At least one aspect of the present disclosure describes a content optimization system using probability factors including a rule management module, a content generation module, and a content evaluation module. The rule management module maintains a set of rules for content generation, the set of rules comprising a rule on probability factor, the rule on probability factor specifying a statistical probability that a piece of content expresses a particular value of a selected content attribute. The content generation module is operative to generate a plurality of pieces of content based on the set of rules, the plurality of pieces of content designed with a particular optimization objective. The content evaluation module is operative to evaluate content performance on reaching the particular optimization objective based on data acquired when the plurality of pieces of content are displayed, and the content evaluation module is further operative to determine an aggregated performance data for the selected content attribute having the particular value based on the evaluated content performance.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for content optimization, comprising: receiving a rule on probability factor, the rule on probability factor specifying a statistical probability that a piece of content expresses a particular value of a selected content attribute; generating a plurality of pieces of content in accordance with the rule on probability factor; collecting content performance data of the plurality of pieces of content when the plurality of pieces of content are in use; determining, by a processor, an aggregated performance data for the selected content attribute having the particular value based on the content performance data of the plurality of pieces of content; and modifying, by the processor, the rule on probability factor based on the aggregated performance data. 2. The method of claim 1, wherein the selected content attribute is an attribute of a content element, an attribute of a relationship, an attribute of a size or position adjustment, or an attribute of a transition. 3. The method of claim 2, wherein the rule on probability factor specifies a statistical probability that a piece of content expresses a particular value of an attribute of a relationship. 4. The method of claim 3, further comprising: modifying, by the processor, a second rule on probability factor based on the aggregated performance data, the second rule on probability factor specifying a statistical probability that a piece of content expresses a particular value of an attribute of a size or position adjustment within the relationship. 5. The method of claim 1, further comprising: determining, by the processor, a deviation value for aggregated performance data the selected content attribute based on the content performance data of the plurality of pieces of content, wherein the modifying step comprises modifying the rule on probability factor based on both the aggregated performance data and the deviation value for the aggregated performance data for the selected content attribute. 6. The method of claim 1, further comprising: receiving a set of rules for content generation; identifying a changeable content attribute based on the set of rules for content generation; and composing, by the processor, one or more rules on probability factor for the changeable content attribute. 7. The method of claim 6, wherein the composing step comprises composing the one or more rules on probability factor to assign an equal probability to each value of a set of selected values for the changeable content attribute. 8. A content optimization system, comprising: a rule management module maintaining a set of rules for content generation, the set of rules comprising a rule on probability factor, the rule on probability factor specifying a statistical probability that a piece of content expresses a particular value of a selected content attribute; a content generation module operative to generate a plurality of pieces of content based on the set of rules, the plurality of pieces of content designed with a particular optimization objective; and a content evaluation module operative to evaluate content performance on reaching the particular optimization objective based on data acquired when the plurality of pieces of content are displayed, wherein the content evaluation module is further operative to determine an aggregated performance data for the selected content attribute having the particular value based on the evaluated content performance, wherein the rule management module is operative to amend the rule on probability factor based on the aggregated content performance data. 9. The system of claim 8, wherein the selected content attribute is an attribute of a content element, an attribute of a relationship, an attribute of a metric adjustment, or an attribute of a transition. 10. The system of claim 8, wherein the content evaluation module is further operative to determine a deviation value for the aggregated performance data for the selected content attribute based on the content performance data of the plurality of pieces of content, and wherein the rule management module is further operative to modify the rule on probability factor based on both the aggregated performance data and the deviation value for the aggregated performance data for the selected content attribute. 11. The system of claim 8, wherein the rule management module is further operative to identify a changeable content attribute based on the set of rules for content generation and compose a rule on probability factor for the changeable content attribute. 12. The system of claim 11, wherein the rule management module is further operative to compose one or more rules on probability factor, wherein the one or more composed rules on probability factor assign an equal probability to each value of a set of selected values for the changeable content attribute. 13. A method comprising: receiving a first rule on probability factor and a second rule on probability factor, the first rule on probability factor defining a statistical probability that a piece of content expresses a first value of a selected content attribute, the second rule on probability factor defining a statistical probability that a piece of content expresses a second value of the selected content attribute, the second value being different from the first value; generating a plurality of pieces of content in accordance with the first rule on probability factor and the second rule on probability factor; collecting content performance data of the plurality of pieces of content when the plurality of pieces of content are in use; determining, by a processor, a first aggregated performance data for the first value of the selected content attribute based on the content performance data of the plurality of pieces of content; determining, by the processor, a second aggregated performance data for the second value of the selected content attribute based on the content performance data of the plurality of pieces of content; and modifying, by the processor, the first rule on probability factor and the second rule on probability factor based on both the first aggregated performance data and the second aggregated performance data. 14. The method of claim 13, wherein the selected content attribute is an attribute of a content element, an attribute of a relationship, an attribute of a metric adjustment, or an attribute of a transition. 15. The method of claim 13, further comprising: determining, by the processor, a deviation value for the first aggregated performance data for the selected content attribute based on the content performance data of the plurality of pieces of content, wherein the modifying step comprises modifying the first rule on probability factor based on both the first aggregated performance data and the deviation value for the first aggregated performance data for the selected content attribute.