CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims priority to Indian Provisional Patent Application No.2879/MUM/2013, filed on September 05, 2013, the entirety of which is hereby incorporated by reference.

TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to intelligent systems for planning, and more particularly to planning systems for generating a plan in a computing environment.

BACKGROUND
[003] In a field of artificial intelligence, planning is a process of selecting and organizing actions by considering the expected outcomes or goals. In the process of planning, plan state denotes a state of the world and is represented as a set of state facts. During the planning process, the state of the system undergoes changes as determined by post conditions of the actions included in the plan. A planner has to select and execute a set of actions in order to reach the goal state.
[004] The current planning techniques or particularly, the plan state representation techniques suffer from many limitations such as practicability, scalability and modularity. In real world scenario, the planning system needs to access a collection of information available from heterogeneous data sources. The heterogeneous data sources include databases, ontology, application program interface calls and web services etc. However, the existing planning techniques do not consider the heterogeneity of state information. Hence, practicability limitation in the planning systems is not solved. In practical cases, plan state may be enormously large which in turn may affect the performance of the planning system. As an example, the HTN (Hierarchical Task Network) planner JSHOP2 (a Java implementation of Simple Hierarchical Ordered Planner) suffers from scalability problem for a plan state having large number of state facts represented in a monolithic predicate-based schema. Further, during planning the complete set of information is loaded into the planning system memory whereas only a part of information is actually required at various points of executions in plan generation. Hence, loading of large set of information in the planning system memory leads to scalability problem.
[005] In general data sources follow modularity approach. For example, weather information, road information, transport information may be maintained in database systems,
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wherein separate tables are used to store the data. Therefore any change in one module, does not affect the change in other modules. The current plan state representation does not follow modularity approach as the entire information is presented using predicate-based schema.
[006] Other similar prior art planning techniques do not provide optimized use of data. The traditional planners use monolithic predicate based schema for plan state representation where a world state is described as a set of predicates that are currently true. However, an approach used by traditional planners may involve certain challenges, firstly, in reality, the world state has to be obtained by aggregating information from different modular sources represented through multiple knowledge representation techniques and secondly, a performance of a planner may be affected when a size of state is enormously large.

