Rationalization of Symbolic Actions


Rationalization of Symbolic Actions – We propose a novel framework for unsupervised reinforcement learning by using temporal dependencies between signals. The underlying temporal relationship is a set of continuous variables in which every interaction is modeled as local, i.e., a new variable, and each time step is modeled as an interval, and each interval represents a time dependency of a learned action, the action’s behavior, and the associated dependency. The proposed framework can be used to learn temporally coherent actions, and it is especially useful for learning in environments that exhibit frequent interactions. Experiments on several challenging benchmarks show that our method outperforms both supervised and unsupervised reinforcement learning.

In this case, a problem of estimating the probability that an entity will appear is used to evaluate the performance of a game of constraint satisfaction. A key problem in the optimization of constrained game programs, however, has been not addressed so far. In this work, we propose an efficient method for this problem to extract meaningful probabilities of a constraint satisfaction by applying a deterministic algorithm. We generalize past results based on classical deterministic methods to two other problems: the problem of finding the smallest set of propositional probability distributions over the state of a game, and the problem of finding the strongest likelihood distribution over the best probability distributions over random variables.

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Rationalization of Symbolic Actions

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  • Falsified Belief-In-A-Set and Other True Beliefs Revisited

    The Generalized Fuzzy Game–An Interactive Game of ConstraintsIn this case, a problem of estimating the probability that an entity will appear is used to evaluate the performance of a game of constraint satisfaction. A key problem in the optimization of constrained game programs, however, has been not addressed so far. In this work, we propose an efficient method for this problem to extract meaningful probabilities of a constraint satisfaction by applying a deterministic algorithm. We generalize past results based on classical deterministic methods to two other problems: the problem of finding the smallest set of propositional probability distributions over the state of a game, and the problem of finding the strongest likelihood distribution over the best probability distributions over random variables.


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