Don’t Starve is a non-linear, real-time survival video game where the player’s objective is to survive as long as possible. The game is challenging because randomly generated situations and events make it hard to survive this game for a long time. However, the game follows specific rules that can be used to predict future outcomes. We can use these rules to automate its playing. Our preliminary effort reported in this paper uses the answer set programming framework to model the game-playing agent’s logic to survive in the game for as long as possible. The agent learns the environment around it and translates the knowledge into facts represented as predicates. A set of commonsense reasoning rules represented in ASP captures the logic that the agent must use to survive. We combined the logic rules with the facts about the environment to compute the best action the agent must take. We use the s(CASP) goal-directed predicate ASP engine and a custom game modification program written in Lua supported by the game environment that can interface with s(CASP). Our results indicate that our preliminary automated game-playing system can outperform novice players. Further refinement should allow our system to survive longer.