1. A method comprising:
obtaining data specifying one or more expressions for a problem to be solved and an action that changes a state of the problem when applied to the one or more expressions;
identifying, by one or more processors, one or more features of the one or more expressions based on stored grammar rules and further based on features of stored positive training problems that are associated with positive feedback;
identifying, by the one or more processors, a precondition for applying the action that changes the state of the problem, with identification of the precondition based on the positive training problems, negative training problems associated with negative feedback, and the identified one or more features of the one or more expressions;
identifying, by the one or more processors, a sequence of operator functions based on the identified one or more features, the action that changes the state of the problem, and the positive training problems; and
generating, by the one or more processors, a production rule based on the identified one or more features, the identified precondition, and the identified operator function.
2. The method of claim 1, wherein the problem to be solved is in a math, science, or language learning domain.
3. The method of claim 1, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules comprises:
generating a parse tree for an expression using the stored grammar rules, with the parse tree comprising one or more nodes for one or more respective features of the expression; and
identifying the one or more features of the one or more expressions based on the generated parse tree.
4. The method of claim 3, wherein identifying the precondition based on the identified one or more features of the one or more expressions comprises:
identifying the precondition based on positions of the one or more nodes for the one or more respective features of the expression.
5. The method of claim 1, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules and the features of the stored positive training problems comprises:
identifying an intermediate symbol in a rule set of a probabilistic context free grammar, the intermediate symbol corresponding to a highest number of the stored positive training problems; and
extracting the one or more features associated with the intermediate symbol.
6. The method of claim 1, wherein identifying the sequence of operator functions comprises:
searching for a composed sequence of operator functions from a stored set of operator functions using iterative-deepening depth-first search to identify the composed sequence of operator functions that has a smallest number of operator functions that includes the identified one or more features and the action that changes the state of the problem.
7. The method of claim 1, wherein generating the production rule based on the identified precondition comprises:
generating a set of tests pertaining to the identified one or more features for determining whether the precondition is satisfied.
8. The method of claim 1, further comprising:
determining a current state of another problem;
identifying the generated production rule from a stored set of production rules based on the current state of the other problem; and
providing a proposed action for solving the other problem based on the generated production rule.
9. The method of claim 8, further comprising:
receiving feedback indicating that the proposed action is correct for solving the other problem; and
storing the current state and the proposed action as a positive training problem.
10. The method of claim 8, further comprising:
receiving feedback indicating that the proposed action is incorrect for solving the other problem; and
storing the current state and the proposed action as a negative training problem.
11. A system comprising:
one or more processing devices; and
one or more computer-readable media storing instructions that are executable by the one or more processing devices to perform operations comprising:
obtaining data specifying one or more expressions for a problem to be solved and an action that changes a state of the problem when applied to the one or more expressions;
identifying, by one or more processors, one or more features of the one or more expressions based on stored grammar rules and further based on features of stored positive training problems that are associated with positive feedback;
identifying, by the one or more processors, a precondition for applying the action that changes the state of the problem, with identification of the precondition based on the positive training problems, negative training problems associated with negative feedback, and the identified one or more features of the one or more expressions;
identifying, by the one or more processors, a sequence of operator functions based on the identified one or more features, the action that changes the state of the problem, and the positive training problems; and
generating, by the one or more processors, a production rule based on the identified one or more features, the identified precondition, and the identified operator function.
12. The system of claim 11, wherein the problem to be solved is in a math, science, or language learning domain.
13. The system of claim 11, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules comprises:
generating a parse tree for an expression using the stored grammar rules, with the parse tree comprising one or more nodes for one or more respective features of the expression; and
identifying the one or more features of the one or more expressions based on the generated parse tree.
14. The system of claim 13, wherein identifying the precondition based on the identified one or more features of the one or more expressions comprises:
identifying the precondition based on positions of the one or more nodes for the one or more respective features of the expression.
15. The system of claim 11, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules and the features of the stored positive training problems comprises:
identifying an intermediate symbol in a rule set of a probabilistic context free grammar, the intermediate symbol corresponding to a highest number of the stored positive training problems; and
extracting the one or more features associated with the intermediate symbol.
16. The system of claim 11, wherein identifying the sequence of operator functions comprises:
searching for a composed sequence of operator functions from a stored set of operator functions using iterative-deepening depth-first search to identify the composed sequence of operator functions that has a smallest number of operator functions that includes the identified one or more features and the action that changes the state of the problem.
17. The system of claim 11, wherein generating the production rule based on the identified precondition comprises:
generating a set of tests pertaining to the identified one or more features for determining whether the precondition is satisfied.
18. The system of claim 11, wherein the operations further comprise:
determining a current state of another problem;
identifying the generated production rule from a stored set of production rules based on the current state of the other problem; and
providing a proposed action for solving the other problem based on the generated production rule.
19. The system of claim 18, wherein the operations further comprise:
receiving feedback indicating that the proposed action is correct for solving the other problem; and
storing the current state and the proposed action as a positive training problem.
20. The system of claim 18, wherein the operations further comprise:
receiving feedback indicating that the proposed action is incorrect for solving the other problem; and
storing the current state and the proposed action as a negative training problem.
