A method comprises building a forecast for an autonomous agent. The building at least comprises assigning a selected parameter of the autonomous agent to a state value, adding a new policy to a set of policies where the new policy maps actions of the autonomous agent for optimizing the state value, and adding a new forecast to a set of forecasts where the forecast at least comprises a prediction of a next state of the autonomous agent following execution of the new policy. The mapped actions are initiated according to the new policy. A state of the autonomous agent is evaluated following completion of the mapped actions. The evaluation at least comprises comparing the state with the forecast. Whether to build an additional forecast is determined. The determining is at least in part based on the evaluation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising the steps of: building a forecast for an autonomous agent, said building at least comprising: assigning a selected parameter of said autonomous agent to a state value; adding a new policy to a set of policies, said new policy mapping actions of said autonomous agent for optimizing said state value; and adding a new forecast to a set of forecasts, said forecast at least comprising a prediction of a next state of said autonomous agent following execution of said new policy; initiating said mapped actions according to said new policy; evaluating a state of said autonomous agent following completion of said mapped actions, said evaluation at least comprising comparing said state with said forecast; and determining whether to build an additional forecast, said determining at least in part based on said evaluation. 2. The method as recited in claim 1, further comprising the steps of: determining if said forecast is ineffective; and pruning said forecast from said set of forecasts upon said determination. 3. The method as recited in claim 1, further comprising the step of determining whether to terminate the method, said determining at least in part based on said evaluation. 4. The method as recited in claim 1, in which said set of forecasts comprises a hierarchical structure. 5. The method as recited in claim 1, in which said new policy further comprises starting and stopping criteria. 6. The method as recited in claim 1, in which a state predicted by any forecast in said set of forecasts is associated with at least one of said policies in said set of policies. 7. The method as recited in claim 1, in which said selected parameter comprises at least one of an observation signal, a forecast of interest, a function of a combination of observation signals, and a function of forecast values in said set of forecasts. 8. The method as recited in claim 3, in which said step of determining whether to terminate the method is further based on a threshold value. 9. The method as recited in claim 1, in which said forecast comprises General Value Functions. 10. A method comprising: steps for building a forecast for an autonomous agent; steps for initiating mapped actions; steps for evaluating a state of said autonomous agent following completion of said mapped actions; and steps for determining whether to build an additional forecast. 11. The method as recited in claim 10, further comprising: steps for determining if said forecast is ineffective; steps for pruning said forecast from said set of forecasts upon said determination; and steps for determining whether to terminate the method. 12. A non-transitory computer-readable storage medium with an executable program stored thereon, wherein the program instructs one or more processors to perform the following steps: building a forecast for an autonomous agent, said building at least comprising: assigning a selected parameter of said autonomous agent to a state value; adding a new policy to a set of policies, said new policy mapping actions of said autonomous agent for optimizing said state value; and adding a new forecast to a set of forecasts, said forecast at least comprising a prediction of a next state of said autonomous agent following execution of said new policy; initiating said mapped actions according to said new policy; evaluating a state of said autonomous agent following completion of said mapped actions, said evaluation at least comprising comparing said state with said forecast; and determining whether to build an additional forecast, said determining at least in part based on said evaluation. 13. The program instructing the one or more processors as recited in claim 12, further comprising the steps of: determining if said forecast is ineffective; and pruning said forecast from said set of forecasts upon said determination. 14. The program instructing the one or more processors as recited in claim 12, further comprising the step of determining whether to terminate the method, said determining at least in part based on said evaluation. 15. The program instructing the one or more processors as recited in claim 12, in which said set of forecasts comprises a hierarchical structure. 16. The program instructing the one or more processors as recited in claim 12, in which said new policy further comprises starting and stopping criteria. 17. The program instructing the one or more processors as recited in claim 12, in which a state predicted by any forecast in said set of forecasts is associated with at least one of said policies in said set of policies. 18. The program instructing the one or more processors as recited in claim 12, in which said selected parameter comprises at least one of an observation signal, a forecast of interest, a function of a combination of observation signals, and a function of forecast values in said set of forecasts. 19. The program instructing the one or more processors as recited in claim 14, in which said step of determining whether to terminate the method is further based on a threshold value. 20. The program instructing the one or more processors as recited in claim 12, in which said forecast comprises General Value Functions.