Abstract Many traditional AI algorithms fail to scale as the size of state space increases exponentially with the number of features. One way to reduce computation in such scenarios is to reduce the problem size by grouping symmetric states together and then running the algorithm on the reduced problem. The focus of this work is to exploit symmetry in problems of sequential decision making and probabilistic inference. Our recent workASAPUCT defines new State-Action Pair (SAP) symmetries in Markov Decision Processes. We also apply these SAP symmetries in Monte Carlo Tree Search (MCTS) framework. In probabilistic inference, we expand the notion of unconditional symmetries to contextual symmetries and apply them in Markov Chain Monte Carlo (MCMC) methods. In future, we plan to explore interesting links in symmetry exploitation in different problems and aim to develop a generic symmetry based framework.