SUMMARY
[007] This summary is provided to introduce aspects related to systems and methods. System for generating a plan to complete a task in a computing environment and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[008] In one implementation, a system for generating a plan to complete a task in a computing environment is disclosed. The system comprises a processor and a memory coupled to the processor. The processor is capable of executing a plurality of modules stored in the memory. The plurality of modules comprises a receiving module, a planning module and an adaptation module. The receiving module is configured to receive, a problem definition. The problem definition comprises a first dataset and a second dataset associated with the task and wherein the first dataset comprises one or more logical atoms, and the second dataset comprises one or more database atoms, wherein the one or more logical atoms are represented in a first schema, and wherein the one or more database atoms are represented in a second schema. The one or more database atoms are represented with a path to selectively access the one or more database atoms stored in one or more data sources, wherein the one or more logical atoms and the one or more database atoms are required to complete the task.
[009] The problem definition further comprises a task list, wherein the task list defines the task. The receiving module is further configured to receive, a domain definition. The domain definition comprises a set of task methods, a set of task operators, wherein the set
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of task methods, and the set of task operators, are required to complete the task. The set of task methods are of non-primitive type and the set of task operators are of primitive type.
[0010] The planning module is configured to select, a task method from the set of task methods or a task operator from the set of task operators by using the problem definition and the domain definition and by matching the task with the set of task methods and the set of task operators. The adaptation module and the planning module are configured to execute at least one of the task method and the task operator in order to complete the task. Further in order to execute the task method or to execute the task operator, the adaptation module is configured to verify whether a precondition associated with one of the task method and the task operator is valid, wherein the precondition is a logical expression of one or more variables, wherein the precondition is verified by comparing the one or more variables with the one or more logical atoms or the one or more database atoms in order to match the one or more variables with the one or more logical atoms or the one or more database atoms, and wherein the comparing of the one or more database atoms is facilitated using the path.
[0011] Further, to execute the task method or to execute the task operator, the adaptation module is configured to assign the one or more variables with one or more values when the precondition is valid. The one or more values are obtained from the one or more logical atoms or the one or more database atoms so matched with the one or more variables. Further to execute the task method, the planning module is configured to decompose the task method into one or more sub-tasks when the task method is selected, and assign one or more arguments of the task method to the one or more sub-tasks‟ arguments, and add the one or more sub-tasks to the task list in order to execute the one or more subtasks when task method precondition is valid. Based on the execution of the task operator, the adaptation module is configured to modify the one or more logical atoms (as required), and/or modify the one or more database atoms (as required), and wherein the modification of the one or more logical atoms and the one or more database atoms is performed when task operator precondition is valid.
[0012] The adaptation module and planning module are configured to execute the one or more sub-tasks by iteratively performing the selecting, the executing, for each of the one or more sub-tasks from the task list. The planning module is further configured to generate the plan by supporting the execution of the task and the one or more sub-tasks mentioned in the task list. The plan is generated based on the execution of the task and each of the one or more sub-tasks mentioned in the task list
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[0013] In one implementation, a method for generating a plan to complete a task in a computing environment is described. The method comprises receiving a problem definition, wherein the problem definition comprises a first dataset and a second dataset associated with the task. The first dataset comprises one or more logical atoms, and the second dataset comprises one or more database atoms, wherein the one or more logical atoms are represented in a first schema, and wherein the one or more database atoms are represented in a second schema. The problem definition further comprises a task list and the task list is in form of task network, wherein the task list defines the task. The method comprises receiving, a domain definition. The domain definition comprises a set of task methods, a set of task operators, wherein the set of task methods are of non-primitive type and the set of task operators are of primitive type. The one or more database atoms are represented with a path to selectively access the one or more database atoms from the one or more data sources, wherein the one or more logical atoms and the one or more database atoms are required to complete the task. The method comprises selecting a task method from the set of task methods or a task operator from the set of task operators by matching the task head with each task method‟s head from the set of task methods and by matching the task head with each task operator‟s head from the set of task operators. The method further comprises executing at least one of the task method and the task operator in order to complete the task. The execution of the task method or the task operator comprises verifying whether a precondition associated with one of the task method and the task operator is valid, wherein the precondition is a logical expression of one or more variables, wherein the precondition is verified by comparing the one or more variables with the one or more logical atoms or the one or more database atoms in order to match the one or more variables with the one or more logical atoms or the one or more database atoms, and wherein the comparing of the one or more database atom is facilitated using the path. The execution of the task method or the task operator further comprises assigning the one or more variables with one or more values when the precondition is valid, wherein the one or more values are obtained from the one or more logical atoms or the one or more database atoms so matched with the one or more variables,
[0014] The method further comprises decomposing the task method into one or more sub-tasks when task method is selected, and assigning the values of one or more arguments of the task method to the one or more sub-tasks‟ arguments. The method further comprises adding the one or more sub-tasks to the task list in order to execute the one or more subtasks. The method further comprises executing the one or more sub-tasks by iteratively performing
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the selecting, the executing each of the one or more sub-tasks from the task list. The method further comprises generating the plan based upon the execution of the task and the one or more sub-tasks from the task list. The plan is generated based upon the execution of the task and each of the sub-tasks from the task list. The steps of the method, such as, the receiving, the selecting, the executing, the verifying, the assigning, the decomposing, the assigning, the adding, the executing and the generating are performed by means of a processor.
[0015] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for generating a plan to complete a task in a computing environment is disclosed. The program comprising a program code for a receiving a problem definition, wherein the problem definition comprising, a first dataset and a second dataset associated with the task, wherein, and wherein the first dataset comprises one or more logical atoms, and the second dataset comprises one or more database atoms, wherein the one or more logical atoms are represented in a first schema, and wherein the one or more database atoms are represented in a second schema. The problem definition further comprises a task list, wherein the task list defines the task.
[0016] The program comprising a program code for a receiving a domain definition comprising a set of task methods, a set of task operators, wherein the set of task methods are of non-primitive type and the set of task operators are of primitive type. The program comprising a program code for selecting a task method from the set of task methods or a task operator from the set of task operators by using the problem definition and the domain definition and by matching the task data with the set of task methods and the set of task operators.
[0017] The program comprising a program code for executing at least one of the task method and the task operator in order to complete the task, the execution of the task method or the execution of the task operator comprises and verifying whether a precondition associated with one of the task method and the task operator is valid, wherein the precondition is a logical expression of one or more variables, wherein the precondition is verified by comparing the one or more variables with the one or more logical atoms or the one or more database atoms in order to match the one or more variables with the one or more logical atoms or the one or more database atoms, and wherein the comparing of the one or more database atom is facilitated using the path,

The program further comprise a program code for assigning the one or more variables with one or more values when the precondition is valid, wherein the one or more values are 7
obtained from the one or more logical atoms or the one or more database atoms so matched with the one or more variables.
[0018] The program further comprising a program code for decomposing the task method into one or more sub-tasks when the task method is selected, and assigning one or more arguments of the task method to the one or more sub-tasks‟ arguments, and adding the one or more sub-tasks to the task list in order to execute the one or more subtasks. The program further comprises a program code for executing the one or more sub-tasks by iteratively performing the selecting, the executing for each of the one or more sub-tasks from the task list and a program code for generating the plan based upon the execution of the task and the one or more sub-tasks.

BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[0020] Figure 1 illustrates a network implementation of a system for generating a plan to complete a task in a computing environment is shown, in accordance with an embodiment of the present subject matter.
[0021] Figure 2 illustrates the system for generating the plan to complete the task in the computing environment, in accordance with an embodiment of the present subject matter.
[0022] Figure 3 illustrates a framework of the system for generating the plan to complete the task in the computing environment, in accordance with an embodiment of the present subject matter.
[0023] Figure 4 illustrates a process flow chart for generating the plan to complete the task in the computing environment, in accordance with an embodiment of the present subject matter.
[0024] Figure 5 illustrates a method for generating the plan to complete the task in the computing environment, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION
[0025] System(s) and method(s) for generating a plan to complete a task in a computing environment are described. The system provides a framework that facilitates use
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of heterogeneous data sources to represent a plan state in order to generate the plan. The system and method facilitates representation of the plan state. The plan state representation comprises combination of information in predicate form as well as references to information in non-predicate form stored in a variety of data sources like databases, ontologies etc. The plan state representation using information from distributed heterogeneous data sources, without altering planning algorithm in concern is disclosed. Hence according to the present disclosure, the system provides a framework. The framework is capable of handling the non-predicate based data along with the predicate based data and a provision to access the non-predicate data from plan state on demand basis. The key aspect of the present disclosure is to handle the overheads caused by non-predicate based data without changing the main planning process.
[0026] The system and method for generating the plan may comprise receiving one or more logical atoms, and one or more database atoms. The one or more logical atoms may be represented in a first schema, and the one or more database atoms may be represented in a second schema. The first schema may be predicate based schema. The logical atoms may be represented using predicate logic. The second schema may be non-predicate based schema and the database atom‟s representation comprises a path to selectively access the database atom stored in one or more data sources. The system and method may further receive a task list in a form of task network, set of task methods and set of operators required to complete the task. The task list defines task.
[0027] Further, the system and method select and execute a task method and/or a task operator iteratively from the set of task methods and the set of task operators to complete the task. The execution of the task method and the task operator iteratively may comprise verifying precondition associated with the task method and the task operator for validity. Precondition may be a logical expression of variables mentioned in the task method representation and the task operator representation. The precondition may be verified by comparing the variables with the logical atoms or the database atoms in order to match the variables with the logical atoms or the database atoms.
[0028] When the precondition is valid, the variables are assigned with the values obtained from the logical atoms and/or the database atoms so matched with the variables. When the task method is selected, the task method is decomposed into sub-tasks, and the values of the arguments of the task method is assigned to the arguments of the sub-tasks, and the sub-tasks are added to the task list in order to execute the subtasks. The sub-tasks may be
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executed by iteratively performing the selection and the execution of each sub-task. Further, the system may generate the plan based upon the execution of all the tasks and sub-tasks form the task list.
[0029] While aspects of described system and method for generating a plan to complete a task in a computing environment may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[0030] Referring now to Figure 1, a network implementation 100 of a system 102 for generating a plan to complete a task in a computing environment is illustrated, in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 selects a task method from the set of task methods and/or a task operator from the set of task operators and executes the task method and/or the task operator. In one embodiment, the system 102 verifies whether a precondition associated with the task method and/or the task operator is valid. After verifying the precondition, one or more variables of the precondition are assigned with one or more values when the precondition is valid. In another embodiment, when the task method is selected, the system decomposes the task method into one or more sub-tasks and iteratively executes the one or more sub-tasks in order to complete the task. In another embodiment, the system generates the plan based on the execution of the task and each of the sub-tasks from the task list.
[0031] Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user devices 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[0032] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the
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different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0033] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[0034] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0035] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0036] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks, functions or implement particular abstract
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data types. In one implementation, the modules 208 may include a receiving module 212, a planning module 214, an adaptation module 216, and other modules 218. The other modules 218 may include programs or coded instructions that supplement applications and functions of the system 102.
[0037] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a system database 222, and other data 224. The other data 224 may include data generated as a result of the execution of one or more modules in the other module 218.
[0038] In one implementation, at first, a user may use the client device 104 to access the system 102 via the I/O interface 204. The user may register using the I/O interface 204 in order to access the system 102. The working of the system 102 may be explained in detail by referring to details of Figures 2 and 3. The system 102 may be used to generate a plan. The system 102 may be used to generate the plan to complete a task in a computing environment. In order to complete the task, the system 102, at first, receives data. Specifically, in the present implementation, the system receives data through the receiving module 212.
[0039] Referring to Figure 2, detailed working of the system 102 is illustrated, in accordance with an embodiment of the present subject matter. Referring to figure 2, the detailed working of the receiving module 212 along with the working of other components of the system 102 is illustrated, in accordance with an embodiment of the present subject matter. The receiving module 212 is configured to receive a problem definition. The problem definition comprises a first dataset and a second dataset. The first dataset and the second dataset are associated with the task. By way of an example the task may be booking a ticket, moving packages between locations by trucks under certain spatial constraints and delivering deadlines, finding a sequence of biochemical (pathways) reactions in an organism producing certain substances, traveling and buying goods at selected markets minimizing costs etc. The first dataset is generated and the second dataset is received from one or more data sources. The first dataset comprises one or more logical atoms. The second dataset comprises one or more database atoms. The one or more logical atoms may be represented in a first schema. The one or more database atoms may be represented in a second schema. The first schema may be a predicate based schema. The one or more logical atoms may be represented using predicate representation. The second schema may be a non-predicate based schema. The one or more database atoms may be represented comprising the path to selectively access the one
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or more data sources. The one or more data sources comprise databases, ontologies, application program interface calls, web services, or a combination thereof.
[0040] In a field of artificial intelligence, planning is a process of selecting and organizing actions by considering expected outcomes or goals. In the planning process, a plan state denotes a state of a world. The plan state is represented as combination of a set of logical atoms and a set of database atoms that may change during the planning. During the planning process, the state of the plan system i.e. the plan state may undergo change as determined by post conditions of the actions included in the plan. The plan state S mentioned may be represented by equation (1)

