Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environments
This addresses the problem of enabling agents to interact more effectively in text-based environments for researchers in AI and interactive fiction, but it is incremental as it builds on existing methods with modifications.
The paper tackled the problem of generating possible actions in text-based simulated environments by studying the use of external knowledge databases like ConceptNet for affordance extraction, and introduced an algorithm evaluated on Interactive Fiction platforms, showing that external databases can be used despite challenges.
Text-based simulated environments have proven to be a valid testbed for machine learning approaches. The process of affordance extraction can be used to generate possible actions for interaction within such an environment. In this paper the capabilities and challenges for utilizing external knowledge databases (in particular ConceptNet) in the process of affordance extraction are studied. An algorithm for automated affordance extraction is introduced and evaluated on the Interactive Fiction (IF) platforms TextWorld and Jericho. For this purpose, the collected affordances are translated into text commands for IF agents. To probe the quality of the automated evaluation process, an additional human baseline study is conducted. The paper illustrates that, despite some challenges, external databases can in principle be used for affordance extraction. The paper concludes with recommendations for further modification and improvement of the process.