Planning for conjunctive goals
Artificial Intelligence
Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
Reasoning about action I: a possible worlds approach
Artificial Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Formal theories of action (preliminary report)
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Taming intractible branching in qualitative simulation
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Sound abstraction of probabilistic actions in the constraint mass assignment framework
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Theoretical foundations for abstraction-based probabilistic planning
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A structured, probabilistic representation of action
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Abstracting probabilistic actions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
A language for planning with statistics
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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Temporal projection--predicting future states of a changing world--has been studied mainly as a formal problem. Researchers have been concerned with getting the concepts of causality and change right, and have ignored the practical issues surrounding projection. In planning, for example, when the effects of a plan's actions depend on the prevailing state of the world and that state of the world is not known with certainty, projecting the plan may generate an exponential number of possible outcomes. This problem has traditionally been eliminated by (1) restricting the domain so the world state is always known, and (2) by restricting the action representation so that either the action's intended effect is realized or the action cannot be projected at all. We argue against these restrictions and instead present a system that (1) represents and reasons about an uncertain world, (2) supports a representation that allows context-sensitive action effects, and (3) generates projections that reflect only the significant or relevant outcomes of the plans, where relevance is determined by the planner's queries about the resulting world state.