............Equation (1)
In equation (1), „la‟ represents a logical atom. The logical atom may be represented in predicate form. The logical atom comprises a logical atom head and a set logical atom arguments. For example, a fact – “amount of available cash is 10,000” may be represented in the plan state as the logical atom „available cash‟ represented as (avail-cash 10000). Here, avail-cash is the logical atom head and 10000 is an argument of the logical atom. The prior art plan state representation suffers from shortcomings such as practicability, scalability and modularity. Hence the plan state representation has become a requirement towards improvement.
[0041] According to an embodiment of the present disclosure, a modification in plan state is disclosed. The system comprises a framework to provide the plan state representation in a modified form. The modification in the plan state comprises representation of the plan state using combination of the one or more logical atoms and the one or more database atoms. The plan state representation using combination of the one or more logical atoms and the one or more database atoms provides a support for use of heterogeneous data sources (such as for booking flight ticket various data sources of agents, flights, weather can be used).
[0042] According to an embodiment of the present disclosure, the plan state comprises the first data set and the second dataset. The first dataset comprises the one or more logical atoms. The one or more logical atoms may be represented in predicate form. The one or more logical atoms may represent frequently changeable data (dynamic data) using predicate based schema. The predicate based schema indicates representation of the one or more logical atoms in the predicate form. The predicate form is a symbolic formal system used to represent facts or information that is true. By way of the above explained example a
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fact or information is “amount of available cash is 10,000”. The predicate representation of the fact is (avail-cash 10000). By way of an example, available cash of a person is represented using logical atom in predicate form as an application of different operators on the logical atom may change value of the logical atom frequently. The logical atoms related to available cash, agent, flight, hotel may be represented in predicate form as discussed below.

(avail-cash 10000)
(agent thomascook 500)
(agent reachfar 600)
(flight-info flight IC814 kolkata london 40000 9 22 13 1)
(flight-info flight KF334 kolkata london 30000 11 23 12 0)
(hotel-info princess-hotel london 4 3500)
(hotel-info travelodge london 3 3600)
[0043] The second dataset comprises the one or more database atoms. The one or more database atoms may be represented in non-predicate schema. The non-predicate schema indicates representation of the one or more database atoms in non-predicate form. In one embodiment, non-predicate representation is a representation other than predicate representation. An example of non-predicate form of representation is natural language. By way of a non limiting example, state facts may be represented as „amount of available cash is 10,000, 6 is even number‟. In one embodiment, less frequently changing data or not changing data (static data) may be stored in one or more database atoms in non-predicate form. Thus by way of storing the less frequently changing data or non-changing data (static data) in a second schema i.e. non-predicate schema the frequent access of the data and frequent modification can be avoided. By way of an example, an agent's information (travel agent name, address, contact number, charge etc.), or flight information (flight name, flight number, source, destination, cost, number of hops, departure, arrival, duration etc.), or the hotel information (hotel name, location, class, type, price etc.) of a city is represented using non-predicate data source such as a database atoms as shown below.

(:import-db mysql agentDB jdbc:mysql://localhost:3306 user user123 agent)
(:import-db mysql flightDB jdbc:mysql://localhost:3306 user user123 flight) 14
(:import-db mysql hotelDB jdbc:mysql://localhost:3306 user user123 hotel)
[0044] Referring to figure 2 and 3, the receiving module 212 is configured to receive a task list in the problem definition, the task list defines tasks. The receiving module 212 is further configured to receive a domain definition. The domain definition comprises a set of task methods and a set of task operators. The set of task methods are of non-primitive type and the set of task operators are of primitive type. The primitive type of task operator indicates atomic work which can be directly accomplished by operator. E.g. “!set-agent” denotes a task operator of primitive type. The non-primitive type of task method indicates the work or action that may not be accomplished directly and need to be decomposed into subtasks. E.g. “BookTicket” denotes task method of non-primitive type.
[0045] The logical representation of the task method comprises the task method precondition and the one or more subtasks. The task method precondition comprises of logical precondition and database precondition. The logical representation of the task operator comprises the task operator precondition and an effect list. The task operator precondition comprises of the logical precondition and the database precondition. The effectlist comprises instructions for deletion or addition of the one or more logical atoms or deletion or addition the one or more database atoms. The task method is decomposed into subtasks if the pre-condition mentioned in the task method is satisfied by the plan state that is the first dataset and the second dataset.
[0046] In the following example of a task method, the task “BookTicket” is first unified with task method head. The task is decomposed into a subtask “agent” if the precondition “(choose ?p)” is satisfied by plan state.

(:method(BookTicket ?x ?y ?z ?m ?p) //task method head and arguments
((choose ?p)) //task method precondition
((agent ?x ?y ?z ?m))) //decomposed task
[0047] The task operator may complete the work/action/task if the pre-condition mentioned in the operator is satisfied by the plan state. As an effect of applying the task operator some of the logical atoms may be deleted from the first dataset or second dataset (if permitted) and some of the logical atoms may be added into the first dataset or second dataset (if permitted). For example, the task operator may complete a action/primitive task “set-agent” if the precondition “(avail-cash ?x)” is satisfied by the current plan state i.e. the first dataset and second dataset. As an effect the logical atom “(avail-cash ?x)” is deleted and two
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logical atoms such as booking details i.e.“(booked-through ?a)” and updated available cash after booking i.e. “(avail-cash (call - ?x ?b))” are added to the first data set. Following are the example of representation and working of the task operator.

(:operator (!set-agent ?a ?b) //task operator head and argument
((avail-cash ?x)) //task operator precondition
((avail-cash ?x)) //task operator delete list
((booked-through ?a)(avail-cash (call - ?x ?b)))) //task operator add list
[0048] Referring to figure 2 and 3, the receiving module 212 is further configured to receive a problem definition. The problem definition may comprise one or more logical atoms, one or more database atoms, and the task list. The one or more database atoms are represented with a path to selectively access the one or more database atoms from the one or more data sources. The one or more logical atoms and the one or more database atoms are required to complete the task.
[0049] The problem definition is a file containing description of the information to be used to complete the task. The information required to complete the task comprises one or more logical atoms, one or more database atoms and the task list. In other words, the problem definition is the plan state in which the task may be completed. By way of an example, the task is to book a ticket and the primary information/data required to complete the task of booking the ticket. The primary information comprises three logical atoms such as available cash information and information about two agents. The task is to book a ticket from Kolkata to London and using cash upto 30000, using a flight via an agent. By way of an example, the problem definition for the task to book a ticket is represented as followed.

