Abstract
In the field of probabilistic planning, in order to efficiently solve sequential decision-making problems modeled by Markov Decision Processes (MDPs), it is crucial to handle stochastic knowledge defined over complex relations that may involve features of transitivity, symmetry, recursion, determinism and context-specific independence. However, traditional representational formalisms used…