In this thesis we investigate the use of hierarchical task network (HTN) planning in domains that involve risk and uncertainty. The objective of the research is to explore the potential of HTN planning in complex decision-making processes. To achieve this goal, we leverage key concepts and definitions from previous research and apply them to model the game of poker. Poker is an ideal domain for investigating decision-making under risk and uncertainty due to its complexity and unpredictability. We focus on two crucial concepts in the poker domain, namely the hand map and hand range. Monte Carlo simulation is used to generate a heatmap of potential win rates for each hand, and the heatmap is divided into different sections based on quantiles. Each section is then mapped to a corresponding action based on the defined risk level, providing a guide for decision-making under risk and uncertainty. To implement the planner, we use PyHOP, a base Python planner, as a starting point. As there are no existing benchmarks available to evaluate the efficiency of an HTN planner for the poker domain, the study creates its own benchmark settings to assess the performance of the planner. Four different scenarios are conducted, and five metrics are used to evaluate the planner’s performance.