21. One or more computer-readable media storing instructions that are executable by one or more processing devices to perform operations comprising:
obtaining data specifying one or more expressions for a problem to be solved and an action that changes a state of the problem when applied to the one or more expressions;
identifying, by one or more processors, one or more features of the one or more expressions based on stored grammar rules and further based on features of stored positive training problems that are associated with positive feedback;
identifying, by the one or more processors, a precondition for applying the action that changes the state of the problem, with identification of the precondition based on the positive training problems, negative training problems associated with negative feedback, and the identified one or more features of the one or more expressions;
identifying, by the one or more processors, a sequence of operator functions based on the identified one or more features, the action that changes the state of the problem, and the positive training problems; and
generating, by the one or more processors, a production rule based on the identified one or more features, the identified precondition, and the identified operator function.
22. The one or more computer-readable media of claim 21, wherein the problem to be solved is in a math, science, or language learning domain.
23. The one or more computer-readable media of claim 21, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules comprises:
generating a parse tree for an expression using the stored grammar rules, with the parse tree comprising one or more nodes for one or more respective features of the expression; and
identifying the one or more features of the one or more expressions based on the generated parse tree.
24. The one or more computer-readable media of claim 23, wherein identifying the precondition based on the identified one or more features of the one or more expressions comprises:
identifying the precondition based on positions of the one or more nodes for the one or more respective features of the expression.
25. The one or more computer-readable media of claim 21, wherein identifying the one or more features of the one or more expressions based on the stored grammar rules and the features of the stored positive training problems comprises:
identifying an intermediate symbol in a rule set of a probabilistic context free grammar, the intermediate symbol corresponding to a highest number of the stored positive training problems; and
extracting the one or more features associated with the intermediate symbol.
26. The one or more computer-readable media of claim 21, wherein identifying the sequence of operator functions comprises:
searching for a composed sequence of operator functions from a stored set of operator functions using iterative-deepening depth-first search to identify the composed sequence of operator functions that has a smallest number of operator functions that includes the identified one or more features and the action that changes the state of the problem.
27. The one or more computer-readable media of claim 21, wherein generating the production rule based on the identified precondition comprises:
generating a set of tests pertaining to the identified one or more features for determining whether the precondition is satisfied.
28. The one or more computer-readable media of claim 21, wherein the operations further comprise:
determining a current state of another problem;
identifying the generated production rule from a stored set of production rules based on the current state of the other problem; and
providing a proposed action for solving the other problem based on the generated production rule.
29. The one or more computer-readable media of claim 28, wherein the operations further comprise:
receiving feedback indicating that the proposed action is correct for solving the other problem; and
storing the current state and the proposed action as a positive training problem.
30. The one or more computer-readable media of claim 28, wherein the operations further comprise:
receiving feedback indicating that the proposed action is incorrect for solving the other problem; and
storing the current state and the proposed action as a negative training problem.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1-14. (canceled)
15. A refrigerator, comprising:
a body and a door enclosing storage chambers;
said storage chambers having individually controlled temperatures, said upper storage chamber being warmer than said lower storage chamber, said upper storage chamber having upper and lower regions, and said upper region of said upper storage chamber having an air outlet;
at least one door rack mounted on said door and encroaching into said upper storage chamber; and
an air passage extending in said door and having an inlet in said lower storage chamber and an outlet in said lower region of said upper storage chamber, in order to route a cold air flow successively through both of said storage chambers, and said air passage bridging said at least one door rack.
16. The refrigerator according to claim 15, which further comprises at least one support for a shelf, said at least one support mutually separating said lower and upper regions of said upper storage chamber.
17. The refrigerator according to claim 15, wherein:
said door has open and closed positions;
said body has a rear wall; and
a shelf is disposed between said upper and lower regions of said upper storage chamber, said shelf being spaced apart both from said door in said closed position and from said rear wall of said body by respective gaps.
18. The refrigerator according to claim 15, wherein said body has a ceiling and a rear wall forming a corner of said upper storage chamber, and said air outlet in said upper storage chamber is disposed in said corner.
19. The refrigerator according to claim 15, wherein said door tray has a width, and said air passage extends over at least half of said width of said door tray.
20. The refrigerator according to claim 15, wherein said door tray has a width, and said air passage extends over at least three-quarters of said width of said door tray.
21. The refrigerator according to claim 15, wherein said door has a thermal insulation layer and an internal wall extending between said thermal insulation layer and said storage chambers, and said internal wall has a recess through which said air passage passes.
22. The refrigerator according to claim 21, which further comprises a wall panel partially covering said recess.
23. The refrigerator according to claim 22, which further comprises lugs disposed on said internal wall, said wall panel being hung on said lugs.
24. The refrigerator according to claim 22, wherein:
said air passage has a cross section;
said body has a divider extending between said storage chambers;
said wall panel and said divider delimit a gap when said door is closed; and
said gap has a cross section being smaller than said cross section of said air passage.
25. The refrigerator according to claim 24, wherein said wall panel has an apron sloping downward onto said divider.
26. The refrigerator according to claim 25, wherein said apron extends over an entire width of said divider.
27. The refrigerator according to claim 15, which further comprises an evaporation chamber, said body having a rear wall with an inlet duct running downward from said air outlet to said evaporation chamber.
28. The refrigerator according to claim 15, which further comprises an evaporation chamber disposed at a level of said lower storage chamber.
29. The refrigerator according to claim 15, wherein said lower storage chamber is a fresh food compartment and said upper storage chamber is a normal refrigeration compartment.
30. The refrigerator according to claim 15, wherein the refrigerator is a household refrigerator.