Poker is an imperfect information game that requires decision-making under conditions of uncertainty, much like many real-world applications. Strong poker players have to skillfully deal with multiple opponents, risk management, opponent modeling, deception and unreliable information. These features make poker an interesting area for Artificial Intelligence research. This thesis describes work done on improving the knowledge representation, betting strategy, and opponent modeling of Loki, a poker-playing program at the University of Alberta. First, a randomized betting strategy that returns a probability triple is introduced. A probability triple is a probabilistic representation of betting decisions that indicates the likelihood of each betting action occurring in a given situation. Second, real-time simulations are used to compute the expected values of betting decisions. These simulations use selective sampling to maximize the information obtained with each simulation trial. Experimental results show that each of these enhancements represents a major advance in the strength of Loki.