(defproblem problem CITY
----------------------Initial plan state-----------------------
(
(avail-cash 100000)
(agent thomascook 500)
(agent reachfar 600)
) 16
--------------Task list for a task of „Bookticket‟--------------------
((BookTicket kolkata london 30000 flight agent)))
[0050] Referring to figure 2 and 3, the receiving module 212 is further configured to receive a domain definition. The domain definition comprises a logical representation of the set of task methods, a logical representation of the set of task operators. The set of task methods and the set of task operators provided in the domain definition are required to complete the task. The domain definition comprises actions namely – a set of task methods, a set of task operators used to complete the task mentioned in the task list. The domain definition is provided by a domain descriptor and the problem definition may be provided by the user.
[0051] Still referring to figure 2, the system 102 comprises the planning module 214. The planning module 214 is configured to select a task method from the set of task methods or a task operator from the set of task operators. According to an embodiment of the present invention, the task method or the task operator may be selected by using the problem definition and the domain definition and by matching the task with the set of task methods and the set of task operators. The matching of the task with the set of task methods and the set of task operators may comprise matching of a task head with the task method head or matching of the task head with the task operator head. The task method head is mentioned in the representation of the task method and the task operator head is mentioned in the representation of the task operator. The task head is compared with the task method head of each task method from the set of task methods. The task head is compared with the task operator head of each task operator from the set of task operators. The matching of the task with the set of task methods and the set of task operators is called as unification of the task method or the task operator. The unification of the task method or the unification of the task operator may be done by matching the task name (known as task-head) with the task method name (known as method-head) or the task operator name (known as operator-head). The task can be unified with more than one task method or more than one task operator at a time. But, only one task method or one task operator is selected by the planning module to complete the task.
[0052] Referring to figure 2, the system 102 comprises the adaptation module 216 and planning module 214 configured to execute at least one of the task method and the task operator in order to complete the task. The execution of the task method or the execution of
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the task operator comprises, verifying whether a precondition associated with one of the task method and the task operator is valid. The precondition is a logical expression of one or more variables. The precondition is verified by comparing the one or more variables with the one or more logical atoms or the one or more database atoms in order to match the one or more variables with the one or more logical atoms or the one or more database atoms. The comparing or matching of the one or more database atom is facilitated using the path mentioned in the representation of the one or more database atoms. The precondition associated with the task method is the method precondition defined in the task method and the precondition associated with the task operator is the operator precondition defined in the task operator. In the following example, precondition of the task method “BookTicket” is (choose ?p). The task “BookTicket” may be decomposed into a subtask “agent” if the precondition “(choose ?p)” is satisfied by plan state.

(:method(BookTicket ?x ?y ?z ?m ?p) //task method head and arguments
((choose ?p)) //precondition
((agent ?x ?y ?z ?m))) //decomposed task
[0053] In the following example, task operator “set-agent” can be applied to complete a task “set-agent” if the precondition “(avail-cash ?x)” is satisfied by the current plan state i.e. the first dataset and second dataset. As an effect the logical atom “(avail-cash ?x)” is deleted and two logical atoms such as booking details i.e.“(booked-through ?a)” and updated available cash after booking i.e. “(avail-cash (call - ?x ?b))” are added to the first data set. Following are the example of representation and working of the task operator.

(:operator (!set-agent ?a ?b)
((avail-cash ?x))
((avail-cash ?x))
((booked-through ?a)(avail-cash (call - ?x ?b))))
[0054] According to an embodiment of the present disclosure, the precondition associated with the task method and/or the precondition associated with the task operator may be NULL. NULL implies no precondition. In one embodiment, the „addlist‟, the „deletelist‟ part of the task operator may also be NULL.
[0055] According to an embodiment of the present subject matter, modification in the precondition is provided. The precondition is represented as a logical expression of predicate
18

based logical atoms as well as non-predicate based database atoms. The precondition is represented as a logical expression of predicate based logical atoms as well as non-predicate based database atoms. Modification in the task operator‟s effect is obtained which refer to the modifications in the predicate based logical atoms as well as modifications in the non-predicate based database atoms based on the requirement. The database atoms are stored in the data sources. Further, the system and method of the present disclosure handles the overhead caused by the database atoms by use of the adaptation module 216 without modifying the main planning process. The overhead caused by the non-predicate based database atoms may be connection establishment to the data sources to access the database atoms, to fetch the database atom, to verify the precondition associated with the database atoms etc.
[0056] The adaptation module 216 is further configured to add a connection to the second dataset to access a database atom and fetch the database atom by using the path as represented in the database atom. The one or more logical atoms and the one or more database atoms are supplied to the adaptation module 216. In accordance with an exemplary embodiment, the adaptation module 216 may be plugged in with other planners like classical planner to avail the advantages mentioned in the present subject matter. The adaptation module 216 can also be reused for data gathering purpose. In the proposed framework the problem definition and domain definition are two inputs to the adaptation module.
[0057] The adaptation module 216 is configured to assign the one or more variables with one or more values when the precondition is valid. The one or more values are obtained from the at least one logical atom and/or the at least one database atom so matched with the one or more variables.
[0058] The adaptation module 216 is configured to execute the task operator. The execution of the task operator comprises verifying the precondition associated with the task operator and if the precondition is satisfied, modifying the one or more logical atoms from the first dataset and/or modifying the one or more database atoms from the second dataset.
[0059] According to an embodiment of the present subject matter, when the task operator is selected and the precondition is valid, the adaptation module 216 is further configured to modify, the logical atom, and/or the database atom as mentioned into the task operators effect part. The modification of the at least one logical atom and the at least one database atom is based on execution of the task operator.
19

