A method for determining a policy that considers observations delayed at runtime is disclosed. The method includes constructing a model of a stochastic decision process that receives delayed observations at run time, wherein the stochastic decision process is executed by an agent, finding an agent policy according to a measure of an expected total reward of a plurality of agent actions within the stochastic decision process over a given time horizon, and bounding an error of the agent policy according to an observation delay of the received delayed observations.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: constructing a model of a stochastic decision process that receives delayed observations at run time, wherein the stochastic decision process is executed by an agent; finding an agent policy according to a measure of an expected total reward of a plurality of agent actions within the stochastic decision process over a given time horizon; bounding an error of the agent policy according to an observation delay of the received delayed observations; and offering a reward to the agent using the agent policy having the error bounded according to the observation delay of the received delayed observations. 2. The method of claim 1, wherein finding the agent policy comprises: updating an agent belief state upon receiving each of the delayed observation; and determining a next agent action according to the expected total reward of a remaining decision epoch given an updated agent belief state. 3. The method of claim 2, wherein the agent belief state is updated using the delayed observation, a history of observations at runtime and a history of agent actions at runtime. 4. The method of claim 2, wherein the agent executes the next agent action in a next decision epoch. 5. The method of claim 1, further comprising: storing a history of observations at runtime; storing a history of agent actions at runtime; and recalling the history of observations at runtime and the history of agent actions at runtime to find the agent policy. 6. The method of claim 1, wherein the expected total reward comprises all rewards that the agent receives when a given agent action is executed in a current agent belief state. 7. The method of claim 1, wherein the observation delay of the received delayed observations is a maximum observation delay among the received delayed observations that is considered by the model. 8. A computer program product for planning in uncertain environments, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: receiving a model of a stochastic decision process that receives delayed observations at run time, wherein the stochastic decision process is executed by an agent; finding an agent policy according to a measure of an expected total reward of a plurality of agent actions within the stochastic decision process over a given time horizon; and bounding an error of the agent policy according to an observation delay of the received delayed observations. 9. The computer program product of claim 8, wherein finding the agent policy comprises: updating an agent belief state upon receiving each of the delayed observation; and determining a next agent action according to the expected total reward of a remaining decision epoch given an updated agent belief state. 10. The computer program product of claim 9, wherein the agent belief state is updated using the delayed observation, a history of observations at runtime and a history of agent actions at runtime. 11. The computer program product of claim 8, further comprising: storing a history of observations at runtime; storing a history of agent actions at runtime; and recalling the history of observations at runtime and the history of agent actions at runtime to find the agent policy. 12. The computer program product of claim 8, wherein the expected total reward comprises all rewards that the agent receives when a given agent action is executed in a current agent belief state. 13. The computer program product of claim 8, wherein the observation delay of the received delayed observations is a maximum observation delay among the received delayed observations that is considered by the model. 14. A decision engine configured execute a stochastic decision process receiving delayed observations using an agent policy comprising: a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the decision engine to: receive a model of the stochastic decision process that receives a plurality of delayed observations at run time, wherein the stochastic decision process is executed by an agent; find an agent policy according to a measure of an expected total reward of a plurality of agent actions within the stochastic decision process over a given time horizon; and bound an error of the agent policy according to an observation delay of the received delayed observations. 15. The decision engine of claim 14, wherein the agent policy comprises: an agent belief state updated upon receiving each of the delayed observation; and a next agent action extracted according to the expected total reward of a remaining decision epoch given the agent belief state. 16. The decision engine of claim 15, wherein the agent belief state is updated using the delayed observation, a history of observations at runtime and a history of agent actions at runtime. 17. The decision engine of claim 14, wherein the program instructions executable by the processor to cause the decision engine to: store a history of observations at runtime; store a history of agent actions at runtime; and recall the history of observations at runtime and the history of agent actions at runtime to find the agent policy. 18. The decision engine of claim 14, wherein the expected total reward comprises all rewards that the agent receives when a given agent action is executed in a current agent belief state. 19. The decision engine of claim 14, wherein the observation delay of the received delayed observations is a maximum observation delay among the received delayed observations that is considered by the model.