Name
Abstract | ||
|---|---|---|
I describe a planning methodology for domains with uncertainty in the form of external events that are not completely predictable. The events are represented by enabling conditions and probabilities of occurrence. The planner is goal-directed and backward chaining, but the subgoals are suggested by analysing the probability of success of the partial plan rather than being simply the open conditions of the operators in the plan. The partial plan is represented as a Bayesian belief net to compute its probability of success. Since calculating the probability of success of a plan can be very expensive I introduce two other techniques for computing it, one that uses Monte Carlo simulation to estimate it and one based on a Markov chain representation that uses knowledge about the dependencies between the predicates describing the domain. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/B978-1-55860-332-5.50017-1 | UAI |
Keywords | Field | DocType |
external event,monte carlo simulation,enabling condition,open condition,partial plan,markov chain representation,bayesian belief,planning methodology | Monte Carlo method,Computer science,Markov chain,Planner,Backward chaining,Artificial intelligence,Operator (computer programming),Machine learning,Bayesian probability | Conference |
Volume | ISBN | Citations |
abs/1302.6791 | 1-55860-332-8 | 19 |
PageRank | References | Authors |
2.92 | 4 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jim Blythe | 1 | 707 | 73.61 |