Abstract / Description of output
We present a simple set of inference rules for reasoning about
the effects of actions in a conditional plan. The rules allow us
to make additional conclusions about a plan at every stage of
its execution, and to augment an agent’s knowledge state in
the presence of incomplete knowledge. We can use the rules
to refine an agent’s knowledge after a particular execution of a
plan has completed, and to improve an agent’s ability to generate
plans. Furthermore, this enhancement gives us the ability
to plan for certain types of temporally oriented goals, e.g.,
goals that require some initial state condition be restored by
the end of the plan. We have implemented this mechanism inside
of a planner, and we demonstrate the planner’s increased
ability to solve a variety of interesting planning problems.
the effects of actions in a conditional plan. The rules allow us
to make additional conclusions about a plan at every stage of
its execution, and to augment an agent’s knowledge state in
the presence of incomplete knowledge. We can use the rules
to refine an agent’s knowledge after a particular execution of a
plan has completed, and to improve an agent’s ability to generate
plans. Furthermore, this enhancement gives us the ability
to plan for certain types of temporally oriented goals, e.g.,
goals that require some initial state condition be restored by
the end of the plan. We have implemented this mechanism inside
of a planner, and we demonstrate the planner’s increased
ability to solve a variety of interesting planning problems.
Original language | English |
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Title of host publication | Proceedings of the ICAPS 2003 Workshop on Planning under Uncertainty and Incomplete Information |
Pages | 96-102 |
Number of pages | 7 |
Publication status | Published - Jun 2003 |