The present invention envisages a personalized plan generation system and a method that satisfies maximum user preferences and constraints; besides including a number of enabling features like of plan repair or revision with dynamically changing situations or contextual information. Moreover, the system is able to perform a collaborative planning by opinion mining in social networks to achieve better optimization. Significantly, the explanation for the selection of plan steps or a change in plan altogether can be expressed in natural language.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of generating an optimal personalized plan executed on a planning server in collaboration with a user preference modeling system over a communicating network, the method comprising: receiving by a server computing system, at least one user-defined task and associated subtasks; extracting dynamic context information and a plurality of control points relevant to the task wherein, each of the control points indicates an optimal state; generating a primary optimal plan complying with the task, the context information each of the control points, the plan including a plurality of intermediate states to achieve a goal state; and iteratively performing a backtracking operation from the goal state to the currently executing intermediate state to identify a deviating intermediate state from the optimal state for ensuing generation of a secondary optimal plan from the identified deviated intermediate state. 2. The method of claim 1, further comprising defining the received tasks, the associated subtasks and the extracted contextual information using stored domain information, the domain information being extracted from a plurality of dynamically maintained domain ontology. 3. The method of claim 1, further comprising rendering at least one optimal plan in compliance with the user-defined task and the associated subtasks, wherein the subtasks comprise at least one of one or more user preferences, intents, and constraints associated with the task. 4. The method of claim 1, further comprising: rendering a logical explanation in a natural language for selecting at least one optimal plan by using functional structures (f-structures) of Lexical Functional Grammar to represent each intermediate state, wherein the natural language sentences are formed by: establishing relationships between the intermediate state constituting the plan by using a Rhetorical Structure Theory; and utilizing the functional structures and the established relationships between the steps (f-structures) to generate the sentences by a Natural Language Generator. 5. The method of claim 1, wherein the planning server is a domain independent Hierarchical Task Network (HTN) planner that utilizes meta-heuristics based optimization approach to achieve the said optimization. 6. (canceled) 7. The method of claim 1, wherein the dynamic contextual information is extracted from the stored domain information, plurality of sensors, or a combination thereof. 8. The method of claim 1, wherein a belief revision or truth maintenance-based technique coupled with stream reasoning techniques are utilized to detect the change in contextual information. 9. The method of claim 1, wherein the defined task and the defined contextual information are represented on a SHOP-like HTN formalism with relevant extensions for accommodating the subtasks and the constraints. 10. The method of claim 1, wherein the backtracking operation from the goal state to the current state identifies for the changing contextual information positing a threat to the goal state such that the optimized secondary plan is generated from the deviated intermediate state identified of the threat. 11. The method of claim 1, wherein the deviated intermediate state from the optimal state in the currently executing plan network is detected for a change in one or more sub tasks or the user selection for a plan change or a combination thereof such that the optimized secondary plan is generated from the immediate intermediate state detected of such deviation. 12. The method of claim 3, wherein the rendered optimal plan is the primary plan for an undetected deviation of the intermediate state and the secondary plan for a detected deviation in the intermediate state, wherein each deviation creates a revised secondary plan. 13. The method of claim 4, wherein for the each plan state, the task is iteratively decomposed to form a relational tree and a semantic structured is derived based on a causal chain in the relational tree, traversed up to a level next to root node to generate a logical explanation of selecting the plan state in the natural language. 14. A computer-implemented method of generating an optimal personalized plan on a planning server in collaboration with a user preference modeling system over a communicating network and communicatively linked to a social platform, the method comprising: receiving by a server computing system, at least one user-defined task and associated subtasks; extracting dynamic context information and a plurality of control points relevant to the task and the context information, each of the control point indicates an optimal state; generating a primary optimal plan state complying with the task, the context information and each of the control point, the plan including a plurality of intermediate states to achieve a goal state; determining a next plan state by a process of opinion mining, opinion mining further comprising: aggregating one or more opinion of multiple entities located at different sources or networks along with the subtasks contained in one or more category of the user preference modeling system, assigning an updated score to each of the aggregated opinion for prioritizing in accordance with a predetermined category score; and selecting the highest updated scored opinion thereof wherein the said updated scored opinion is derived by a combination of the entity opinion score and the category score; and iteratively performing a backtracking operation from the goal state to the currently executing intermediate state to identify a deviating intermediate state from the optimal state for ensuing generation of a secondary optimal plan state there from the deviated intermediate state. 15-24. (canceled) 25. The method of claim 14, wherein the updated assigned scores are normalized and weighted to frame a positive opinion or a negative opinion or a neutral opinion based on a predetermined threshold value. 26. (canceled) 27. The method of claim 14, wherein the predetermined category score is associated with a reward value, wherein the reward value is positive for the entities involved in generating successful optimal plan and negative for the entities involved in the plan steps, inclusion of which requires elimination of the intermediate states during plan revision. 28. The method of claim 14, wherein the scores are updated by combining an initial score and the reward value. 29-31. (canceled) 32. An optimal personalized plan generation system implemented on a planning server, the system comprising: a user preference modeling system to model a user defined task and associated subtasks; a context processing system communicating with a plurality of sensors and an ontology store management system for extracting dynamic contextual information; and a plan execution decider system for generating a primary optimal plan in compliance to the task, the subtasks and the contextual information, wherein the decider system is configured to generate a secondary optimal plan upon an indication of deviation from the user preference modeling system or the context processing system plan or a combination thereof. 33. The system of claim 32, further comprising: a user interface for receiving the at least one task and the associated subtasks from the one or more user; a defining system coupled with the user preference modeling system and a context processing system for generating the task description and a domain information description with the relevant subtasks using the ontology store management system; an opinion aggregation system implemented on a server or a cloud resource for extracting opinions of plurality of entities located at different sources or networks; a plan rendering system for rendering the at least one optimal plan in compliance with the user defined task and the associated subtasks; and a plan explanation generator system for generating natural language explanations to the user for selecting the optimal plan. 34. (canceled) 35. The system of claim 32, wherein the plan execution decider system further comprises: a monitoring system for detecting the deviation from the primary optimal plan, the plan including plurality of intermediate states; and a plan revision system to identify the intermediate state where on a plan revision condition exists. 36. (canceled) 37. (canceled) 38. The system of claim 32, wherein the ontology store management system further comprises: a database for storing the domain specific information including the user defined tasks, dependencies among the tasks, parameters governing invoking of a task, and different constraints associated with the task execution; and a ontology management tool for managing the stored information and a ontology extraction system. 39-45. (canceled)