A mechanism is provided for computing a solution to a plan recognition problem. The plan recognition problem includes the model and a partially ordered sequence of observations or traces. The plan recognition is transformed into an AI planning problem such that a planner can be used to compute a solution to it. The approach is general. It addresses unreliable observations: missing observations, noisy observations (or observations that need to be discarded), and ambiguous observations). The approach does not require plan libraries or a possible set of goals. A planner can find either one solution to the resulting planning problem or multiple ranked solutions, which maps to the most plausible solution to the original problem.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for computing a plan with unreliable observations, the method comprises: accessing, by a processor, a description of a domain model that incorporates both behavior and data; receiving a partially ordered sequence of observations, where at least one or more of the observations is a fluent condition that changes over time and any remaining observations are action conditions; transforming, by use of a transform algorithm, the partially ordered sequence of observations and the domain model to an artificial intelligence (AI) planning problem; computing at least one plan from the AI planning problem; and translating the at least one plan to provide a solution by removing any hidden actions therefrom. 2. The method of claim 1, wherein the receiving the partially ordered sequence of observations, where the remaining observations are action conditions that are translated into fluent conditions. 3. The method of claim 1, wherein the receiving the at least the partially ordered sequence of observations includes receiving observations in which each of the observations is a fluent condition that changes over time. 4. The method of claim 1, wherein the description of the domain model includes a finite set of facts, an initial state, a finite set of action operators, and a goal condition. 5. The method of claim 1, wherein the description of the domain model does not include at one of an initial state and a goal condition. 6. The method of claim 1, wherein the receiving the at least the partially ordered sequence of observations includes receiving observations in which each of the observations is an ordered sequence and one or more of the observations are discarded missing; and ambiguous. 7. The method of claim 1, further comprising: accessing, by the processor, an integer K indicating a number of plans to compute; and wherein the computing the plan includes computing a number of plans equal to K. 8. The method of claim 1, wherein the computing the plan includes using a top-K planner. 9. The method of claim 8, wherein a quality of the plan is measured by a cost of the plan and selecting the at least one plan that has a minimal cost. 10. The method of claim 1, wherein the computing the plan includes using a diverse planner. 11. The method of claim 1, further comprising: accessing, by the processor, an integer T is indicating a future time horizon of plans to compute; and wherein the computing the plan includes computing plans with a future time horizon. 12. A computer program product for computing a plan with unreliable observations comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform: accessing, by a processor, a description of a domain model that incorporates both behavior and data; receiving a partially ordered sequence of observations, where at least one or more of the observations is a fluent condition that changes over time and any remaining observations are action conditions; transforming, by use of a transformation algorithm, the partially ordered sequence of observations and the domain model to an artificial intelligence (AI) planning problem; computing at least one plan from the AI planning problem; and translating the at least one plan to provide a solution by removing any hidden actions therefrom. 13. The computer program product of claim 12, wherein the receiving the partially ordered sequence of observations, where the remaining observations are action conditions that are translated into fluent conditions. 14. The computer program product of claim 12, wherein the receiving the at least the partially ordered sequence of observations includes receiving observations in which each of the observations is a fluent condition that changes over time. 15. The computer program product of claim 12, wherein the description of the domain model includes a finite set of facts, an initial state, a finite set of action operators, and a goal condition. 16. The computer program product of claim 12, wherein the description of the domain model does not include at one of an initial state and a goal condition. 17. The computer program product of claim 12, wherein the receiving the at least the partially ordered sequence of observations includes receiving observations in which each of the observations is an ordered sequence and one or more of the observations are discarded missing; and ambiguous. 18. The computer program product of claim 12, further comprising: accessing, by the processor, an integer K indicating a number of plans to compute; and wherein the computing the plan includes computing a number of plans equal to K. 19. The computer program product of claim 18, wherein a quality of the plan is measured by a cost of the plan and wherein each action in the plan is associated with an action cost. 20. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to perform: accessing, by a processor, a description of a domain model that incorporates both behavior and data; receiving a partially ordered sequence of observations, where at least one or more of the observations is a fluent condition that changes over time and any remaining observations are action conditions; transforming, by use of transformation algorithm, the partially ordered sequence of observations and the domain model to an artificial intelligence (AI) planning problem; computing at least one plan from the AI planning problem; and translating the at least one plan to provide a solution by removing any hidden actions therefrom.