Predicting optimal solution cost with conditional probabilities

Predicting Solution Cost with Conditional Probabilities

Classical heuristic search algorithms find the solution cost of a problem while finding the path from the start state to a goal state. However, there are applications in which finding the path is not needed. In this paper we propose an algorithm that accurately and efficiently predicts the solution cost of a problem without finding the actual solution. We show empirically that our predictor mak...

Predicting Optimal Solution Cost with Bidirectional Stratified Sampling

Optimal planning and heuristic search systems solve state-space search problems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., without actually finding a solution path of that cos...

Quantum Conditional Probabilities Are Not Probabilities of Quantum Conditional

Probabilities of Conditionals and Conditional Probabilities Ii

A dams's thesis about indicative conditionals is that their assertIlability goes by the conditional subjective probability of the consequent given the antecedent, in very much the same way that assertability normally goes by the subjective probability of truth.' The thesis is well established; the remaining question is how it may best be explained. The nicest explanation would be that the truth...

Toward Markov Logic with Conditional Probabilities

Combining probability and first-order logic has been the subject of intensive research during the last ten years. The most well-known formalisms combining probability and some subset of first-order logic are probabilistic relational models (PRMs), Bayesian logic programs (BLPs) and Markov logic networks (MLNs). Of these three formalisms, MLN is the currently most actively researched. While the ...