Towards a general theory of action and time
Artificial Intelligence
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decision theory and the cost of planning
Decision theory and the cost of planning
Planning using a temporal world model
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
A parallel algorithm for statistical belief refinement and its use in causal reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A logic and time nets for probabilistic inference
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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When a planner must decide whether it has enough evidence to make a decision based on probability, it faces the sample size problem. Current planners using probabilities need not deal with this problem because they do not generate their probabilities from observations. This paper presents an event-based language in which the planner's probabilities are calculated from the binomial random variable generated by the observed ratio of one type of event to another. Such probabilities are subject to error, so the planner must introspect about their validity. Inferences about the probability of these events can be made using statistics. Inferences about the validity of the approximations can be made using interval estimation. Interval estimation allows the planner to avoid making choices that are only weakly supported by the planner's evidence.