[0060] The adaptation module 216 is further configured to add new one or more logical atoms to the first dataset and to add new one or more database atoms to the second dataset based on the execution of one of the task operator. The new one or more logical atoms, are not present in the first data set initially and the new one or more database atoms, are not present in the second data set initially. According to an embodiment of the present subject matter, cache memory is used to store the one or more database atoms fetched. The adaptation module 216 fetches the one or more database atoms from the data sources to verify the precondition associated with the one or more database atoms. The one or more database atoms so fetched may be needed in near future to verify another precondition. To reduce the same one or more database atoms fetching overhead, the so fetched one or more database atoms are kept into the cache memory. Therefore, while verifying another precondition, if the adaptation layer gets a non-predicate precondition, first the adaptation module checks the one or more database atoms into the cache memory. Further, if the one or more database atoms are not matched with a database atom mentioned in the precondition, then the adaptation module 216 checks into data source using the path mentioned in the database atom.
[0061] The planning module is further configured to decompose the task method into one or more sub-tasks when the task method is selected. The planning module is further configured to assign one or more values of arguments of the task method to one or more sub-tasks‟ arguments. The planning module is further configured to add the one or more sub-tasks to the task list in order to execute the one or more subtasks. The adaptation module and planning module is further configured to execute the one or more sub-tasks. The one or more sub-tasks are executed by iteratively performing the selecting of the sub task from the task list.
[0062] The tasks and the sub-tasks are selected based on priority, as mentioned in the task list. Further, the execution of sub-task comprises the selection of a task method from the set of task methods or a task operator from the set of task operators by using the problem definition and the domain definition and by matching the sub-task head with each task method‟s head from the set of task methods and by matching the sub-task head with each task operator‟s head from the set of task operators. The execution of sub-task further comprises executing at least one of the task method and/or the task operator so selected in order to complete the sub-task. The sub-task is executed using the same steps as mentioned in the execution of the task.
20

[0063] The execution of the at least one of the task method and the task operator comprises verifying whether a precondition associated with one of the task method and the task operator is valid. The precondition is a logical expression of one or more variables. The precondition is verified by comparing the one or more variables with the one or more logical atoms or the one or more database atoms in order to match the one or more variables with at least one logical atom or at least one database atom. The comparing of the one or more database atom is facilitated using the path provided in the database atom representation.
[0064] According to an embodiment of the present subject matter, the adaptation module 216 facilitates optimized use of the data and the optimized use of the memory in the planning process. In the present disclosure, the adaptation module 216 verifies the precondition part with the first dataset and the second dataset. The precondition may be associated with the task method or the task operator. The precondition is a combination of logical atoms and/or database atoms. The logical atom may be the predicate based state fact that is information represented in predicate form. The database atoms may be the non-predicate based state facts that is the information represented in non-predicate form. By way of an example, if the precondition associated with the task method is “(:sort-by ?q <(mysql agentDB jdbc:mysql://localhost:3306 agent ?p/aname ?q/charge))”, the adaptation module 216 connects to the database “agent”, sorts the entries from the database agent based on “charge” and returns a tuple with least value of charge associated with the agent. Whereas in the prior art techniques, the agent information represented as (agent thomascook 500) and (agent reachfar 600) need to be loaded into main memory of the planning system since agent thomascook and agent reachfar are declared using predicate based schema.
[0065] The execution of the at least one of the task method and the task operator comprises assigning the one or more variables with one or more values when the precondition is valid. The one or more values are obtained from the one or more logical atoms or the one or more database atoms so matched with the one or more variables. Hence, execution of the one or more sub-tasks comprises iteratively performing the selecting of the task method and/or the task operator, the executing of the task method and/or the task operator for each sub-task from the task list. The above said steps are performed iteratively for each sub-task from the task list by selecting and executing each sub-task from the task list till there is no sub-task remaining in the task list. The execution of the sub-task comprises the verifying the precondition, the assigning the one or more variables, the decomposing the task method, the
21

