Aspects herein describe new methods of determining optimal actions to achieve high-level goals with minimum total future cost. At least one high-level goal is inputted into a user device along with various observational data about the world, and a computational unit determines, though a method comprising backward and forward sweeps, an optimal course of action as well as emotions. In one embodiment a user inputs a high-level goal into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions. The server determines an optimal course of action via the method of backward and forward sweeps, and the cell phone then displays the instructions and emotions to the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by a sensor, a current observation about a real or simulated world; maintaining, by a computational unit, an objective; representing the objective using an incremental cost of a plurality of potential actions; maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainty is represented by one or more probabilities of a plurality of high-level explanations of the world, such that the set of possible explanations at any one time is sparse, and wherein the current uncertainty is updated from the current observation using a filter comprising a sparse network; and determining, by the computational unit, one or more optimal actions to achieve the objective with an optimized expected total future cost, wherein said determining comprises performing both backward induction on the optimized expected total future cost and forward induction on the uncertainty about the unknown state of the world. 2. The method of claim 1, wherein the filter comprises a neural network. 3. The method of claim 1, wherein the filter comprises sparse coding. 4. The method of claim 1, wherein the filter comprises one or more Boltzmann machines. 5. The method of claim 1, wherein the filter also comprises a stickiness penalty that penalizes changes in resulting possible high-level explanations over time. 6. The method of claim 5, wherein the stickiness penalty linearly penalizes the absolute values of the changes, thereby giving preference to locally constant possible explanations with occasional jumps. 7. The method of claim 1, further comprising: determining a first number t, which represents a future time; determining a first vector x, which represents a possible explanation at time t; determining a second vector u, which represents a potential action at time t; determining a sequence of vectors i, called the archetype, which represents a preferred history of observations from the current time up to time t, consistent with explanation x; determining a first function ƒ(t, x), called the local objective function; and determining a first sparse probability distribution p(t, x), called the unconditional uncertainty, which represents the uncertainty of the plurality of explanations x of the world at time t, unconditional on future observations, wherein said forward and backward induction comprises: restricting the set of future uncertainties conditional on observations that need to be considered to probability distributions of the form pi(t, x), which comprises the restriction of the unconditional uncertainty p(t, x) to explanations x having the same archetype i at time t; representing the expected objective value at time t with archetype i as Epi[ƒ], where the operator E represents the expectation of a function with respect to a probability distribution; determining the local objective function ƒ by backward induction; determining the unconditional uncertainty p and the archetype i by forward induction; and determining the optimal actions u for each archetype i and time t. 8. The method of claim 7, wherein said forward and backward induction further comprises: applying the filter, given a current explanation xt and proposed action ut, to predict a sparse distribution X(xt, ut) of possible explanations at the next time t+1, along with a predicted incremental objective C(xt, ut) and a predicted archetype I(xt, ut); determining the backward induction step by propagating the local objective function back in time as ƒt(xt|ut)=C(xt, ut)+EX(xt,ut)(x)[ƒt+1(x)], where ut is chosen to optimize the expected objective value E p t ,  i t  ( x t )  [ f t  ( x t | u t ) ] , which happens where u t = ( E p t , i t  ( x t )  [ C u  ( x t , u t ) ] ) - 1  ( - E p t ,  i t  ( x t )  E X u  ( x t , u t )  ( x )  [ f t + 1  ( x ) ] ) ; determining the forward induction step by propagating the unconditional uncertainty pt+1(xt+1) forward in time as the convolution of pt(xt) with the X(xt, ut)(x), and the archetype it+1(xt+1) as the most likely future archetype I(xt, ut) for x at time t+1, based on the probabilities X(xt, ut)(x). 9. A system comprising: a processor; and memory storing computer readable instructions that, when executed by the processor, configure the system to perform: receiving, by a sensor, a current observation about a real or simulated world; maintaining, by a computational unit, an objective; representing the objective using an incremental cost of a plurality of potential actions; maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainty is represented by one or more probabilities of a plurality of high-level explanations of the world, such that the set of possible explanations at any one time is sparse, and wherein the current uncertainty is updated from the current observation using a filter comprising a sparse network; and determining, by the computational unit, one or more optimal actions to achieve the objective with an optimized expected total future cost, wherein said determining comprises performing both backward induction on the optimized expected total future cost and forward induction on the uncertainty about the unknown state of the world. 10. The system of claim 9, wherein the filter comprises a neural network. 11. The system of claim 9, wherein the filter comprises sparse coding. 12. The system of claim 9, wherein the filter comprises one or more Boltzmann machines. 13. The system of claim 9, wherein the filter also comprises a stickiness penalty that penalizes changes in resulting possible high-level explanations over time. 14. The system of claim 13, wherein the stickiness penalty linearly penalizes the absolute values of the changes, thereby giving preference to locally constant possible explanations with occasional jumps. 