Learning from Exploration: Towards an Explainable Goal Reasoning Agent

Complex, real-world environments may not be fully modeled for an agent, especially if the agent has never operated in the environment before. The agent’s ability to effectively plan and act in the environment is influenced by its knowledge of when it can perform specific actions and the effects of those actions. We describe progress on an explainable exploratory planning agent that is capable of learning action preconditions using an exploratory planning process. The agent’s architecture allows it to perform both exploratory actions as well as goal-directed actions, which opens up important considerations for how exploratory planning and goal planning should be controlled, as well as how the agent’s behavior should be explained to any teammates it may have. We describe initial approaches for both exploratory planning and action model learning, and evaluate them in the video game Dungeon Crawl Stone Soup.