well as its integration with a planning agent for the execution and monitoring of the plans. Planners use these action models to get the deliberative behaviour for an agent in many different video games and, combined with a reactive module, solve deterministic and no-deterministic levels. Experimental results validate the methodology and prove that the effort put by a knowledge engineer can be greatly reduced in the definition of such complex domains. Furthermore, benchmarks of the domains has been produced that can be of interest to the international planning community to evaluate planners in international planning competitions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 32 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

3 of 32 references.

References · 3

  1. 2014The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarkscited by 87
  2. 2018Deep Reinforcement Learning for General Video Game AIcited by 25
  3. 2020Goal Reasoning by Selecting Subgoals with Deep Q-Learningcited by 1

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