assigning one or more arguments and adding the one or more sub-tasks to the task list. The sub-task is executed as execution of the task.
[0066] Referring to figure 2 and figure 3, the planning module 214 is further configured to generate the plan based upon the execution of at least one of the task and the one or more sub-tasks. The plan is generated by using the problem definition and the domain definition. In the given example of completing the task “Bookticket” is to execute “agent”, “set-agent”, “book-via-agent”, “book-transport-flight”, “book-ticket” methods and operators.
[0067] Referring to figure 4, working of the system 102, in accordance with an embodiment of the present disclosure is explained. In step 401, a task list is received by the receiving module. Further, in order to accomplish the task from the task list, in step 402, the planning module (212) unifies the chosen task with a task method or a task operator, matching a task head with the task methods‟ head (for non-primitive task) or matching a task head with the task operators‟ head (for primitive task) based on the source of task methods and task operators stored in the system database. The planning module selects the unified task method(s) or unified task operator(s) and may pass the unified task method(s) or unified task operator(s) to the adaptation module (216). In step 403, the adaptation module (216) then verifies the precondition of the task method(s) and task operator(s), with respect to the current plan state. The plan state comprises a first dataset comprising a plurality of logical atoms and a second dataset comprising plurality of database atoms. The pluralities of logical atoms are represented in predicate passed schema and plurality of database atoms are represented in non-predicate based schema. A precondition is a logical expression of variables indicative of logical atoms and database atoms. In step 404, the variables (constituents) of the precondition (logical expression) are verified whether the variables (constituents) are logical atoms (predicates) or database atoms (non-predicates). So, satisfaction of the precondition depends on the matching of the logical atoms and database atoms from the plan state with the variables (constituents) of the logical expression. The matching of the logical atoms and database atoms from the plan state with the variables of the precondition (logical expression) is carried out in step 405 and 406 respectively. If there are no such logical atoms or the database atoms present in the plan state, the truth value of precondition becomes false, otherwise true. When the truth value of the precondition becomes false, the system 102 exits and stops.
[0068] Further, in step 405, the adaptation module verifies availability for the set of logical atoms in the plan state. In step 406, for verifying the database atoms, adaptation
22

module fetches specified data and checks that the data present in the data source can satisfy the precondition or not. The satisfaction of the verification associated with the logical atoms indicates presence of the logical atoms in the first dataset. Satisfaction of the verification associated with the database atoms indicates presence of the database atoms in the second dataset. For successful verification, the adaptation module 216 substitutes variables mentioned in the precondition with the grounded value. The grounded values are the values obtained from the one or more logical atoms or the one or more database atoms that satisfies the precondition.
[0069] The adaptation module 216 further assigns the grounded value to the uninstantiated argument list of the task method or the task operator. By way of an example, in precondition (choose ?p), „p‟ is „uninstantiated‟ variable which is instantiated by value „agent‟. Next, in case of the task method, in step 408, the planning module selects an applicable task method or task operator and in step 409 the planning module decomposes the task methods into sub-tasks and the planning module adds the decomposed task of the task method to the task list. For task operator in step 408, the adaptation module executes the task operator‟s effect part. The task Operator‟ effect part contains “deletelist” and “addlist”. If the “deletelist”/ “addlist” refers to a modification in the one or more predicates (logical atoms), then the adaptation module 216 in step 410, modifies the predicate(s) accordingly. Otherwise, the adaptation module 216 in step 411, checks for the permission of modification for non-predicate data from the data sources (in the case of dynamic data maintained in non-predicate based data sources). If the modification of the non-predicate data is permitted and required, then in step 412 adaptation module connects to the data sources and modifies that is adds or deletes or updates the non-predicate data accordingly.
[0070] The method steps as described above are repeated until all the sub-tasks of task list are completed. The process also exits if no step as above mentioned can accomplish any of the sub-tasks. Figure 4 represents the process flowchart. Whenever the control passes from planning module to adaptation module or vice versa, the passing of control is mentioned within the bracket in the process flowchart.
[0071] According to an embodiment of the present subject matter, implementation of the system 102 for generating the plan to complete the task in the computing environment is provided. Incorporation of use of heterogeneous data sources in the planning system requires significant modification in grammar rule as well as in the domain definition and the problem definition. The modifications in the grammar rule, the domain definition and the problem
23

definition are provided by way of an example considering database as the non-predicate information source is provided.
[0072] The modification in grammar rule is explained. The keywords (e.g. and, call, imply etc.) and grammar terminals (e.g. :method, :sort-by, :first etc.) used by the problem definition and the domain definition is mentioned in JSHOP2 grammar. A new grammar terminal :import-db to differentiate database information from information represented by predicate based schema in the plan state is disclosed. Any logical expression starts with :import-db refers to a database.
[0073] The modification in the problem statement is explained. The problem statement requires following modifications. The modification in the plan state is explained in equation (2).

……Equation (2)
In equation (2), „‟ represents logical atom and represents database atom. Each database belongs to a server. So, a server name, a database name and a table name needs to be mentioned in the database atom. In order to establish connection with database driver settings, user name and password is also required. Hence the driver settings, the user name and the password is also mentioned in the database atom. Syntactically the database atom is represented as shown below.
(:importdb servername dbname driversettings username password)
Wherein server name implies the name of server, db name implies database name. driver settings, user name, password denotes driver setting, username and password respectively. When the adaptation module gets a logical atom (predicate) while reading the problem definition, the adaptation module checks whether the logical atom is used (mentioned in) by the domain definition or not. If the logical atom is used (mentioned) in the domain definition, the logical atom (predicate) is loaded in memory else discarded. If the adaptation module finds a database atom having a reference to the database that is a path mentioned in the database atom to access the database, the adaptation module establishes a connection with the corresponding database with the help of the path provided in the database atom. The adaptation module adds the connection to the second dataset of the plan state. The connection is used in later stage of planning process may be for precondition checking, data modification etc. The complete plan state comprising the first dataset, the second dataset is passed to the planning module at the initial stage of planning process. 24

[0074] The modification in domain definition is explained. The domain definition also requires some syntactic, operational modifications. The modification in precondition as mentioned in the domain definition is explained. The domain definition comprises the task methods and the task operators that are used by the planning module and the adaptation module to complete the task. Both the task method and the task operator have a precondition part (P) represented as,

…..Equation (3)
…….. Equation (4)
Precondition mentioned above is associated with both (task method and task operator). Equation 3 and 4 represents syntax of precondition.
[0075] As shown in above Equation (3) said precondition represents the logical precondition which is actually logical expression or first satisfier precondition or sorted precondition. Precondition compels planning module (along with adaptation module) to consider only the first set of bindings with respect to plan state that satisfies . Planning module (along with adaptation module) may not consider the next bindings even if the first bindings do not lead to a valid plan. instructs the planning module to consider bindings in decreasing or increasing order using fid depending on the values of variable . The logical expression may be a logical atom or any complex expression of conjunctions, disjunctions, negations, implications, universal quantifications, assignments, or call expressions. The modified logical expression of the precondition is provided as in Equation 5 below.