15. The system of claim 9, further comprising: determining a first number t, which represents a future time; determining a first vector x, which represents a possible explanation at time t; determining a second vector u, which represents a potential action at time t; determining a sequence of vectors i, called the archetype, which represents a preferred history of observations from the current time up to time t, consistent with explanation x; determining a first function ƒ(t, x), called the local objective function; and determining a first sparse probability distribution p(t, x), called the unconditional uncertainty, which represents the uncertainty of the plurality of explanations x of the world at time t, unconditional on future observations, wherein said forward and backward induction comprises: restricting the set of future uncertainties conditional on observations that need to be considered to probability distributions of the form pi(t, x), which comprises the restriction of the unconditional uncertainty p(t, x) to explanations x having the same archetype i at time t; representing the expected objective value at time t with archetype i as Epi[ƒ], where the operator E[ ] represents the expectation of a function with respect to a probability distribution; determining the local objective function ƒ by backward induction; determining the unconditional uncertainty p and the archetype i by forward induction; and determining the optimal actions u for each archetype i and time t. 16. The system of claim 15, wherein said forward and backward induction further comprises: applying the filter, given a current explanation xt and proposed action ut, to predict a sparse distribution X(xt, ut) of possible explanations at the next time t+1, along with a predicted incremental objective C(xt, ut) and a predicted archetype I(xt, ut); determining the backward induction step by propagating the local objective function back in time as ƒt(xt|ut)=C(xt, ut)+EX(xt,ut)(x)[ƒt+1(x)], where ut is chosen to optimize the expected objective value E p t ,  i t  ( x t )  [ f t  ( x t | u t ) ] , which happens where u t = ( E p t , i t  ( x t )  [ C u  ( x t , u t ) ] ) - 1  ( - E p t ,  i t  ( x t )  E X u  ( x t , u t )  ( x )  [ f t + 1  ( x ) ] ) ; determining the forward induction step by propagating the unconditional uncertainty pt+1(xt+1) forward in time as the convolution of pt(xt) with the X(xt, ut)(x), and the archetype it+1(xt+1) as the most likely future archetype I(xt, ut) for x at time t+1, based on the probabilities X(xt, ut)(x). 17. One or more computer readable media comprising computer readable instructions stored thereon, wherein when the instructions are executed by a data processing system, said instructions configure the system to perform: receiving, by a sensor, a current observation about a real or simulated world; maintaining, by a computational unit, an objective; representing the objective using an incremental cost of a plurality of potential actions; maintaining, by the computational unit, a current uncertainty about an unknown state of the world, wherein the current uncertainty is represented by one or more probabilities of a plurality of high-level explanations of the world, such that the set of possible explanations at any one time is sparse, and wherein the current uncertainty is updated from the current observation using a filter comprising a sparse network; and determining, by the computational unit, one or more optimal actions to achieve the objective with an optimized expected total future cost, wherein said determining comprises performing both backward induction on the optimized expected total future cost and forward induction on the uncertainty about the unknown state of the world. 18. The method of claim 17, wherein the filter comprises a neural network. 19. The method of claim 17, wherein the filter comprises sparse coding. 20. The method of claim 17, wherein the filter comprises one or more Boltzmann machines. 21. The method of claim 17, wherein the filter also comprises a stickiness penalty that penalizes changes in resulting possible high-level explanations over time. 22. The method of claim 21, wherein the stickiness penalty linearly penalizes the absolute values of the changes, thereby giving preference to locally constant possible explanations with occasional jumps. 23. The method of claim 17, further comprising: determining a first number t, which represents a future time; determining a first vector x, which represents a possible explanation at time t; determining a second vector u, which represents a potential action at time t; determining a sequence of vectors i, called the archetype, which represents a preferred history of observations from the current time up to time t, consistent with explanation x; determining a first function ƒ(t, x), called the local objective function; and determining a first sparse probability distribution p(t, x), called the unconditional uncertainty, which represents the uncertainty of the plurality of explanations x of the world at time t, unconditional on future observations, wherein said forward and backward induction comprises: restricting the set of future uncertainties conditional on observations that need to be considered to probability distributions of the form pi(t, x), which comprises the restriction of the unconditional uncertainty p(t, x) to explanations x having the same archetype i at time t; representing the expected objective value at time t with archetype i as Epi[ƒ], where the operator E[ ] represents the expectation of a function with respect to a probability distribution; determining the local objective function ƒ by backward induction; determining the unconditional uncertainty p and the archetype i by forward induction; and determining the optimal actions u for each archetype i and time t. 24. The method of claim 23, wherein said forward and backward induction further comprises: applying the filter, given a current explanation xt and proposed action ut, to predict a sparse distribution X(xt, ut) of possible explanations at the next time t+1, along with a predicted incremental objective C(xt, ut) and a predicted archetype I(xt, ut); determining the backward induction step by propagating the local objective function back in time as ƒt(xt|ut)=C(xt, ut)+EX(xt,ut)(x)[ƒt+1(x)], where ut is chosen to optimize the expected objective value E p t , i t  ( x t )  [ f t  ( x t | u t ) ] , which happens where u t = ( E p t , i t  ( x t )  [ C u  ( x t , u t ) ] ) - 1  ( - E p t , i t  ( x t )  E X u  ( x t , u t )  ( x )  [ f t + 1  ( x ) ] ) ; determining the forward induction step by propagating the unconditional uncertainty pt+1(xt+1) forward in time as the convolution of pt(xt) with the X(xt, ut)(x), and the archetype it+1(xt+1) as the most likely future archetype I(xt, ut) for x at time t+1, based on the probabilities X(xt, ut)(x).