……… Equation (5)
[0076] Here as provided in Equation (5), a database atom consists of a head and an argument list. The database atom head contains server name, database name, driver setting and the table name. The argument list of database atom refers to the uninstantiated variables.
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The uninstantiated argument list is instantiated with the values obtained from database tables. The uninstantiated variables mentioned in the precondition are instantiated by the values fetched from the database. If a data associated with the database atom needs to be fetched from the database table with restriction (some specific value for a particular column), the column name needs to be mentioned within third bracket of the database atom representation. Otherwise, if a data needs to be fetched without restriction, the column names of the table may or may not be mentioned. In this case, declaration of the column names is optional. The variables of database atom (da) are substituted after precondition checking. Syntactically the basic database precondition is represented as,

(servername_dbname_driversettings_tablename arg1[/col1] ... argm[/colm]).
[0077] When the adaptation module reads the basic database precondition, the adaptation module uses the already established connections and obtains data from the databases by executing a sql query mentioned below by way of an example.

SELECT * FROM table name where colk = argk.
[0078] The plan state may contain more than one logical atom (predicate) that satisfies the precondition. The logical expression mentioned in the precondition can filter the logical atoms (predicates) that satisfy the precondition by applying restrictions such as call, sort-by etc. For example, the logical expression starts with sort-by, sorts the logical atoms (predicates) on some arguments mentioned in the argumentlist and passes the top most predicate to the adaptation module. In case of database precondition these restrictions are handled by formulating equivalent sql query. An example of such precondition and equivalent sql query is provided below. Sort-by database precondition:

(:sort-by ?col <(servername_dbname_driversettings_tablename arg1[/col1] ... argm[/colm)).
Equivalent sql query:
SELECT * FROM table name ORDER BY col ASC;
[0079] The modification in effect other than the precondition part such as syntactic modification is also required in the task operator‟s effect part that is in addlist and deletelist is explained. The addlist and deletelist can change database entry. It may be preferred to keep static data in the database table and therefore no modifications are required (insert and delete). Addlist can refer update and addition of value in the database table and deletelist may
26

refer deletion of data in the database table. Syntactically, the addlist and deletelist are represented as similar database precondition expression.

(servername_dbname_driversettings_tablename arg1[/col1] ... argm[/colm]).
[0080] Referring to above said addlist and deletelist the server name, the database (db) name, driver settings, table name follows the same notation as mentioned earlier. If the adaptation module 216 encounters a logical atom (predicate) in the addlist and deletelist, the adaptation module adds or deletes corresponding logical atom (predicate) in the first dataset. If the addlist and deletelist refers to a database record, adaptation module first checks the write permission of that database. If permitted, for addlist the adaption layer executes equivalent sql query by way of an example shown below,

INSERT INTO table name VALUES (arg1, ...,argm);
And for delete list following query is executed.
DELETE FROM table name WHERE colk = argk;
[0081] According to an exemplary embodiment of the present disclosure, the working of the system 102 to for generating the plan to complete the task in the computing environment is explained. By way of an example, the task is to book a ticket by selecting an agent and a flight. The domain definition consists of four task methods and two task operators. The functionality of each task method and each task operator is discussed below. The task method details are provided as herein.
[0082] Task Method - BookTicket: checks the option for booking ticket and chooses booking according to the option. Task Method - agent: finds agents, their charges and selects an agent with least charge. Task Operator - set-agent: add an agent name in plan state, modify available cash after selecting the agent. Task Method - book-via-agent: selects flight as transport mode. Task Method - book-transport-flight: checks flight options and selects a flight for given source and destination whose fare is less than the available cash. Task Operator - book-ticket: adds flight number in plan state and modifies the cash available after booking the flight.
[0083] The domain definition is given below.

(defdomain CITY)
( 27
(:method(BookTicket ?x ?y ?z ?m ?p)
((choose ?p))
((agent ?x ?y ?z ?m)))
(:method (agent ?x ?y ?z ?m)
(:sort-by ?q <(mysql_agentDB_jdbc:mysql://localhost:3306_agent ?p/aname ?q/charge))
((!set-agent ?p ?q)(book-via-agent ?x ?y ?z ?m)))
(:operator (!set-agent ?a ?b)
((avail-cash ?x))
((avail-cash ?x))
((booked-through ?a)(avail-cash (call - ?x ?b))))
(:method(book-via-agent ?x ?y ?z ?m)
((transport-mode-mod ?m ?b ?c))
((book-transport-flight ?x ?y ?z)))
(:method (book-transport-flight ?x ?y ?z)
((mysql_flightDB_jdbc:mysql://localhost:3306_flight ?a/mode ?b/fno ?x/source
?y/destination ?e/fare ?f/start ?g/finish ?h/duration ?i/hops)(call